Topologically Protected Transport in Engineered Mechanical Systems
Tirth Shah, Christian Brendel, Vittorio Peano, Florian Marquardt
Reviews of Modern Physics
96
021002
(2024)
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Mechanical vibrations are being harnessed for a variety of purposes and at many length scales, from the macroscopic world down to the nanoscale. The considerable design freedom in mechanical structures allows to engineer new<br>functionalities. In recent years, this has been exploited to generate setups that offer topologically protected transport of vibrational waves, both in the solid state and in fluids. Borrowing concepts from electronic physics and being cross-fertilized by concurrent studies for cold atoms and electromagnetic waves, this field of topological transport in engineered mechanical systems offers a rich variety of phenomena and platforms. In this review, we provide a unifying overview of the various ideas employed in this area, summarize the different approaches and experimental implementations, and comment on the challenges as well as the prospects.
Multiphoton electron emission with non-classical light
Jonas Heimerl, Alexander Mikhaylov, Stefan Meier, Henrick Höllerer, Ido Kaminer, Maria Chekhova, Peter Hommelhoff
Photon number distributions of classical and non-classical light sources have been studied extensively, yet their impact on photoemission processes is largely unexplored. In this article, we present measurements of electron number distributions from metal needle tips illuminated with ultrashort light pulses with various photon quantum statistics. By varying the photon statistics of the exciting light field between classical (Poissonian) and quantum (super-Poissonian), we demonstrate that the measured electron distributions are changed substantially. Using single-mode bright squeezed vacuum light, we measure extreme statistics events with up to 65 electrons from one light pulse at a mean of 0.27 electrons per pulse—the likelihood for such an event equals 10−128 with Poissonian statistics. By changing the number of modes of the exciting bright squeezed vacuum, we can tailor the electron number distribution on demand. Most importantly, our results demonstrate that the photon statistics is imprinted from the driving light to the emitted electrons, opening the door to new sensor devices and to strong-field optics with quantum light and electrons.
Quantitative analysis of the intensity distribution of optical rogue waves
Éva Rácz, Kirill Spasibko, Mathieu Manceau, László Ruppert, Maria Chekhova, Radim Filip
Communications Physics (7)
119
(2024)
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The field of optical rogue waves is a rapidly expanding topic with a focus on explaining their emergence. To complement this research, instead of providing a microscopic model that generates extreme events, we concentrate on a general quantitative description of the observed behavior. We explore two complementary top-down approaches to estimating the exponent describing the power-law decaying distribution of optical rogue waves observed in supercontinuum generated in a single-mode fiber in the normal-dispersion regime by applying a highly fluctuating pump. The two distinct approaches provide consistent results, outperforming the standard Hill estimator. Further analysis of the distributions reveals the breakdown of power-law behavior due to pump depletion and detector saturation. Either of our methods is adaptable to analyze extreme-intensity events from arbitrary experimental data.
High-throughput viscoelastic characterization of cells in hyperbolic microchannels
Felix Reichel, Ruchi Goswami, Salvatore Girardo, Jochen Guck
Extensive research has demonstrated the potential of cell viscoelastic properties as intrinsic indicators of cell state, functionality, and disease. For this, several microfluidic techniques have been developed to measure cell viscoelasticity with high-throughput. However, current microchannel designs introduce complex stress distributions on cells, leading to inaccuracies in determining the stress-strain relationship and, consequently, the viscoelastic properties. Here, we introduce a novel approach using hyperbolic microchannels that enable precise measurements under a constant extensional stress and offer a straightforward stress-strain relationship, while operating at a measurement rate of up to 100 cells per second. We quantified the stresses acting in the channels using mechanical calibration particles made from polyacrylamide (PAAm) and found that the measurement buffer, a solution of methyl cellulose and phosphate buffered saline, has a constant extensional viscosity of 0.5 Pa s up to 200 s-1. By measuring oil droplets with varying viscosities, we successfully detected changes in the relaxation time of the droplets and our approach could be used to get the interfacial tension and viscosity of liquid-liquid droplet systems from the same measurement. We further applied this methodology to PAAm microgel beads, demonstrating the accurate recovery of Young’s moduli and the near-ideal elastic behavior of the beads. To explore the influence of altered cell viscoelasticity, we treated HL60 human leukemia cells with Latrunculin B and Nocodazole, resulting in clear changes in cell stiffness while relaxation times were only minimally affected. In conclusion, our approach offers a streamlined and time-efficient solution for assessing the viscoelastic properties of large cell populations and other microscale soft particles.
An optofluidic antenna for enhancing the sensitivity of single-emitter measurements
Luis Morales-Inostroza, Julian Folz, Ralf Kühnemuth, Suren Felekyan, Franz Wieser, Claus A.M. Seidel, Stephan Götzinger, Vahid Sandoghdar
Many single-molecule investigations are performed in fluidic environments, e.g., to avoid unwanted consequences of contact with surfaces. Diffusion of molecules in this arrangement limits the observation time and the number of collected photons, thus, compromising studies of processes with fast or slow dynamics. Here, we introduce a planar optofluidic antenna (OFA), which enhances the fluorescence signal from molecules by about 5 times per passage, leads to about 7-fold more frequent returns to the observation volume, and significantly lengthens the diffusion time within one passage. We use single-molecule multi-parameter fluorescence detection (sm-MFD), fluorescence correlation spectroscopy (FCS) and Förster resonance energy transfer (FRET) measurements to characterize our OFAs. The antenna advantages are showcased by examining both the slow (ms) and fast (50μs) dynamics of DNA four-way (Holliday) junctions with real-time resolution. The FRET trajectories provide evidence for the absence of an intermediate conformational state and introduce an upper bound for its lifetime. The ease of implementation and compatibility with various microscopy modalities make OFAs broadly applicable to a diverse range of studies.
Exceptional points of any order in a generalized Hatano-Nelson model
Exceptional points (EPs) are truly non-Hermitian (NH) degeneracies where matrices become defective. The order of such an EP is given by the number of coalescing eigenvectors. On the one hand, most work focusses on studying Nth-order EPs in N≤4-dimensional NH Bloch Hamiltonians. On the other hand, some works have remarked on the existence of EPs of orders scaling with systems size in models exhibiting the NH skin effect. In this letter, we introduce a new type of EP and provide a recipe on how to realize EPs of arbitrary order not scaling with system size. We introduce a generalized version of the paradigmatic Hatano-Nelson model with longer-range hoppings. The EPs existing in this system show remarkable physical features: Their associated eigenstates are localized on a subset of sites and are exhibiting the NH skin effect. Furthermore, the EPs are robust against generic perturbations in the hopping strengths as well as against a specific form of on-site disorder.
Quantum interference between distant creation processes
Johannes Pseiner, Manuel Erhard, Mario Krenn
Physical Review Research
6
013294
(2024)
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The search for macroscopic quantum phenomena is a fundamental pursuit in quantum mechanics. It allows us to test the limits of quantum physics and provides new avenues for exploring the interplay between quantum mechanics and relativity. In this work, we introduce a novel approach to generate macroscopic quantum systems by demonstrating that the creation process of a quantum system can span a macroscopic distance. Specifically, we generate photon pairs in a coherent superposition of two origins separated by up to 70 meters. This new approach not only provides an exciting opportunity for foundational experiments in quantum physics, but also has practical applications for high-precision measurements of distributed properties such as pressure and humidity of air or gases.
Essential implications of similarities in non-Hermitian systems
In this paper, we show that three different generalized similarities enclose all unitary and anti-unitary symmetries that induce exceptional points in lower-dimensional non-Hermitian systems. We prove that the generalized similarity conditions result in a larger class of systems than any class defined by a unitary or anti-unitary symmetry. Further we highlight that the similarities enforce spectral symmetry on the Hamiltonian resulting in a reduction of the codimension of exceptional points. As a consequence we show that the similarities drive the emergence of exceptional points in lower dimensions without the more restrictive need for a unitary and/or anti-unitary symmetry.
Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
Remmy Zen, Jan Olle, Luis Colmenarez, Matteo Puviani, Markus Müller, Florian Marquardt
One of the key aspects in the realization of large-scale fault-tolerant quantum computers is quan- tum error correction (QEC). The first essential step of QEC is to encode the logical state into physical qubits in a fault-tolerant manner. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a compact quantum circuit with flag-based protocols for fault-tolerant logical state preparation. It is even more difficult when we consider the hardware constraints, such as qubit connectivity and gate set. In this work, we propose and explore reinforcement learning (RL) to automatically discover compact and hardware-adapted quantum circuits that fault-tolerantly prepare the logical state of a QEC code. We show that RL discovers circuits with fewer gates and ancillary qubits than published results without and with hardware constraints of up to 15 physical qubits. Furthermore, RL allows for straightforward exploration of different qubit connectivities and the use of transfer learning to accelerate the discovery. More generally, our work opens the door towards the use of RL for the discovery of fault-tolerant quantum circuits for addressing tasks beyond state preparation, including magic state preparation, logical gate synthesis, or syndrome measurement.
The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics<br>community, which have revolutionized medical diagnostics and patient care. However, fundamental<br>medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and<br>genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the<br>human body that are based on physical principles and yet govern our health over a vast range of length and<br>time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how,<br>and mindset of the physics community and that the pursuit of basic research in medicine is compatible with<br>the mission of physics.
invited essay
Single-Cell Mechanics: Structural Determinants and Functional Relevance
The mechanical phenotype of a cell determines its ability to deform under force and is therefore relevant to cellular functions that require changes in cell shape, such as migration or circulation through the microvasculature. On the practical level, the mechanical phenotype can be used as a global readout of the cell's functional state, a marker for disease diagnostics, or an input for tissue modeling. We focus our review on the current knowledge of structural components that contribute to the determination of the cellular mechanical properties and highlight the physiological processes in which the mechanical phenotype of the cells is of critical relevance. The ongoing efforts to understand how to efficiently measure and control the mechanical properties of cells will define the progress in the field and drive mechanical phenotyping toward clinical applications.
Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn
Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.
Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
Tareq Jaouni, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, Mario Krenn
Machine Learning: Science and Technology (5)
015029
(2024)
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Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this techniques to explore what neural networks learn about quantum optics experiments. Our story begins by training a deep neural networks on the properties of quantum systems. Once trained, we "invert" the neural network – effectively asking how it imagines a quantum system with a specific property, and how it would continuously modify the quantum system to change a property. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network. Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement. This is in reminiscence of long-understood properties known in computer vision, which we now identify in a complex natural science task. Our approach could be useful in a more interpretable way to develop new advanced AI-based scientific discovery techniques in quantum physics.
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one’s own field. While there are ways to predict a scientific paper’s future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy, and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.
Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation
Qingshan Wang, Clara C. Wanjura, Florian Marquardt
Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only infer- ence but also training can exploit the physical dynamics. In this work, we show that it is possible to successfully train a system of coupled phase oscillators—one of the most widely investigated nonlinear dynamical systems with a multitude of physical implementations, comprising laser arrays, coupled mechanical limit cycles, super- fluids, and exciton-polaritons. To this end, we apply the approach of equilibrium propagation, which permits to extract training gradients via a physical realization of backpropagation, based only on local interactions. The complex energy landscape of the XY/ Kuramoto model leads to multistability, and we show how to address this challenge. Our study identifies coupled phase oscillators as a new general-purpose neuromorphic platform and opens the door towards future experimental implementations.
High-resolution Cryogenic Spectroscopy of Single Molecules in Nanoprinted Crystals
Mohammad Musavinezhad, Jan Renger, Johannes Zirkelbach, Tobias Utikal, Claudio U. Hail, Thomas Basché, Dimos Poulikakos, Stephan Götzinger, Vahid Sandoghdar
We perform laser spectroscopy at liquid helium temperatures (T=2 K) to investigate single dibenzoterrylene<br>(DBT) molecules doped in anthracene crystals of nanoscopic height fabricated by electrohydrodynamic dripping.<br>Using high-resolution fluorescence excitation spectroscopy, we show that zero-phonon lines of single molecules<br>in printed nanocrystals are nearly as narrow as the Fourier-limited transitions observed for the same guest-host<br>system in the bulk. Moreover, the spectral instabilities are comparable to or less than one linewidth. By<br>recording super-resolution images of DBT molecules and varying the polarization of the excitation beam, we<br>determine the dimensions of the printed crystals and the orientation of the crystals’ axes. Electrohydrodynamic<br>printing of organic nano and microcrystals paves the way for a series of applications, where controlled positioning<br>of quantum emitters with narrow optical transitions is desirable.
A paintbrush for delivery of nanoparticles and molecules to live cells with precise spatiotemporal control
Cornelia Holler, Richard W. Taylor, Alexandra Schambony, Leonhard Möckl, Vahid Sandoghdar
Delivery of very small amounts of reagents to the near-field of cells with micrometer spatial precision and millisecond time resolution is currently out of reach. Here we present μkiss as a micropipette-based scheme for brushing a layer of small molecules and nanoparticles onto the live cell membrane from a subfemtoliter confined volume of a perfusion flow. We characterize our system through both experiments and modeling, and find excellent agreement. We demonstrate several applications that benefit from a controlled brush delivery, such as a direct means to quantify local and long-range membrane mobility and organization as well as dynamical probing of intercellular force signaling.
Theory of symmetry-resolved quench-drive spectroscopy: Nonlinear response of phase-fluctuating superconductors
Recent experiments on cuprates have shown the possibility of opening a gap above the super- conducting critical temperature, in the so-called phase-fluctuating state, by enhancing the phase coherence of preformed Cooper pairs. Quench-drive spectroscopy, an implementation of 2D coher- ent spectroscopy, has emerged as a powerful tool for investigating out-of-equilibrium superconductors and their collective modes. In this work, we enrich the quench-drive scheme by developing a sys- tematic generalization to study the nonlinear response of d-wave fully incoherent Cooper pairs in a symmetry resolved manner. In particular, we do not only show that it is possible to obtain a third harmonic signal from fully incoherent pairs with an equilibrium vanishing order parameter, but we also characterize the full flourishing 2D spectrum of the generated nonlinear response. The results provide a deeper theoretical insight on recent experimental results, opening the door to a new symmetry-driven design of future experiments on unconventional and enhanced superconductors.
p21 Prevents the Exhaustion of CD4+ T Cells Within the Antitumor Immune Response Against Colorectal Cancer
Oana-Maria Thoma, Elisabeth Naschberger, Markéta Kubánková, Imen Larafa, Viktoria Kramer, Bianca Menchicchi, Susanne Merkel, Nathalie Britzen-Laurent, André Jefremow, et al.
Gastroenterology
166(2)
284-297
(2024)
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BACKGROUND & AIMS: T cells are crucial for the antitumor response against colorectal cancer (CRC). T-cell reactivity to CRC is nevertheless limited by T-cell exhaustion. However, molecular mechanisms regulating T-cell exhaustion are only poorly understood. METHODS: We investigated the functional role of cyclin-dependent kinase 1a (Cdkn1a or p21) in cluster of differentiation (CD) 4+ T cells using murine CRC models. Furthermore, we evaluated the expression of p21 in patients with stage I to IV CRC. In vitro coculture models were used to understand the effector function of p21-deficient CD4+ T cells. RESULTS: We observed that the activation of cell cycle regulator p21 is crucial for CD4+ T-cell cytotoxic function and that p21 deficiency in type 1 helper T cells (Th1) leads to increased tumor growth in murine CRC. Similarly, low p21 expression in CD4+ T cells infiltrated into tumors of CRC patients is associated with reduced cancer-related survival. In mouse models of CRC, p21-deficient Th1 cells show signs of exhaustion, where an accumulation of effector/effector memory T cells and CD27/CD28 loss are predominant. Immune reconstitution of tumor-bearing Rag1−/− mice using ex vivo-treated p21-deficient T cells with palbociclib, an inhibitor of cyclin-dependent kinase 4/6, restored cytotoxic function and prevented exhaustion of p21-deficient CD4+ T cells as a possible concept for future immunotherapy of human disease. CONCLUSIONS: Our data reveal the importance of p21 in controlling the cell cycle and preventing exhaustion of Th1 cells. Furthermore, we unveil the therapeutic potential of cyclin-dependent kinase inhibitors such as palbociclib to reduce T-cell exhaustion for future treatment of patients with colorectal cancer.
The field of Brillouin microscopy and imaging was established approximately 20 years ago, thanks to the development of non-scanning high-resolution optical spectrometers. Since then, the field has experienced rapid expansion, incorporating technologies from telecommunications, astrophotonics, multiplexed microscopy, quantum optics and machine learning. Consequently, these advancements have led to much-needed improvements in imaging speed, spectral resolution and sensitivity. The progress in Brillouin microscopy is driven by a strong demand for label-free and contact-free methods to characterize the mechanical properties of biomaterials at the cellular and subcellular scales. Understanding the local biomechanics of cells and tissues has become crucial in predicting cellular fate and tissue pathogenesis. This Primer aims to provide a comprehensive overview of the methods and applications of Brillouin microscopy. It includes key demonstrations of Brillouin microscopy and imaging that can serve as a reference for the existing research community and new adopters of this technology. The article concludes with an outlook, presenting the authors’ vision for future developments in this vibrant field. The Primer also highlights specific examples where Brillouin microscopy can have a transformative impact on biology and biomedicine.
Beyond comparison: Brillouin microscopy and AFM-based indentation reveal divergent insights into the mechanical profile of the murine retina
Stephanie Möllmert, Marcus Gutmann, Paul Müller, Kyoohyun Kim, Jana Bachir Salvador, Serhii Aif, Lorenz Meinel, Jochen Guck
Mechanical tissue properties increasingly serve as pivotal phenotypic characteristics that are subject to change during development or pathological progression. The quantification of such material properties often relies on physical contact between a load-applying probe and an exposed sample surface. For most tissues, these requirements necessitate animal sacrifice, tissue dissection and sectioning. These invasive procedures bear the risk of yielding mechanical properties that do not portray the physiological mechanical state of a tissue within a functioning organism. Brillouin microscopy has emerged as a non-<br>invasive, optical technique that allows to assess mechanical cell and tissue properties with high spatio-temporal resolution. In optically transparent specimens, this technique does not require animal sacrifice, tissue dissection or sectioning. However, the extent to which results obtained from Brillouin microscopy allow to infer conclusions about potential results obtained with a contact-based technique, and vice versa, is unclear. Potential sources for discrepancies include the varying characteristic temporal and spatial scales, the directionality of measurement, environmental factors, and mechanical moduli probed. In this<br>work, we addressed those aspects by quantifying the mechanical properties of acutely dissected murine retinal tissues using<br>Brillouin microscopy and atomic force microscopy (AFM)-based indentation measurements. Our results show a distinct<br>mechanical profile of the retinal layers with respect to the Brillouin frequency shift, the Brillouin linewidth and the apparent<br>Young’s modulus. Contrary to previous reports, our findings do not support a simple correlative relationship between Brillouin frequency shift and apparent Young’s modulus. Additionally, the divergent sensitivity of Brillouin microscopy and AFM-indentation measurements to cross-linking or changes post mortem underscores the dangers of assuming both methods can be<br>generally used interchangeably. In conclusion, our study advocates for viewing Brillouin microscopy and AFM-based indentation measurements as complementary tools, discouraging direct comparisons a priori and suggesting their combined use for a more comprehensive understanding of tissue mechanical properties.
Real-time imaging of standing-wave patterns in microresonators
Haochen Yan, Alekhya Ghosh, Arghadeep Pal, Hao Zhang, Toby Bi, George N. Ghalanos, Shuangyou Zhang, Lewis Hill, Yaojing Zhang, et al.
https://doi.org/10.1073/pnas.2313981121
(2024)
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Real-time characterization of microresonator dynamics is important for many applications. In<br>particular it is critical for near-field sensing and understanding light-matter interactions. Here,<br>we report camera-facilitated imaging and analysis of standing wave patterns in optical ring<br>resonators. The standing wave pattern is generated through bi-directional pumping of a<br>microresonator and the scattered light from the microresonator is collected by a short-wave<br>infrared (SWIR) camera. The recorded scattering patterns are wavelength dependent, and the<br>scattered intensity exhibits a linear relation with the circulating power within the microresonator.<br>By modulating the relative phase between the two pump waves, we can control the generated<br>standing waves’ movements and characterize the resonator with the SWIR camera. The<br>visualized standing wave enables subwavelength distance measurements of scattering targets<br>with nanometer-level accuracy. This work opens new avenues for applications in on-chip nearfield<br>(bio-)sensing, real time characterization of photonic integrated circuits and backscattering<br>control in telecom systems.
AI-driven projection tomography with multicore fibre-optic cell rotation
Jiawei Sun, Bin Yang, Nektarios Koukourakis, Jochen Guck, Juergen W. Czarske
Nature Communications
15
147
(2024)
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Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.
Long-range three-dimensional tracking of nanoparticles using interferometric scattering (iSCAT) microscopy
Tracking nanoparticle movement is highly desirable in many scientific areas, and various imaging<br>methods have been employed to achieve this goal. Interferometric scattering (iSCAT) microscopy has<br>been particularly successful in combining very high spatial and temporal resolution for tracking small<br>nanoparticles in all three dimensions. However, previous works have been limited to an axial range<br>of only a few hundred nanometers. Here, we present a robust and efficient strategy for localizing<br>nanoparticles recorded in high-speed iSCAT videos in three dimensions over tens of micrometers. We<br>showcase the performance of our algorithm by tracking gold nanoparticles as small as 10 nm diffusing<br>in water while maintaining 5 μs temporal resolution and nanometer axial localization precision. Our<br>results hold promise for applications in cell biology and material science, where the three-dimensional<br>motion of nanoparticles in complex media is of interest
Optoacoustic Cooling of Traveling Hypersound Waves
Laura Blázquez Martínez, Philipp Wiedemann, Changlong Zhu, Andreas Geilen, Birgit Stiller
Physical Review Letters
132
023603
(2024)
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We experimentally demonstrate optoacoustic cooling via stimulated Brillouin-Mandelstam scattering in a 50 cm long tapered photonic crystal fiber. For a 7.38 GHz acoustic mode, a cooling rate of 219 K from room temperature has been achieved. As anti-Stokes and Stokes Brillouin processes naturally break the symmetry of phonon cooling and heating, resolved sideband schemes are not necessary. The experiments pave the way to explore the classical to quantum transition for macroscopic objects and could enable new quantum technologies in terms of storage and repeater schemes.
Symmetry-induced higher-order exceptional points in two dimensions
Exceptional points of order n (EPns) appear in non-Hermitian systems as points where the eigen- values and eigenvectors coalesce. Whereas EP2s generically appear in two dimensions (2D), higher- order EPs require a higher-dimensional parameter space to emerge. In this work, we provide a complete characterization the appearance of symmetry-induced higher-order EPs in 2D parameter space. We find that besides EP2s only EP3s, EP4s, and EP5s can be stabilized in 2D. Moreover, these higher-order EPs must always appear in pairs with their dispersion determined by the sym- metries. Upon studying the complex spectral structure around these EPs, we find that depending on the symmetry, EP3s are accompanied by EP2 arcs, and 2- and 3-level open Fermi structures. Similarly, EP4s and closely related EP5s, which arise due to multiple symmetries, are accompanied by exotic EP arcs and open Fermi structures. For each case, we provide an explicit example. We also comment on the topological charge of these EPs, and discuss similarities and differences between symmetry-protected higher-order EPs and EP2s.
Measuring concentration of nanoparticles in polydisperse mixtures using interferometric nanoparticle tracking analysis (iNTA)
Anna Kashkanova, David Albrecht, Michelle Kueppers, Martin Blessing, Vahid Sandoghdar
Quantitative measurements of nanoparticle concentration in liquid suspensions are in high demand, for example, in the medical and food industries. Conventional methods remain unsatisfactory, especially for polydisperse samples with overlapping size ranges. Recently, we introduced interferometric nanoparticle tracking analysis (iNTA) as a new method for high-precision measurement of nanoparticle size and refractive index. Here, we show that by counting the number of trajectories that cross the focal plane, iNTA can measure concentrations of subpopulations in a polydisperse mixture in a quantitative manner and without the need for a calibration sample. We evaluate our method on both monodisperse samples and mixtures of known concentrations. Furthermore, we assess the concentration of SARS-CoV-2 in supernatant samples obtained from infected cells.
2023
Low-Temperature Sputtered Ultralow-Loss Silicon Nitride for Hybrid Photonic Integration
Shuangyou Zhang, Toby Bi, Irina Harder, Olga Ohletz, Florentina Gannott, Alexander Gumann, Eduard Butzen, Yaojing Zhang, Pascal Del'Haye
Silicon-nitride-on-insulator (Si3N4) photonic circuits have seen tremendous advances in many applications, such as on-chip frequency combs, Lidar, telecommunications, and spectroscopy. So far, the best film quality has been achieved with low pressure chemical vapor deposition (LPCVD) and high-temperature annealing (1200°C). However, high processing temperatures pose challenges to the cointegration of Si3N4 with pre-processed silicon electronic and photonic devices, lithium niobate on insulator (LNOI), and Ge-on-Si photodiodes. This limits LPCVD as a front-end-of-line process. Here, ultralow-loss Si3N4 photonics based on room-temperature reactive sputtering is demonstrated. Propagation losses as low as 5.4 dB m−1 after 400°C annealing and 3.5 dB m−1 after 800°C annealing are achieved, enabling ring resonators with highest optical quality factors of > 10 million and an average quality factor of 7.5 million. To the best of the knowledge, these are the lowest propagation losses achieved with low temperature Si3N4. This ultralow loss enables the generation of microresonator soliton frequency combs with threshold powers of 1.1 mW. The introduced sputtering process offers full complementary metal oxide semiconductor (CMOS) compatibility with front-end silicon electronics and photonics. This could enable hybrid 3D integration of low loss waveguides with integrated lasers and lithium niobate on insulator.
Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters. For each sensing platform, pinpointing the optimal controls to enhance the sensor’s precision remains a challenging task. While an analytical solution might be out of reach, machine learning offers a promising avenue for many systems of interest, especially given the capabilities of contemporary hardware. We have introduced a versatile procedure capable of optimizing a wide range of problems in quantum metrology, estimation, and hypothesis testing by combining model-aware reinforcement learning (RL) with Bayesian estimation based on particle filtering. To achieve this, we had to address the challenge of incorporating the many non-differentiable steps of the estimation in the training process, such as measurements and the resampling of the particle filter. Model-aware RL is a gradient-based method, where the derivatives of the sensor’s precision are obtained through automatic differentiation (AD) in the simulation of the experiment. Our approach is suitable for optimizing both non-adaptive and adaptive strategies, using neural networks or other agents. We provide an implementation of this technique in the form of a Python library called qsensoropt, alongside several pre-made applications for relevant physical platforms, namely NV centers, photonic circuits, and optical cavities. This library will be released soon on PyPI. Leveraging our method, we’ve achieved results for many examples that surpass the current state-of-the-art in experimental design. In addition to Bayesian estimation, leveraging model-aware RL, it is also possible to find optimal controls for the minimization of the Cram ́er-Rao bound, based on Fisher information.
Microresonator soliton frequency combs via cascaded Brillouin scattering
Hao Zhang, Shuangyou Zhang, Toby Bi, George N. Ghalanos, Yaojing Zhang, Haochen Yan, Arghadeep Pal, Jijun He, Shilong Pan, et al.
https://doi.org/10.48550/arXiv.2312.15506
(2023)
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We demonstrate Kerr soliton frequency comb generation that is seeded by a cascaded Brillouin scattering process. In this process, a pump laser is used to generate multiple orders of Brillouin sidebands in a microresonator, which in turn generate the soliton. In such a process, even orders of Brillouin scattering sidebands are co-propagating with respect to the pump laser while odd orders of Brillouin scattering are backwards propagating. In this work we present the generation of forward propagating Kerr solitons via a forward propagating second order Brillouin scattering process in a fused silica rod resonator. Importantly, we show that the Brillouin scattering process can bridge the gap between different microresonator mode families, such that the repetition rate of the Kerr soliton is independent from the Brillouin gain frequency shift (about 10 GHz in fused silica). In our work we demonstrate this by generating soliton pulse trains with a repetition rate of 107 GHz. Our work opens up a new way for using cascaded Brillouin lasing as a seed for microresonator frequency comb generation. This can be of particular interest for the realization of soliton frequency combs with low noise properties from Brillouin lasing while still having arbitrary repetition rates that are determined by the resonator size. Applications range from optical communication to LIDAR systems and photonic signal generation.
Broadband Spectroscopy and Interferometry with Undetected Photons at Strong Parametric Amplification
Kazuki Hashimoto, Dmitri B. Horoshko, Maria Chekhova
Advanced Quantum Technologies
2300299
(2023)
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Nonlinear interferometry with entangled photons allows for characterizing a sample without detecting the photons interacting with it. This method enables highly sensitive optical sensing in the wavelength regions where efficient detectors are still under development. Recently, nonlinear interferometry has been applied to interferometric measurement techniques with broadband light sources, such as Fourier-transform infrared spectroscopy and infrared optical coherence tomography. However, they have been demonstrated with photon pairs produced through spontaneous parametric down-conversion (SPDC) at a low parametric gain, where the average number of photons per mode is much smaller than one. The regime of high-gain SPDC offers several important advantages, such as the amplification of light after its interaction with the sample and a large number of photons per mode at the interferometer output. This work presents broadband spectroscopy and high-resolution optical coherence tomography with undetected photons generated via high-gain SPDC in an aperiodically poled lithium niobate crystal. To prove the principle, reflective Fourier-transform near-infrared spectroscopy with a spectral bandwidth of 17 THz and optical coherence tomography with an axial resolution of 11 µm are demonstrated.<br>
Temporally Distilled High-Dimensional Biphotonic States from Thin Sources
Generation of entangled photons through spontaneous parametric down-conversion (SPDC) from micro- and nanoscale sources offers unprecedented freedom in quantum state engineering, including the ability to generate two-photon states with high-dimensional hyperentanglement. However, as the source of SPDC gets smaller, the role of photoluminescence increases, which leads to the contamination of two-photon states with a thermal background. Here we propose and implement a solution to this problem: by using pulsed SPDC and time distillation, we increase the purity and the heralding efficiency of the photon pairs. In the experiment, we increased the purity of the two-photon states generated in a 7 μm film of lithium niobate from 0.002 to 0.99. With the higher purity we were able to observe and characterize different polarization states of photon pairs generated simultaneously due to relaxed phase matching. In particular, we showed the presence of orthogonally polarized photons that are potentially usable for the generation of polarization entanglement.
Spectral splitting of a stimulated Raman transition in a single molecule
Johannes Zirkelbach, Burak Gürlek, Masoud Mirzaei, Alexey Shkarin, Tobias Utikal, Stephan Götzinger, Vahid Sandoghdar
Physical Review Research
5
043244
(2023)
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The small cross-section of Raman scattering poses a great challenge for its direct study at the single-molecule level. By exploiting the high Franck-Condon factor of a common-mode resonance, choosing a large vibrational frequency difference in electronic ground and excited states and operating at T<2K, we succeed at driving a coherent stimulated Raman transition in individual molecules. We observe and model a spectral splitting that serves as a characteristic signature of the phenomenon at hand. Our study sets the ground for exploiting the intrinsic optomechanical degrees of freedom of molecules for applications in solid-state quantum optics and information processing.
Catch and release of propagating bosonic field with non-Markovian giant atom
The non-Markovianity of physical systems is considered to be a valuable resource that has potential applications to quantum information processing. The control of traveling quantum fields encoded with information (flying qubit) is crucial for quantum networks. In this work, we propose to catch and release the propagating photon/phonon with a non-Markovian giant atom, which is coupled to the environment via multiple coupling points. Based on the Heisenberg equation of motion for the giant atom and field operators, we calculate the time- dependent scattering coefficients from the linear response theory and define the criteria for the non-Markovian giant atom. We analyze and numerically verify that the field bound states due to non-Markovianity can be harnessed to catch and release the propagating bosonic field on demand by tuning the parameters of giant atom.
Experimental Solutions to the High-Dimensional Mean King's Problem
Tareq Jaouni, Xiaoqin Gao, Sören Arlt, Mario Krenn, Ebrahim Karimi
In 1987, Vaidman, Aharanov, and Albert put forward a puzzle called the Mean<br>King's Problem (MKP) that can be solved only by harnessing quantum<br>entanglement. Prime-powered solutions to the problem have been shown to exist,<br>but they have not yet been experimentally realized for any dimension beyond<br>two. We propose a general first-of-its-kind experimental scheme for solving the<br>MKP in prime dimensions ($D$). Our search is guided by the digital discovery<br>framework PyTheus, which finds highly interpretable graph-based representations<br>of quantum optical experimental setups; using it, we find specific solutions<br>and generalize to higher dimensions through human insight. As proof of<br>principle, we present a detailed investigation of our solution for the three-,<br>five-, and seven-dimensional cases. We obtain maximum success probabilities of<br>$72.8 \%$, $45.8\%$, and $34.8 \%$, respectively. We, therefore, posit that our<br>computer-inspired scheme yields solutions that exceed the classical probability<br>($1/D$) twofold, demonstrating its promise for experimental implementation.<br>
Boosting the Gottesman-Kitaev-Preskill quantum error correction with non-Markovian feedback
Matteo Puviani, Sangkha Borah, Remmy Zen, Jan Olle, Florian Marquardt
Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of a harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctable well beyond the break-even point of the best passive encoding in the same system. Current approaches to quantum error correction (QEC) for this system are based on protocols that use feedback, but the response is based only on the latest measurement outcome. In our work, we use the recently proposed Feedback-GRAPE (Gra- dient Ascent Pulse Engineering with Feedback) method to train a recurrent neural network that provides a QEC scheme based on memory, responding in a non-Markovian way to the full history of previous measurement outcomes, optimizing all subsequent unitary operations. This approach sig- nificantly outperforms current strategies and paves the way for more powerful measurement-based QEC protocols.
Digital Discovery of 100 diverse Quantum Experiments with PyTheus
Carlos Ruiz-Gonzalez, Sören Arlt, Jan Petermann, Sharareh Sayyad, Tareq Jaouni, Ebrahim Karimi, Nora Tischler, Xuemei Gu, Mario Krenn
Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries -- one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology.
Coherent pair injection as a route towards the enhancement of supersolid order in many-body bosonic models
Emmanouil Grigoriou, Zhiyao Ning, Hang Su, Benjamin Löckler, Ming Li, Yoshitomo Kamiya, Carlos Navarrete-Benlloch
Over the last couple of decades, quantum simulators have been probing quantum many-body physics with un- precedented levels of control. So far, the main focus has been on the access to novel observables and dynamical conditions related to condensed-matter models. However, the potential of quantum simulators goes beyond the traditional scope of condensed-matter physics: Being based on driven-dissipative quantum optical platforms, quantum simulators allow for processes that are typically not considered in condensed-matter physics. These processes can enrich in unexplored ways the phase diagram of well-established models. Taking the extended Bose-Hubbard model as the guiding example, in this work we examine the impact of coherent pair injection, a process readily available in, for example, superconducting circuit arrays. The interest behind this process is that, in contrast to the standard injection of single excitations, it can be configured to preserve the U(1) symmetry underlying the model. We prove that this process favors both superfluid and density-wave order, as opposed to insulation or homogeneous states, thereby providing a novel route towards the access of lattice supersolidity.
Digital Discovery of interferometric Gravitational Wave Detectors
Gravitational waves, detected a century after they were first theorized, are spacetime distortions caused by some of the most cataclysmic events in the universe, including black hole mergers and supernovae. The successful detection of these waves has been made possible by ingenious detectors designed by human experts. Beyond these successful designs, the vast space of experimental config- urations remains largely unexplored, offering an exciting territory potentially rich in innovative and unconventional detection strategies. Here, we demonstrate the application of artificial intelligence (AI) to systematically explore this enormous space, revealing novel topologies for gravitational wave (GW) detectors that outperform current next-generation designs under realistic experimental con- straints. Our results span a broad range of astrophysical targets, such as black hole and neutron star mergers, supernovae, and primordial GW sources. Moreover, we are able to conceptualize the initially unorthodox discovered designs, emphasizing the potential of using AI algorithms not only in discovering but also in understanding these novel topologies. We’ve assembled more than 50 superior solutions in a publicly available Gravitational Wave Detector Zoo which could lead to many new surprising techniques. At a bigger picture, our approach is not limited to gravitational wave detectors and can be extended to AI-driven design of experiments across diverse domains of fundamental physics.
Estimation of the mass density of biological matter from refractive index
measurements
Conrad Möckel, Timon Beck, Sara Kaliman, Shada Abuhattum, Kyoohyun Kim, Julia Kolb, Daniel Wehner, Vasily Zaburdaev, Jochen Guck
The quantification of physical properties of biological matter gives rise to novel ways of understanding functional mechanisms by utilizing models that explicitly depend on physical observables. One of the basic biophysical properties is the mass density (MD), which determines the degree of<br>crowdedness. It impacts the dynamics in subcellular compartments and further plays a major role in defining the opto-acoustical properties of cells and tissues. As such, the MD can be connected to the refractive index (RI) via the well known Lorentz-Lorenz relation, which takes into account the polarizability of matter. However, computing the MD based on RI measurements poses a challenge as it requires detailed knowledge of the biochemical composition of the sample. Here we propose<br>a methodology on how to account for a priori and a posteriori assumptions about the biochemical composition of the sample as well as respective RI measurements. To that aim, we employ the Biot mixing rule of RIs alongside the assumption of volume additivity to find an approximate relation of MD and RI. We use Monte-Carlo simulations as well as Gaussian propagation of uncertainty to obtain approximate analytical solutions for the respective uncertainties of MD and RI. We validate this approach by applying it to a set of well characterized complex mixtures given by bovine milk and intralipid emulsion. Further, we employ it to estimate the mass density of trunk tissue of living zebrafish (Danio rerio) larvae. Our results enable quantifying changes of mass density estimates based on variations in the a priori assumptions. This illustrates the importance of implementing this methodology not only for MD estimations but for many other related biophysical problems, such as<br>mechanical measurements using Brillouin microscopy and transient optical coherence elastography.
Membrane to cortex attachment determines different mechanical phenotypes in LGR5+ and LGR5- colorectal cancer cells
Sefora Conti, Valeria Venturini, Adrià Cañellas-Socias, Carmen Cortina, Juan F. Abenza, Camille Stephan-Otto Attolini, Emily Middendorp Guerra, Catherine Xu, Jia Hui Li, et al.
Colorectal cancer tumors are composed of heterogeneous and plastic cell populations, including a pool of cancer stem cells that express LGR5. Whether these distinct cell populations display different mechanical properties, and how these properties might contribute to metastasis is unknown. Using CRC patient derived organoids (PDOs), we found that compared to LGR5- cells,<br>LGR5+ cancer stem cells are stiffer, adhere better to the extracellular matrix (ECM), move slower both as single cells and clusters, display higher nuclear YAP, show a higher survival rate in response to mechanical confinement, and form larger transendothelial gaps. These differences are largely explained by the downregulation of the membrane to cortex attachment proteins Ezrin/Radixin/Moesin (ERMs) in the LGR5+ cells. By analyzing scRNA-seq expression patterns from a patient cohort, we show that this downregulation is a robust signature of colorectal tumors. Our results show that LGR5- cells display a mechanically dynamic phenotype suitable for<br>dissemination from the primary tumor whereas LGR5+ cells display a mechanically stable and<br>resilient phenotype suitable for extravasation and metastatic growth.
Topological Properties of a Non-Hermitian Quasi-1D Chainwith a Flat Band
C. Martínez-Strasser, M. A. J. Herrera, G. Palumbo, Flore K. Kunst, D. Bercioux
Advanced Quantum Technologies
(2023)
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We investigate the spectral properties of a non-Hermitian quasi-one-dimensional lattice in two possible dimerization configurations.<br>Specifically, we focus on a non-Hermitian diamond chain that presents a zero-energy flat band. The flat band originates from wave interference and results in eigenstates with a finite contribution only on two sites of the unit<br>cell. To achieve the non-Hermitian characteristics, we introduce non-reciprocal<br>intrasite hopping terms in the chain. This leads to the accumulation of eigenstates on the boundary of the system, known as the non-Hermitian skin effect. Despite this accumulation of eigenstates, for one of the two possible<br>configurations, we can characterize the presence of non-trivial edge states at zero energy by a real-space topological invariant known as the biorthogonal polarization. We show that this invariant, evaluated using the destructive interference method, characterizes the non-trivial phase of the non-Hermitian<br>diamond chain. For the other possible non-Hermitian configuration, we find that there is a finite quantum metric associated with the flat band. Additionally, we observe the skin effect despite having the system a purely real or imaginary spectrum. For both configurations, we show that two non- Hermitian diamond<br>chains can be mapped into two models of the Su-Schrieffer-Heeger chains, either non-Hermitian and Hermitian, in the presence of a flat band. This mapping allows us to draw valuable insights into the behavior and properties of these systems.
Optimizing ZX-Diagrams with Deep Reinforcement Learning
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum process they describe. These rules can be exploited to optimize the structure of ZX-diagrams for a range of applications. However, finding an optimal sequence of transformation rules is generally an open problem. In this work, we bring together ZX-diagrams with reinforcement learning, a machine learning technique designed to discover an optimal sequence of actions in a decision-making problem and show that a trained reinforcement learning agent can significantly outperform other optimization techniques like a greedy strategy or simulated annealing. The use of graph neural networks to encode the policy of the agent enables generalization to diagrams much bigger than seen during the training phase.
On-chip interference of scattering from two individual molecules
Dominik Rattenbacher, Alexey Shkarin, Jan Renger, Tobias Utikal, Stephan Götzinger, Vahid Sandoghdar
Integrated photonic circuits offer a promising route for studying coherent cooperative effects of a controlled collection<br>of quantum emitters. However, spectral inhomogeneities, decoherence, and material incompatibilities in the solid<br>state make this a nontrivial task. Here, we demonstrate efficient coupling of a pair of Fourier-limited organic molecules<br>embedded in a polyethylene film to a TiO2 microdisc resonator on a glass chip. Moreover, we tune the resonance frequen-<br>cies of the emitters with respect to that of the microresonator by employing nanofabricated electrodes. For two molecules<br>separated by a distance of about 8 μm and an optical phase difference of about π/2, we report on a large collective<br>extinction of the incident light in the forward direction and the destructive interference of its scattering in the backward<br>direction. Our work sets the ground for coherent coupling of several quantum emitters via a common mode and realiza-<br>tion of polymer-based hybrid quantum photonic circuits.
Geometry optimization for dark soliton combs in
thin multimode silicon nitride microresonators
Optics Express
31(25)
41420-41427
(2023)
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Silicon nitride (Si3N4) has been well established as an ultralow-loss material for integrated photonics, particularly for the generation of dissipative Kerr soliton frequency combs, enabling various applications for optical metrology, biological imaging, and coherent telecommunications. Typically, bright soliton generation in Si3N4 devices requires thick (>600 nm) films to fulfill the condition of anomalous dispersion at telecom wavelengths. However, thick films of ultralow-loss Si3N4 (>400 nm) often suffer from high internal stress, leading to cracks. As an alternative approach, thin Si3N4 films (<400 nm) provide the advantage of one-step deposition and are widely applied for commercial use. Here, we provide insights into engineering an integrated Si3N4 structure that achieves optimal effective nonlinearity and maintains a compact footprint. A comparative analysis of Si3N4 resonators with varying waveguide thicknesses is conducted and reveals that a 400-nm thin Si3N4 film emerges as a promising solution that strikes a balance among the aforementioned criteria. Based on a commercially available 400-nm Si3N4 film, we experimentally demonstrate the generation of low-noise coherent dark pulses with a repetition rate of 25 GHz in a multimode Si3N4 resonator. The compact spiral-shaped resonator has a footprint of 0.28 mm2 with a high-quality factor of 4 × 106. Our demonstrated dark combs with mode spacings of tens of GHz have applications in microwave photonics, optical spectroscopy, and telecommunication systems.
Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent
Jan Olle, Remmy Zen, Matteo Puviani, Florian Marquardt
Finding optimal ways to protect quantum states from noise remains an outstanding challenge across all quantum technologies, and quantum error correction (QEC) is the most promising strategy to address this issue. Constructing QEC codes is a complex task that has historically been powered by human creativity with the discovery of a large zoo of families of codes. However, in the context of real-world scenarios there are two challenges: these codes have typically been categorized only for their performance under an idealized noise model and the implementation-specific optimal encoding circuit is not known. In this work, we train a Deep Reinforcement Learning agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity, and error model. We introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Moreover, thanks to the use of the stabilizer formalism and a vectorized Clifford simulator, our RL implementation is extremely efficient, allowing us to produce many codes and their encoders from scratch within seconds, with code distances varying from 3 to 5 and with up to 20 physical qubits. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.
Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
Kevin Reuer, Jonas Landgraf, Thomas Fösel, James O'Sullivan, Liberto Beltrán, Abdulkadir Akin, Graham J. Norris, Ants Remm, Michael Kerschbaum, et al.
Nature Communications
14
7138
(2023)
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To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state<br><br><br>preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use<br><br><br>model-free reinforcement learning that is based solely on measurement data. We study the agent’s performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of<br><br><br>reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control.
Insights into protein structure using cryogenic light microscopy
Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light microscopy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.<br>
Bile Is a Selective Elevator for Mucosal Mechanics and Transport
Simon Hanio, Stephanie Möllmert, Conrad Möckel, Susobhan Choudhury, Andreas I. Höpfel, Theresa Zorn, Sebastian Endres, Jonas Schlauersbach, Lena Scheller, et al.
Mucus mechanically protects the intestinal epithelium and impacts the absorption of drugs, with a largely unknown role for bile. We explored the impacts of bile on mucosal biomechanics and drug transport within mucus. Bile diffused with square-root-of-time kinetics and interplayed with mucus, leading to transient stiffening captured in Brillouin images and a concentration-dependent change from subdiffusive to Brownian-like diffusion kinetics within the mucus demonstrated by differential dynamic microscopy. Bile-interacting drugs, Fluphenazine and Perphenazine, diffused faster through mucus in the presence of bile, while Metoprolol, a drug with no bile interaction, displayed consistent diffusion. Our findings were corroborated by rat studies, where co-dosing of a bile acid sequestrant substantially reduced the bioavailability of Perphenazine but not Metoprolol. We clustered over 50 drugs based on their interactions with bile and mucin. Drugs that interacted with bile also interacted with mucin but not vice versa. This study detailed the dynamics of mucus biomechanics under bile exposure and linked the ability of a drug to interact with bile to its abbility to interact with mucus.
Near-Petahertz Fieldoscopy of Liquid
Anchit Srivastava, Andreas Herbst, Mahdi M. Bidhendi, Max Kieker, Francesco Tani, Hanieh Fattahi
Measuring transient optical field is pivotal not only for understanding ultrafast phenomena but also for quantitative detection of various molecular species in a sample. In this work, we demonstrate near-petahertz electric field detection of a few femtosecond pulses with 2oo attosecond temporal resolution, 10 detection dynamic range in electric field and sub-femtojoule detection sensitivity, exceeding those reported by the current methods. By field-resolved detection of the impulsively excited molecules in the liquid phase, termed 'femtosecond fieldoscopy', we demonstrate temporal isolation of the response of the target molecules from those of the environment and the excitation pulse. In a proof-of-concept analysis of aqueous and liquid samples, we demonstrate field-sensitive detection of combination bands of 4.13 {\mu}mol ethanol for the first time. This method expands the scope of aqueous sample analysis to higher detection sensitivity and dynamic range, while the simultaneous direct measurements of phase and intensity information pave the path towards high-resolution biological spectro-microscopy
Deep Bayesian Experimental Design for Quantum Many-Body Systems
Leopoldo Sarra, Florian Marquardt
Machine Learning: Science and Technology (4)
045022
(2023)
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Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design.
Small leucine-rich proteoglycans inhibit CNS regeneration by modifying the structural and mechanical properties of the lesion environment
Julia Kolb, Vasiliki Tsata, Nora John, Kyoohyun Kim, Conrad Möckel, Gonzalo Rosso, Veronika Kurbel, Asha Parmar, Gargi Sharma, et al.
Nature Communications
14
6814
(2023)
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Extracellular matrix (ECM) deposition after central nervous system (CNS) injury leads to inhibitory scarring in humans and other mammals, whereas it facilitates axon regeneration in the zebrafish. However, the molecular basis of these different fates is not understood. Here, we identify small leucine-rich proteoglycans (SLRPs) as a contributing factor to regeneration failure in mammals. We demonstrate that the SLRPs chondroadherin, fibromodulin, lumican, and prolargin are enriched in rodent and human but not zebrafish CNS lesions. Targeting SLRPs to the zebrafish injury ECM inhibits axon regeneration and functional recovery. Mechanistically, we find that SLRPs confer mechano-structural properties to the lesion environment that are adverse to axon growth. Our study reveals SLRPs as inhibitory ECM factors that impair axon regeneration by modifying tissue mechanics and structure, and identifies their enrichment as a feature of human brain and spinal cord lesions. These findings imply that SLRPs may be targets for therapeutic strategies to promote CNS regeneration.
Reservoir Engineering for Classical Nonlinear Fields
Reservoir engineering has become a prominent tool to control quantum systems. Recently, there have been first experiments applying it to many-body systems, especially with a view to engineer particle-conserving dissipation for quantum simulations using bosons. In this work, we explore the dissipative dynamics of these systems in the classical limit. We derive a general equation of motion capturing the effective nonlinear dissipation introduced by the bath and apply it to the special case of a Bose-Hubbard model, where it leads to an unconventional type of dissipative nonlinear Schr ̈odinger equation. Building on that, we study the dynamics of one and two solitons in such a dissipative classical field theory.
Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network
Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, et al.
Nature Machine Intelligence
10.1038/s42256-023-00735-0
(2023)
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A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could profoundly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over recent years, making it challenging for human researchers to keep track of the progress. Here we use AI techniques to predict the future research directions of AI itself. We introduce a graph-based benchmark based on real-world data—the Science4Cast benchmark, which aims to predict the future state of an evolving semantic network of AI. For that, we use more than 143,000 research papers and build up a knowledge network with more than 64,000 concept nodes. We then present ten diverse methods to tackle this task, ranging from pure statistical to pure learning methods. Surprisingly, the most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach. These results indicate a great potential that can be unleashed for purely ML approaches without human knowledge. Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools.
Experimental Optical Simulator of Reconfigurable and Complex Quantum Environment
P. Renault, J. Nokkala, G. Roeland, Nicolas Y. Joly, R. Zambrini, S. Maniscalco, J. Piilo, N. Treps, V. Parigi
No quantum system can be considered totally isolated from its environment. In most cases the interaction between the system of interest and the external degrees of freedom deeply changes its dynamics, as described by open quantum system theory. Nevertheless engineered environment can be turned into beneficial effects for some quantum information tasks. Here we demonstrate an optical simulator of a quantum system coupled to an arbitrary and reconfigurable environment built as a complex network of quantum interacting systems. We experimentally retrieve typical features of open quantum system dynamics like the spectral density and quantum non-Markovianity, by exploiting squeezing and entanglement correlation of a continuous-variable optical platform. This opens the way to the experimental tests of open quantum systems in reconfigurable environments that are relevant in, among others, quantum information, quantum thermodynamics, quantum transport, and quantum synchronization.
Fast quantum control of cavities using an improved protocol without coherent errors
Jonas Landgraf, Christa Flühmann, Thomas Fösel, Florian Marquardt, Robert J. Schoelkopf
The selective number-dependent arbitrary phase (SNAP) gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock modes of a cavity. However, for short pulses, coherent errors limit the performance. Here we demonstrate in theory and experiment that such errors can be completely suppressed, provided that the pulse times exceed a specific limit. The resulting shorter gate times also reduce incoherent errors. Our approach needs only a small number of frequency components, the resulting pulses can be interpreted easily, and it is compatible with fault-tolerant schemes.
Four-field symmetry breakings in twin-resonator photonic isomers
Alekhya Ghosh, Lewis Hill, Gian-Luca Oppo , Pascal Del'Haye
Physical Review Research
5(4)
L042012
(2023)
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Symmetry and symmetry breaking of light states play an important role in photonic integrated circuits and have recently attracted lots of research interest that is relevant to the manipulation of light polarization, telecommunications, all optical computing, and more. We consider four-field symmetry breaking within two different configurations of photonic dimer systems, both comprised of two identical Kerr ring resonators. In each configuration we observe multiple degrees and levels of spontaneous symmetry breaking between circulating photon numbers and further, a wide range of oscillatory dynamics, such as chaos and multiple variations of periodic switching. These dynamics are of interest for optical data processing, optical memories, telecommunication systems, and integrated photonic sensors.
XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy
Carla Rodríguez Mangues, Sören Arlt, Leonhard Möckl, Mario Krenn
In this work we introduce XLuminA, an original computational framework designed for the discovery of novel optical hardware in super-resolution microscopy. Our framework offers auto-differentiation capabilities, allowing for the fast and efficient simulation and automated design of entirely new optical setups from scratch. We showcase its potential by re-discovering three foundational experiments, each one covering different areas in optics: an optical telescope, STED microscopy and the focusing beyond the diffraction limit of a radially polarized light beam. Intriguingly, for this last experiment, the machine found an alternative solution following the same physical principle exploited for breaking the diffraction limit. With XLuminA, we can go beyond simple optimization and calibration of known experimental setups, opening the door to potentially uncovering new microscopy concepts within the vast landscape of experimental possibilities.
Low-noise supercontinuum generation in chiral all-normal dispersion photonic crystal fibers
We present the advantages of supercontinuum generation in chiral, therefore circularly birefringent, all-normal dispersion fibers. Due to the absence of nonlinear power transfer between the polarization eigenstates of the fiber, chiral all-normal dispersion fibers do not exhibit any polarization instabilities and thus are an ideal platform for a low-noise supercontinuum generation. By pumping a chiral all-normal dispersion fiber at 802 nm, we obtained an octave-spanning, robustly circularly polarized supercontinuum with a low noise.
Deep-Learning Approach for the Atomic Configuration Interaction Problem on Large Basis Sets
High-precision atomic structure calculations require accurate modeling of electronic correlations typically addressed via the configuration interaction (CI) problem on a multiconfiguration wave function expansion. The latter can easily become challenging or infeasibly large even for advanced supercomputers. Here, we develop a deep-learning approach which allows us to preselect the most relevant configurations out of large CI basis sets until the targeted energy precision is achieved. The large CI computation is thereby replaced by a series of smaller ones performed on an iteratively expanding basis subset managed by a neural network. While dense architectures as used in quantum chemistry fail, we show that a convolutional neural network naturally accounts for the physical structure of the basis set and allows for robust and accurate CI calculations. The method was benchmarked on basis sets of moderate size allowing for the direct CI calculation, and further demonstrated on prohibitively large sets where the direct computation is not possible.
Merging automatic differentiation and the adjoint method for photonic inverse design
Alexander Luce, Rasoul Alaee, Fabian Knorr, Florian Marquardt
Optimizing shapes and topology of physical devices is crucial for both<br>scientific and technological advancements, given its wide-ranging implications<br>across numerous industries and research areas. Innovations in shape and<br>topology optimization have been seen across a wide range of fields, notably<br>structural mechanics, fluid mechanics, and photonics. Gradient-based inverse<br>design techniques have been particularly successful for photonic and optical<br>problems, resulting in integrated, miniaturized hardware that has set new<br>standards in device performance. To calculate the gradients, there are<br>typically two approaches: implementing specialized solvers using automatic<br>differentiation or deriving analytical solutions for gradient calculation and<br>adjoint sources by hand. In this work, we propose a middle ground and present a<br>hybrid approach that leverages and enables the benefits of automatic<br>differentiation and machine learning frameworks for handling gradient<br>derivation while using existing, proven solvers for numerical solutions.<br>Utilizing the adjoint method, we turn existing numerical solvers differentiable<br>and seamlessly integrate them into an automatic differentiation framework.<br>Further, this enables users to integrate the optimization environment with<br>machine learning applications which could lead to better photonic design<br>workflows. We illustrate the approach through two distinct examples: optimizing<br>the Purcell factor of a magnetic dipole in the vicinity of an optical<br>nanocavity and enhancing the light extraction efficiency of a {\textmu}LED.<br>
Extreme thermodynamics in nanolitre volumes through stimulated Brillouin–Mandelstam scattering
Andreas Geilen, Alexandra Popp, Debavan Das, Saher Junaid, Christopher G. Poulton, Mario Chemnitz, Christoph Marquardt, Markus A. Schmidt, Birgit Stiller
Nature Physics
19
1805-1812
(2023)
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Examining the physical properties of materials—particularly of toxic liquids—under a wide range of thermodynamic states is a challenging problem due to the extreme conditions the material has to experience. Such temperature and pressure regimes, which result in a change in the refractive index and sound velocity, can be accessed by optoacoustic interactions such as Brillouin–Mandelstam scattering. Here we demonstrate the Brillouin–Mandelstam measurements of nanolitre volumes of liquids in extreme thermodynamic regimes. This is enabled by a fully sealed liquid-core optical fibre containing carbon disulfide. Within this waveguide, which exhibits tight optoacoustic confinement and a high Brillouin gain, we are able to conduct spatially resolved measurements of the local Brillouin response, giving us access to a resolved image of the temperature and pressure values along the liquid channel. We measure the material properties of the liquid core at very large positive pressures (above 1,000 bar) and substantial negative pressures (below –300 bar), as well as explore the isobaric and isochoric regimes. The extensive thermodynamic control allows the tunability of the Brillouin frequency shift of more than 40% using only minute volumes of liquid.
Varying the Stiffness and Diffusivity of Rod-Shaped Microgels Independently through Their Molecular Building Blocks
Yonca Kittel, Luis P. B. Guerzoni, Carolina Itzin, Dirk Rommel, Matthias Mork, Céline Bastard, Bernhard Häßel, Abdolrahman Omidinia-Anarkoli, Silvia P. Centeno, et al.
Angewandte Chemie, International Edition in English
62
e202309779
(2023)
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Microgels are water-swollen, crosslinked polymers that are widely used as colloidal building blocks in scaffold materials for tissue engineering and regenerative medicine. Microgels can be controlled in their stiffness, degree of swelling, and mesh size depending on their polymer architecture, crosslink density, and fabrication method – all of which influence their function and interaction with the environment. Currently, there is a lack of understanding of how the polymer composition influences the internal structure of soft microgels and how this morphology affects specific biomedical applications. In this report, we systematically vary the architecture and molar mass of polyethylene glycol-acrylate (PEG-Ac) precursors, as well as their concentration and combination, to gain insight in the different parameters that affect the internal structure of rod-shaped microgels. We characterize the mechanical properties and diffusivity, as well as the conversion of acrylate groups during photopolymerization, in both bulk hydrogels and microgels produced from the PEG-Ac precursors. Furthermore, we investigate cell-microgel interaction, and we observe improved cell spreading on microgels with more accessible RGD peptide and with a stiffness in a range of 20 kPa to 50 kPa lead to better cell growth.
Transfer learning from Hermitian to non-Hermitian quantum many-body physics
Identifying phase boundaries of interacting systems is one of the key steps to understanding quantum many-body models. The development of various numerical and analytical methods has allowed exploring the phase diagrams of many Hermitian interacting systems. However, numerical challenges and scarcity of analytical solutions hinder obtaining phase boundaries in non-Hermitian many-body models. Recent machine learning methods have emerged as a potential strategy to learn phase boundaries from various observables without having access to the full many-body wavefunc- tion. Here, we show that a machine learning methodology trained solely on Hermitian correlation functions allows identifying phase boundaries of non-Hermitian interacting models. These results demonstrate that Hermitian machine learning algorithms can be redeployed to non-Hermitian mod- els without requiring further training to reveal non-Hermitian phase diagrams. Our findings es- tablish transfer learning as a versatile strategy to leverage Hermitian physics to machine learning non-Hermitian phenomena.
Dark solitons in Fabry-Pérot resonators with Kerr media and normal dispersion
Graeme, N. Campbell, Lewis Hill, Pascal Del'Haye, Gian-Luca Oppo
Physical Review A
108(3)
033505
(2023)
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The ranges of existence and stability of dark cavity-soliton stationary states in a Fabry-Pérot resonator with a Kerr nonlinear medium and normal dispersion are determined. The Fabry-Pérot configuration introduces nonlocal coupling that shifts the cavity detuning by the round trip average power of the intracavity field. When compared with ring resonators described by the Lugiato-Lefever equation, nonlocal coupling leads to strongly detuned dark cavity solitons that exist over a wide range of detunings. This shift is a consequence of the counterpropagation of intracavity fields inherent to Fabry-Pérot resonators. In contrast with ring resonators, the existence and stability of dark soliton solutions are dependent on the size and number of solitons in the cavity. We investigate the effect of nonlocal coupling of Fabry-Pérot resonators on multiple dark solitons, and we demonstrate long-range interactions and synchronization of temporal oscillations.
Conserved nucleocytoplasmic density homeostasis drives cellular organization across eukaryotes
Abin Biswas, Omar Munos, Kyoohyun Kim, Carsten Hoege, Benjamin M. Lorton, David Shechter, Jochen Guck, Vasily Zaburdaev, Simone Reber
The packing and confinement of macromolecules in the cytoplasm and nucleoplasm has profound implications for cellular biochemistry. How intracellular density distributions vary and affect cellular physiology remains largely unknown. Here, we show that the nucleus is less dense than the cytoplasm and that living systems establish and maintain a constant density ratio between these compartments. Using label-free biophotonics and theory, we show that nuclear density is set by a pressure balance across the nuclear envelope in vitro, in vivo and during early development. Nuclear transport establishes a specific nuclear proteome that exerts a colloid osmotic pressure, which, assisted by entropic chromatin pressure, draws water into the nucleus. Using C. elegans, we show that while nuclear-to-cytoplasmic (N/C) volume ratios change during early development, the N/C density ratio is robustly maintained. We propose that the maintenance of a constant N/C density ratio is the biophysical driver of one of the oldest tenets of cell biology: the N/C volume ratio. In summary, this study reveals a previously unidentified homeostatic coupling of macromolecular densities that drives cellular organization with implications for pathophysiologies such as senescence and cancer.
RNA binding proteins and glycoRNAs form domains on the cell surface for cell penetrating peptide entry
Jonathan Perr, Andreas Langen, Karim Almahayni, Gianluca Nestola, Peiyuan Chai, Charlotta G. Lebedenko, Regan Volk, Reese M. Caldwell, Malte Spiekermann, et al.
bioRxiv: https://doi.org/10.1101/2023.09.04.556039
(2023)
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The composition and organization of the cell surface determine how cells interact with their environment. Traditionally, glycosylated transmembrane proteins were thought to be the major constituents of the external surface of the plasma membrane. Here, we provide evidence that a group of RNA binding proteins (RBPs) are present on the surface of living cells. These cell surface RBPs (csRBPs) precisely organize into well-defined nanoclusters that are enriched for multiple RBPs, glycoRNAs, and their clustering can be disrupted by extracellular RNase addition. These glycoRNA-csRBP clusters further serve as sites of cell surface interaction for the cell penetrating peptide TAT. Removal of RNA from the cell surface, or loss of RNA binding activity by TAT, causes defects in TAT cell internalization. Together, we provide evidence of an expanded view of the cell surface by positioning glycoRNA-csRBP clusters as a regulator of communication between cells and the extracellular environment.
Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering
The increasing complexity of neural networks and the energy consumption associated with training and inference create a need for alternative neuromorphic approaches, e.g. using optics. Current proposals and implementations rely on physical non-linearities or opto-electronic conversion to realise the required non-linear activation function. However, there are significant challenges with these approaches related to power levels, control, energy-efficiency, and delays. Here, we present a scheme for a neuromorphic system that relies on linear wave scattering and yet achieves non-linear processing with a high expressivity. The key idea is to inject the input via physical parameters that affect the scattering processes. Moreover, we show that gradients needed for training can be directly measured in scattering experiments. We predict classification accuracies on par with results obtained by standard artificial neural networks. Our proposal can be readily implemented with existing state-of-the-art, scalable platforms, e.g. in optics, microwave and electrical circuits, and we propose an integrated-photonics implementation based on racetrack resonators that achieves high connectivity with a minimal number of waveguide crossings.
Roadmap on structured waves
Konstantin Y Bliokh, Ebrahim Karimi, Miles J Padgett, Miguel A Alonso, Mark R Dennis, Angela Dudley, Andrew Forbes, Sina Zahedpour, Scott W Hancock, et al.
Biomolecular condensates are membrane-less organelles made of multiple components, often including several distinct proteins and nucleic acids. However, current tools to measure condensate composition are limited and cannot capture this complexity quantitatively, as they either require fluorescent labels, which we show can perturb composition, or can distinguish only 1-2 components. Here, we describe a label-free method based on quantitative phase microscopy to measure the composition of condensates with an arbitrarily large number of components. We first validate the method empirically in binary mixtures, revealing sequence-encoded density variation and complex aging dynamics for condensates composed of full-length proteins. In simplified multi-component protein/RNA condensates, we uncover a regime of constant condensate density and a large range of protein:RNA stoichiometry when varying average composition. The unexpected decoupling of density and composition highlights the need to determine molecular stoichiometry in multi-component condensates. We foresee this approach enabling the study of compositional regulation of condensate properties and function.
Wigner function tomography via optical parametric amplification
Wigner function tomography is indispensable for characterizing quantum states, but its commonly used version, balanced homodyne detection, suffers from several weaknesses. First, it requires efficient detection, which is critical for measuring fragile non-Gaussian states, especially bright ones. Second, it needs a local oscillator, tailored to match the spatiotemporal properties of the state under test, and fails for multimode and broadband states. Here we propose Wigner function tomography based on optical parametric amplification followed by direct detection. The method is immune to detection inefficiency and loss, and suitable for broadband, spatially and temporally multimode quantum states. To prove the principle, we experimentally reconstruct the Wigner function of squeezed vacuum occupying a single mode of a strongly multimode state. We obtain a squeezing of −7.5±0.4dB and purity of 0.91(+0.09−0.08) despite more than 97% loss caused mainly by filtering. Theoretically, we also consider the reconstruction of a squeezed single photon—a bright non-Gaussian state. Due to multimode parametric amplification, the method allows for simultaneous tomography of multiple modes. This makes it a powerful tool for optical quantum information processing.
Self-learning Machines based on Hamiltonian Echo Backpropagation
Victor Lopez-Pastor, Florian Marquardt
Physical Review X
13(3)
031020
(2023)
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A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields.
Human T cells loaded with superparamagnetic iron oxide nanoparticles retain antigen-specific TCR functionality
Felix Pfister, Jan Dörrie, Niels Schaft, Vera Buchele, Harald Unterweger, Lucas R. Carnell, Patrick Schreier, Rene Stein, Markéta Kubánková, et al.
Frontiers in Immunology
14
1223695
(2023)
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BACKGROUND: Immunotherapy of cancer is an emerging field with the potential to improve long-term survival. Thus far, adoptive transfer of tumor-specific T cells represents an effective treatment option for tumors of the hematological system such as lymphoma, leukemia or myeloma. However, in solid tumors, treatment efficacy is low owing to the immunosuppressive microenvironment, on-target/off-tumor toxicity, limited extravasation out of the blood vessel, or ineffective trafficking of T cells into the tumor region. Superparamagnetic iron oxide nanoparticles (SPIONs) can make cells magnetically controllable for the site-specific enrichment. METHODS: In this study, we investigated the influence of SPION-loading on primary human T cells for the magnetically targeted adoptive T cell therapy. For this, we analyzed cellular mechanics and the T cell response after stimulation via an exogenous T cell receptor (TCR) specific for the melanoma antigen MelanA or the endogenous TCR specific for the cytomegalovirus antigen pp65 and compared them to T cells that had not received SPIONs. RESULTS: SPION-loading of human T cells showed no influence on cellular mechanics, therefore retaining their ability to deform to external pressure. Additionally, SPION-loading did not impair the T cell proliferation, expression of activation markers, cytokine secretion, and tumor cell killing after antigen-specific activation mediated by the TCR. CONCLUSION: In summary, we demonstrated that SPION-loading of T cells did not affect cellular mechanics or the functionality of the endogenous or an exogenous TCR, which allows future approaches using SPIONs for the magnetically enrichment of T cells in solid tumors.
Symmetry Broken Vectorial Kerr Frequency Combs from Fabry-Pérot Resonators
Lewis Hill, Eva-Maria Hirmer, Graeme Campell, Toby Bi, Alekhya Ghosh, Pascal Del'Haye, Gian-Luca Oppo
https://doi.org/10.48550/arXiv.2308.05039
(2023)
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Optical frequency combs find many applications in metrology, frequency standards, communications and photonic devices. We consider field polarization properties and describe a vector comb generation through the spontaneous symmetry breaking of temporal cavity solitons within coherently driven, passive, Fabry-Pérot cavities with Kerr nonlinearity. Global coupling effects due to the interactions of counter-propagating light restrict the maximum number of soliton pairs within the cavity - even down to a single soliton pair - and force long range polarization conformity in trains of vector solitons.
Highly Nonlinear Dynamics of In Vivo Deep-Tissue Interaction with
Femtosecond Laser Pulses at 1030 nm
Soyeon Jun, Andreas Herbst, Kilian Scheffter, Nora John, Julia Kolb, Daniel Wehner, Hanieh Fattahi
We report on the highly nonlinear behavior observed in the central nervous system tissue of zebrafish (Danio rerio) when exposed to femtosecond pulses at 1030 nm. At this irradiation wavelength, photo damage becomes detectable only after exceeding a specific peak intensity threshold, which is independent of the photon flux and irradiation time, distinguishing it from irradiation at shorter wavelengths. Furthermore, we investigate and quantify the role of excessive heat in reducing the damage threshold, particularly during high-repetition-rate operations, which are desirable for label-free and multi-dimensional microscopy techniques. To verify our findings, we examined cellular responses to tissue damage, including apoptosis and the recruitment of macrophages and fibroblasts at different time points post-irradiation. These findings substantially contribute to advancing the emerging nonlinear optical microscopy techniques and provide a strategy for inducing deep-tissue, precise and localized injuries using near-infrared femtosecond laser pulses.
Multi-stage spontaneous symmetry breaking of
light in Kerr ring resonators
Lewis Hill, Gian-Luca Oppo, Pascal Del'Haye
Communications Physics (6)
208
(2023)
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Symmetry breaking of light states is of interest for the understanding of nonlinear optics, photonic circuits, telecom applications and optical pulse generation. Here we demonstrate multi-stage symmetry breaking of the resonances of ring resonators with Kerr nonlinearity. This multi-stage symmetry breaking naturally occurs in a resonator with bidirectionally propagating light with orthogonal polarization components. The derived model used to theoretically describe the system shows that the four circulating field components can display full symmetry, full asymmetry, and multiple versions of partial symmetry, and are later shown to result in complex oscillatory dynamics - such as four-field self-switching, and unusual pulsing with extended delays between subsequent peaks. To highlight a few examples, our work has application in the development of photonic devices like isolators and circulators, logic gates, and random numbers generators, and could also be used for optical-sensors, e.g. by further enhancing the Sagnac effect.
IL-3 receptor signalling suppresses chronic intestinal inflammation by controlling mechanobiology and tissue egress of regulatory T cells
Karen Anne-Marie Ullrich, Julia Derdau, Carsten Baltes, Alice Battistella, Gonzalo Rosso, Stefan Uderhardt, Lisa Lou Schulze, Li-Juan Liu, Mark Dedden, et al.
IL-3 has been reported to be involved in various inflammatory disorders, but its role in inflammatory bowel disease (IBD) has not been addressed so far. Here, we determined IL-3 expression in samples from patients with IBD and studied the impact of Il3 or Il3r deficiency on T cell-dependent experimental colitis. We explored the mechanical, cytoskeletal and migratory properties of Il3r −/− and Il3r +/+ T cells using real-time deformability cytometry, atomic force microscopy, scanning electron microscopy, fluorescence recovery after photobleaching and in vitro and in vivo cell trafficking assays. We observed that, in patients with IBD, the levels of IL-3 in the inflamed mucosa were increased. In vivo, experimental chronic colitis on T cell transfer was exacerbated in the absence of Il-3 or Il-3r signalling. This was attributable to Il-3r signalling-induced changes in kinase phosphorylation and actin cytoskeleton structure, resulting in increased mechanical deformability and enhanced egress of Tregs from the inflamed colon mucosa. Similarly, IL-3 controlled mechanobiology in human Tregs and was associated with increased mucosal Treg abundance in patients with IBD. Collectively, our data reveal that IL-3 signaling exerts an important regulatory role at the interface of biophysical and migratory T cell features in intestinal inflammation and suggest that this might be an interesting target for future intervention.
Günter Ellrott, Paul Beck, Vitaliy Sultanov, Sergej Rothau, Norbert Lindlein, Maria Chekhova, Vojislav Kristic
Advanced Photonics Research
4(10)
2300159
(2023)
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Circular dichroism is a unique chiroptical signature of the chirality of a system and is a prevalent way to characterize and distinguish systems on a fundamental level and for their technological applicability. Thus, engineering and maximizing the chiroptical response of a single chiral object or a metasurface composed of chiral entities is a formidable task. Current efforts strongly focus on individual metallic nanostructures and their periodic ensembles to harvest on (resonant) plasmonic properties and interactions. This route, however, waives the advantages of high-refractive-index nanoscale materials embracing low dissipative losses at optical wavelengths and electromagnetic fields penetrating and propagating in such materials. Herein, a strong circular dichroism is demonstrated in square lattices of nanohelices made of the high-refractive-index semiconductor germanium, with dissymmetry factors outperforming metal-based ensembles. The observation of a much higher dissymmetry emerges for illumination with spatially coherent light, in comparison to spatially incoherent light. High dissymmetry is attributed to cooperative coupling between single helices, resulting from the combination of dielectric resonances of both the individual helical building blocks and the highly ordered lattice.
Label-free discrimination of extracellular vesicles from large lipoproteins
Anna D. Kashkanova, Martin Blessing, Marie Reischke, Jan-Ole Baur, Andreas S. Baur, Vahid Sandoghdar, Jan Van Deun
Journal of extracellular vesicles
12
12348
(2023)
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Journal
Extracellular vesicles (EVs) are increasingly gaining interest as biomarkers and therapeutics. Accurate sizing and quantification of EVs remain problematic, given their nanometre size range and small scattering cross-sections. This is compounded by the fact that common EV isolation methods result in co-isolation of particles with comparable features. Especially in blood plasma, similarly-sized lipoproteins outnumber EVs to a great extent. Recently, interferometric nanoparticle tracking analysis (iNTA) was introduced as a particle analysis method that enables determining the size and refractive index of nanoparticles with high sensitivity and precision. In this work, we apply iNTA to differentiate between EVs and lipoproteins, and compare its performance to conventional nanoparticle tracking analysis (NTA). We show that iNTA can accurately quantify EVs in artificial EV-lipoprotein mixtures and in plasma-derived EV samples of varying complexity. Conventional NTA could not report on EV numbers, as it was not able to distinguish EVs from lipoproteins. iNTA has the potential to become a new standard for label-free EV characterization in suspension.
Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies
Ultrashort time-domain spectroscopy, particularly field-resolved spectroscopy, are established methods for identifying the constituents and internal dynamics of samples. However, these techniques are often encumbered by the Nyquist criterion, leading to prolonged data acquisition and processing times as well as sizable data volumes. To mitigate these issues, we have successfully implemented the first instance of time-domain compressed sensing, enabling us to pinpoint the primary absorption peaks of atmospheric water vapor in response to tera-hertz light transients that exceed the Nyquist limit. Our method demonstrates successful identification of water absorption peaks up to 2.5 THz, even for sampling rates where the Nyquist frequency is as low as 0.75 THz, with a mean squared error of 12*10-4. Time-domain sparse sampling achieves considerable data compression while also expediting both the measurement and data processing time, representing a significant stride towards the realm of real-time spectroscopy
Quadrature nonreciprocity in bosonic networks without breaking time-reversal symmetry
Clara C. Wanjura, Jesse J. Slim, Javier del Pino, Matteo Brunelli, Ewold Verhagen, Andreas Nunnenkamp
Nature Physics
19
1429-1436
(2023)
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Nonreciprocity means that the transmission of a signal depends on its direction of propagation. Despite vastly different platforms and underlying working principles, the realizations of nonreciprocal transport in linear, time-independent systems rely on Aharonov–Bohm interference among several pathways and require breaking time-reversal symmetry. Here we extend the notion of nonreciprocity to unidirectional bosonic transport in systems with a time-reversal symmetric Hamiltonian by exploiting interference between beamsplitter (excitation-preserving) and two-mode-squeezing (excitation non-preserving) interactions. In contrast to standard nonreciprocity, this unidirectional transport manifests when the mode quadratures are resolved with respect to an external reference phase. Accordingly, we dub this phenomenon ‘quadrature nonreciprocity’. We experimentally demonstrate it in the minimal system of two coupled nanomechanical modes orchestrated by optomechanical interactions. Next, we develop a theoretical framework to characterize the class of networks exhibiting quadrature nonreciprocity based on features of their particle–hole graphs. In addition to unidirectionality, these networks can exhibit an even–odd pairing between collective quadratures, which we confirm experimentally in a four-mode system, and an exponential end-to-end gain in the case of arrays of cavities.
Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully solved using optimization techniques, including methods such as gradient-ascent pulse engineering (GRAPE) , relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. In this work, we introduce feedback GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power considering various scenarios based on cavity-QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multiqubit devices to linear-optics quantum computation strategies, quantum enhanced sensing with adaptive measurements, and quantum error correction.
De novo identification of universal cell mechanics gene signatures
Marta Urbanska, Yan Ge, Maria Winzi, Shada Abuhattum Hofemeier, Syed Shafat Ali, Maik Herbig, Martin Kräter, Nicole Toepfner, Joanna Durgan, et al.
Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.
Topological phase diagrams of exactly solvable non-Hermitian interacting Kitaev chains
Sharareh Sayyad, Jose L. Lado
Physical Review Research
5(2)
L022046
(2023)
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Many-body interactions give rise to the appearance of exotic phases in Hermitian physics. Despite their importance, many-body effects remain an open problem in non-Hermitian physics due to the complexity of treating many-body interactions. Here, we present a family of exact and numerical phase diagrams for non-Hermitian interacting Kitaev chains. In particular, we establish the exact phase boundaries for the dimerized Kitaev-Hubbard chain with complex-valued Hubbard interactions. Our results reveal that some of the Hermitian phases disappear as non-Hermiticty is enhanced. Based on our analytical findings, we explore the regime of the model that goes beyond the solvable regime, revealing regimes where non-Hermitian topological degeneracy remains. The combination of our exact and numerical phase diagrams provides an extensive description of a family of non-Hermitian interacting models. Our<br>results provide a stepping stone toward characterizing non-Hermitian topology in realistic interacting quantum many-body systems.
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik
Digital Discovery
10.1039/d3dd00044c
(2023)
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String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional<br>string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencIng Embedded Strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions<br>of \selfieslib, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of \selfieslib (version 2.1.1) in this manuscript.
Spin-orbit interaction in nanofiber-based Brillouin scattering
Maxime Zerbib, Maxime Romanet, Thibaut Sylvestre, Christian Wolff, Birgit Stiller, Jean-Charles Beugnot, Kien Phan Huy
Optics Express
31(14)
22284-22295
(2023)
Angular momentum is an important physical property that plays a key role in light-matter interactions such as spin-orbit interaction. Here, we investigate theoretically and experimentally the spin-orbit interaction between a circularly polarized optical (spin) and a transverse vortex acoustic wave (orbital) using Brillouin backscattering in a silica optical nanofiber. We specifically explore the state of polarization of Brillouin backscattering induced by the TR21 torso-radial vortex acoustic mode that carries an orbital angular momentum. Using a full-vectorial theoretical model, we predict and observe two operating regimes for which the backscattered Brillouin signal is either depolarized or circularly polarized depending on the input pump polarization. We demonstrate that when the pump is circularly polarized and thus carries a spin angular momentum, the backscattered signal undergoes a handedness reversal of circular polarization due to optoacoustic spin-orbit interaction and the conservation of overall angular momentum.
Alcohol-sourced acetate impairs T cell function by promoting cortactin acetylation
Vugar Azizov, Michael Hübner, Michael Frech, Jörg Hofmann, Markéta Kubánková, Dennis Lapuente, Matthias Tenbusch, Jochen Guck, Georg Schett, et al.
Alcohol is among the most widely consumed dietary substances. Excessive alcohol consumption damages the liver, heart, and brain. Alcohol also has strong immunoregulatory properties. Here, we report how alcohol impairs T cell function via acetylation of cortactin, a protein that binds filamentous actin and facilitates branching. Upon alcohol consumption, acetate, the metabolite of alcohol, accumulates in lymphoid organs. T cells exposed to acetate, exhibit increased acetylation of cortactin. Acetylation of cortactin inhibits filamentous actin binding and hence reduces T cell migration, immune synapse formation and activation. While mutated, acetylation-resistant cortactin rescues the acetate-induced inhibition of T cell migration, primary mouse cortactin knockout T cells exhibited impaired migration. Acetate-induced cytoskeletal changes effectively inhibited activation, proliferation, and immune synapse formation in T cells in vitro and in vivo in an influenza infection model in mice. Together these findings reveal cortactin as a possible target for mitigation of T cell driven autoimmune diseases.
Non-Hermitian chiral anomalies in interacting systems
Optical monitoring and screening of photocatalytic batch reactions using cuvettes ex situ is time-consuming, requires substantial amounts of samples, and does not allow the analysis of species with low extinction coefficients. Hollow-core photonic crystal fibers (HC-PCFs) provide an innovative approach for in situ reaction detection using ultraviolet–visible absorption spectroscopy, with the potential for high-throughput automation using extremely low sample volumes with high sensitivity for monitoring of the analyte. HC-PCFs use interference effects to guide light at the center of a microfluidic channel and use this to enhance detection sensitivity. They open the possibility of comprehensively studying photocatalysts to extract structure–activity relationships, which is unfeasible with similar reaction volume, time, and sensitivity in cuvettes. Here, we demonstrate the use of HC-PCF microreactors for the screening of the electron transfer properties of carbon dots (CDs), a nanometer-sized material that is emerging as a homogeneous light absorber in photocatalysis. The CD-driven photoreduction reaction of viologens (XV2+) to the corresponding radical monocation XV•+ is monitored in situ as a model reaction, using a sample volume of 1 μL per measurement and with a detectability of <1 μM. A range of different reaction conditions have been systematically studied, including different types of CDs (i.e., amorphous, graphitic, and graphitic nitrogen-doped CDs), surface chemistry, viologens, and electron donors. Furthermore, the excitation irradiance was varied to study its effect on the photoreduction rate. The findings are correlated with the electron transfer properties of CDs based on their electronic structure characterized by soft X-ray absorption spectroscopy. Optofluidic microreactors with real-time optical detection provide unique insight into the reaction dynamics of photocatalytic systems and could form the basis of future automated catalyst screening platforms, where samples are only available on small scales or at a high cost.
Tunable fiber source of entangled UV-C and infrared photons
Santiago López-Huidrobro, Noureddin Mohammad, Maria V. Chekhova, Nicolas Y. Joly
One of the main challenges in quantum physics is predicting efficiently the dynamics of observables in many-body problems out of equilibrium. A particular example occurs in adiabatic quantum computing, where finding the structure of the instantaneous gap of the Hamiltonian is crucial in order to optimize the speed of the computation. Inspired by this challenge, in this work we explore the potential of deep learning for discovering a mapping from the parameters that fully identify a problem Hamiltonian to the full evolution of the gap during an adiabatic sweep applying different network architectures. Through this example, we find that a limiting factor for the learnability of the dynamics is the size of the input, that is, how the number of parameters needed to identify the Hamiltonian scales with the system size. We demonstrate that a long short-term memory network succeeds in predicting the gap when the parameter space scales linearly with system size. Remarkably, we show that once this architecture is combined with a convolutional neural network to deal with the spatial structure of the model, the gap evolution can even be predicted for system sizes larger than the ones seen by the neural network during training. This provides a significant speedup in comparison with the existing exact and approximate algorithms in calculating the gap.
Organic Molecules as Origin of Visible-Range Single Photon Emission from Hexagonal Boron Nitride and Mica
Michael Neumann, Xu Wei, Luis Morales-Inostroza, Seunghyun Song, Sung-Gyu Lee, Kenji Watanabe, Takashi Taniguchi, Stephan Götzinger, Young Hee Lee
The discovery of room-temperature single-photon emitters (SPEs) hosted by two-dimensional hexagonal boron nitride (2D hBN) has sparked intense research interest. Although emitters in the vicinity of 2 eV have been studied extensively, their microscopic identity has remained elusive. The discussion of this class of SPEs has centered on point defects in the hBN crystal lattice, but none of the candidate defect structures have been able to capture the great heterogeneity in emitter properties that is observed experimentally. Employing a widely used sample preparation protocol but disentangling several confounding factors, we demonstrate conclusively that heterogeneous single-photon emission at ∼2 eV associated with hBN originates from organic molecules, presumably aromatic fluorophores. The appearance of those SPEs depends critically on the presence of organic processing residues during sample preparation, and emitters formed during heat treatment are not located within the hBN crystal as previously thought, but at the hBN/substrate interface. We further demonstrate that the same class of SPEs can be observed in a different 2D insulator, fluorophlogopite mica.
Quantum Efficiency of Single Dibenzoterrylene Molecules in p-Dichlorobenzene at Cryogenic Temperatures
Mohammad Musavinezhad, Alexey Shkarin, Dominik Rattenbacher, Jan Renger, Tobias Utikal, Stephan Götzinger, Vahid Sandoghdar
The Journal of Physical Chemistry B
5353-5359
(2023)
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Journal
We measure the quantum efficiency (QE) of individual dibenzoterrylene (DBT) molecules embedded in p-dichlorobenzene at cryogenic temperatures. To achieve this, we combine two distinct methods based on the maximal photon emission and on the power required to saturate the zero-phonon line to compensate for uncertainties in some key system parameters. We find that the outcomes of the two approaches are in good agreement for reasonable values of the parameters involved, reporting a large fraction of molecules with QE values above 50%, with some exceeding 70%. Furthermore, we observe no correlation between the observed lower bound on the QE and the lifetime of the molecule, suggesting that most of the molecules have a QE exceeding the established lower bound. This confirms the suitability of DBT for quantum optics experiments. In light of previous reports of low QE values at ambient conditions, our results hint at the possibility of a strong temperature dependence of the QE.
Protecting Quantum Modes in Optical Fibers
Muhammad Abdullah Butt, Paul Roth, Gordon Wong, Michael Frosz, Luis Sanchez-Soto, E. A. Anashkina, A. V. Andrianov, Peter Banzer, Philip Russell, et al.
Polarization-preserving fibers maintain the two polarization states of an orthogonal basis. Quantum communication, however, requires sending at least two nonorthogonal states and these cannot both be preserved. We present an alternative scheme that allows for using polarization encoding in a fiber not only in the discrete, but also in the continuous-variable regime. For the example of a helically twisted photonic crystal fiber, we experimentally demonstrate that using appropriate nonorthogonal modes, the polarization-preserving fiber does not fully scramble these modes over the full Poincaré sphere, but that the output polarization will stay on a great circle; that is, within a one-dimensional protected subspace, which can be parametrized by a single variable. This allows for more efficient measurements of quantum excitations in nonorthogonal modes.
Quintic Dispersion Soliton Frequency Combs in a Microresonator
Chip-scale optical frequency combs have attracted significant research interest and can be used in applications ranging from precision spectroscopy to telecom channel generators and lidar systems. In the time domain, microresonator based frequency combs correspond to self-stabilized soliton pulses. In two distinct regimes, microresonators are shown to emit either bright solitons in the anomalous dispersion regime or dark solitons (a short time of darkness in a bright background signal) in the normal dispersion regime. Here, the dynamics of continuous-wave-laser-driven soliton generation is investigated in the zero-group-velocity-dispersion regime as well as the generation of solitons that are spectrally crossing different dispersion regimes. In the measurements, zero-dispersion solitons with multipeak structures (soliton molecules) are observed with distinct and predictable spectral envelopes that are a result of fifth-order dispersion of the resonators. Numerical simulations and the analysis of bifurcation structures agree well with the observed soliton states. This is the first observation of soliton generation that is governed by fifth-order dispersion, which can have applications in ultrafast optics, telecom systems, and optical spectroscopy.
Simple, Economic, and Robust Rail-Based Setup for Super-Resolution Localization Microscopy
The Journal of Physical Chemistry A
127(20)
4553-4560
(2023)
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Research during the past 2 decades has showcased the power of single-molecule<br>localization microscopy (SMLM) as a tool for exploring the nanoworld. However, SMLM<br>systems are typically available in specialized laboratories and imaging facilities, owing to their expensiveness as well as complex assembly and alignment procedure. Here, we lay out the blueprint of a sturdy, rail-based, cost-efficient, multicolor SMLM setup that is easy to construct and align in service of simplifying the accessibility of SMLM. We characterize the optical properties of the design and assess its capabilities, robustness, and stability. The performance<br>of the system is assayed using super-resolution imaging of biological samples. We believe that this design will make SMLM more affordable and broaden its availability.
Nonlinear Interferometry for Quantum-Enhanced Measurements of Multiphoton Absorption
Shahram Panahiyan, Carlos Sánchez Muñoz, Maria V. Chekhova, Frank Schlawin
Physical Review Letters
130
203604
(2023)
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Multiphoton absorption is of vital importance in many spectroscopic, microscopic, or lithographic applications. However, given that it is an inherently weak process, the detection of multiphoton absorption signals typically requires large field intensities, hindering its applicability in many practical situations. In this Letter, we show that placing a multiphoton absorbent inside an imbalanced nonlinear interferometer can enhance the precision of multiphoton cross section estimation with respect to strategies based on photon-number measurements using coherent or even squeezed light directly transmitted through the medium. In particular, the power scaling of the sensitivity with photon flux can be increased by 1 order compared with transmission measurements of the sample with coherent light, such that the measurement precision at any given intensity can be greatly enhanced. Furthermore, we show that this enhanced measurement precision is robust against experimental imperfections leading to photon losses, which usually tend to degrade the detection sensitivity. We trace the origin of this enhancement to an optimal degree of squeezing which has to be generated in a nonlinear SU(1,1) interferometer.
Discovering Quantum Circuit Components with Program Synthesis
Despite rapid progress in the field, it is still challenging to discover new<br>ways to take advantage of quantum computation: all quantum algorithms need to<br>be designed by hand, and quantum mechanics is notoriously counterintuitive. In<br>this paper, we study how artificial intelligence, in the form of program<br>synthesis, may help to overcome some of these difficulties, by showing how a<br>computer can incrementally learn concepts relevant for quantum circuit<br>synthesis with experience, and reuse them in unseen tasks. In particular, we<br>focus on the decomposition of unitary matrices into quantum circuits, and we<br>show how, starting from a set of elementary gates, we can automatically<br>discover a library of new useful composite gates and use them to decompose more<br>and more complicated unitaries.<br>
Electromagnetically induced transparency-like
effect in a lithium niobate resonator via
electronic control
In this study, we theoretically proposed a method to achieve an electromagnetically induced transparency (EIT)-like effect in a whispering gallery mode resonator (WGMR) and experimentally validated the method in a lithium niobate (LN) device. Benefitting from the electro-optic and inverse piezoelectric effects of the LN material, two modes of the LN WGMR that are close in frequency can be tuned at different tuning rates, resulting in EIT-like resonance lineshapes. By varying the electric field applied to the LN WGMR, the full dynamic of the EIT-like phenomenon can be precisely controlled. The experimental results agreed well with the calculations based on the coupled mode theory. Moreover, we observed a hysteresis resulting from the photorefractive effect of LN. We believe our proposed method and demonstrated devices offer a way to control an EIT-like effect, which could have potential applications in light storage, quantum information processing, and enhanced sensing techniques.
Identification of a Distinct Monocyte-Driven Signature in Systemic Sclerosis Using Biophysical Phenotyping of Circulating Immune Cells
Alexandru-Emil Matei, Markéta Kubánková, Liyan Xu, Andrea-Hermina Györfi, Evgenia Boxberger, Despina Soteriou, Maria Papava, Julia Prater, Xuezhi Hong, et al.
Arthritis & Rheumatology
75(5)
768-781
(2023)
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OBJECTIVE: Pathologically activated circulating immune cells, including monocytes, play major roles in systemic sclerosis (SSc). Their functional characterization can provide crucial information with direct clinical relevance. However, tools for the evaluation of pathologic immune cell activation and, in general, of clinical outcomes in SSc are scarce. Biophysical phenotyping (including characterization of cell mechanics and morphology) provides access to a novel, mostly unexplored layer of information regarding pathophysiologic immune cell activation. We hypothesized that the biophysical phenotyping of circulating immune cells, reflecting their pathologic activation, can be used as a clinical tool for the evaluation and risk stratification of patients with SSc. METHODS: We performed biophysical phenotyping of circulating immune cells by real-time fluorescence and deformability cytometry (RT-FDC) in 63 SSc patients, 59 rheumatoid arthritis (RA) patients, 28 antineutrophil cytoplasmic antibody–associated vasculitis (AAV) patients, and 22 age- and sex-matched healthy donors. RESULTS: We identified a specific signature of biophysical properties of circulating immune cells in SSc patients that was mainly driven by monocytes. Since it is absent in RA and AAV, this signature reflects an SSc-specific monocyte activation rather than general inflammation. The biophysical properties of monocytes indicate current disease activity, the extent of skin or lung fibrosis, and the severity of manifestations of microvascular damage, as well as the risk of disease progression in SSc patients. CONCLUSION: Changes in the biophysical properties of circulating immune cells reflect their pathologic activation in SSc patients and are associated with clinical outcomes. As a high-throughput approach that requires minimal preparations, RT-FDC–based biophysical phenotyping of monocytes can serve as a tool for the evaluation and risk stratification of patients with SSc.
Dynamics of cell rounding during detachment
Agata Nyga, Katarzyna Plak, Martin Kräter, Marta Urbanska, Kyoohyun Kim, Jochen Guck, Buzz Baum
Animal cells undergo repeated shape changes, for example by rounding up and respreading as they divide. Cell rounding can be also observed in interphase cells, for example when cancer cells switch from a mesenchymal to an ameboid mode of cell migration. Nevertheless, it remains unclear how interphase cells round up. In this article, we demonstrate that a partial loss of substrate adhesion triggers actomyosin-dependent cortical remodeling and ERM activation, which facilitates further adhesion loss causing cells to round. Although the path of rounding in this case superficially resembles mitotic rounding in involving ERM phosphorylation, retraction fiber formation, and cortical remodeling downstream of ROCK, it does not require Ect2. This work provides insights into the way partial loss of adhesion actives cortical remodeling to drive cell detachment from the substrate. This is important to consider when studying the mechanics of cells in suspension, for example using methods like real-time deformability cytometry (RT-DC).
Setting the stage for universal pharmacological targeting of the glycocalyx
All cells in the human body are covered by a complex meshwork of sugars as well as proteins and lipids to which these sugars are attached, collectively termed the glycocalyx. Over the past few decades, the glycocalyx has been implicated in a range of vital cellular processes in health and disease. Therefore, it has attracted considerable interest as a therapeutic target. Considering its omnipresence and its relevance for various areas of cell biology, the glycocalyx should be a versatile platform for therapeutic intervention, however, the full potential of the glycocalyx as therapeutic target is yet to unfold. This might be attributable to the fact that glycocalyx alterations are currently discussed mainly in the context of specific diseases. In this perspective review, we shift the attention away from a disease-centered view of the glycocalyx, focusing on changes in glycocalyx state. Furthermore, we survey important glycocalyx-targeted drugs currently available and finally discuss future steps. We hope that this approach will inspire a unified, holistic view of the glycocalyx in disease, helping to stimulate novel glycocalyx-targeted therapy strategies.
Hybrid THz architectures for molecular polaritonics
Ahmed Jaber, Michael Reitz, Avinash Singh, Ali Maleki, Yongbao Xin, Brian Sullivan, Ksenia Dolgaleva, Robert W. Boyd, Claudiu Genes, et al.
We explore several schemes of electromagnetic field confinement aimed at<br>facilitating the collective strong coupling of a localized photonic mode to<br>molecular vibrations in the terahertz region. The key aspects are the use of<br>plasmonic metasurface structures combined with standard Fabry-Perot<br>configurations and the deposition of a thin layer of glucose, via a spray<br>coating technique, within a tightly focused electromagnetic mode volume. We<br>observe vacuum Rabi splittings reaching up to 140 GHz and study the complex<br>interplay between plasmonic resonances, photonic cavity modes and low-energy<br>molecular resonances. Our study provides key insight into the design of<br>polaritonic platforms with organic molecules to harvest the unique properties<br>of hybrid light-matter states.<br>
Rapid single-cell physical phenotyping of mechanically dissociated tissue biopsies
Despina Soteriou, Markéta Kubánková, Christine Schweitzer, Rocío López-Posadas, Rahmita Pradhan, Oana-Maria Thoma, Andrea-Hermina Györfi, Alexandru-Emil Matei, Maximilian Waldner, et al.
Nature Biomedical Engineering
7
1392-1403
(2023)
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During surgery, rapid and accurate histopathological diagnosis is essential for clinical decision making. Yet the prevalent method of intra-operative consultation pathology is intensive in time, labour and costs, and requires the expertise of trained pathologists. Here we show that biopsy samples can be analysed within 30 min by sequentially assessing the physical phenotypes of singularized suspended cells dissociated from the tissues. The diagnostic method combines the enzyme-free mechanical dissociation of tissues, real-time deformability cytometry at rates of 100–1,000 cells s−1 and data analysis by unsupervised dimensionality reduction and logistic regression. Physical phenotype parameters extracted from brightfield images of single cells distinguished cell subpopulations in various tissues, enhancing or even substituting measurements of molecular markers. We used the method to quantify the degree of colon inflammation and to accurately discriminate healthy and tumorous tissue in biopsy samples of mouse and human colons. This fast and label-free approach may aid the intra-operative detection of pathological changes in solid biopsies.
Modulational instability and spectral broadening of vortex modes in chiral photonic crystal fibers
Paul Roth, Philip Russell, Michael Frosz, Yang Chen, Gordon Wong
Journal of Lightwave Technology
41(7)
2061-2069
(2023)
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We report on intra- and inter-modal four-wave-mixing (FWM) in N-fold rotationally symmetric (C_N) single- and multi-core chiral photonic crystal fiber (PCF), created by spinning the preform during fiber drawing. The non-circular modal field is forced to rotate as it propagates along the fiber, resulting in circular birefringence and robust maintenance of circular polarization state. Multi-core chiral C_N PCF supports vortex-carrying helical Bloch modes (HBMs) in which the degeneracy between clockwise and counter-clockwise vortices is lifted. This makes possible new kinds of intermodal polarization modulational instability (PMI). We develop PMI theory for vortex HBMs, and illustrate the results by a series of experiments in which two or more PMI sidebands with different vorticities and polarization states are selectively generated by adjusting the polarization state and topological charge of the pump light. In every case both the topological charge and the spin of the pump light are conserved. We also report generation of a broadband supercontinuum in a single circularly polarized vortex mode.
Confocal Interferometric Scattering Microscopy Reveals 3D Nanoscopic Structure and Dynamics in Live Cells
Michelle Küppers, David Albrecht, Anna D. Kashkanova, Jennifer Lühr, Vahid Sandoghdar
Bright-field light microscopy and related techniques continue to play a key role in life sciences because they provide a facile and label-free insight into biological specimen. However, lack of three-dimensional imaging and low sensitivity to nanoscopic features hamper their application in high-end quantitative studies. Here, we remedy these shortcomings by employing confocal interferometric scattering (iSCAT) microscopy. We demonstrate the performance of this label-free technique in a selection of case studies in live cells and benchmark our findings against simultaneously acquired fluorescence images. We reveal the nanometric topography of the nuclear envelope, quantify the dynamics of the endoplasmic reticulum, detect single microtubules, and map nanoscopic diffusion of clathrin-coated pits undergoing endocytosis. Furthermore, we introduce the combination of confocal and wide-field iSCAT modalities for simultaneous imaging of cellular structures and high-speed tracking of nanoscopic entities such as single SARS-CoV2 virions. Confocal iSCAT can be readily implemented as an additional contrast mechanism in existing laser scanning microscopes.
On-the-fly precision spectroscopy with a dual-modulated tunable diode laser
and Hz-level referencing to a cavity
Shuangyou Zhang, Toby Bi, Pascal Del'Haye
https://doi.org/10.48550/arXiv.2303.14180
(2023)
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Advances in high-resolution laser spectroscopy have enabled many scientific breakthroughs in physics, chemistry, biology and astronomy. Optical frequency combs have pushed measurement limits with ultrahigh-frequency accuracy and fast-measurement speed while tunable diode laser spectroscopy is used in scenarios that require high power and continuous spectral coverage. Despite these advantages of tunable diode laser spectroscopy, it is challenging to precisely determine the instantaneous frequency of the laser because of fluctuations in the scan speed. Here we demonstrate a simple spectroscopy scheme with a frequency modulated diode laser that references the diode laser on-the-fly to a fiber cavity with sub-15 Hz frequency precision over an 11-THz range at a measurement speed of 1 THz/s. This is an improvement of more than two orders of magnitude compared to existing diode laser spectroscopy methods. Our scheme provides precise frequency calibration markers while simultaneously tracking the instantaneous scan speed of the laser. We demonstrate several applications, including dispersion measurement of an ultra-high-Q microresonator and spectroscopy of an HF gas cell, which can be used for absolute frequency referencing of the tunable diode laser. The simplicity, robustness and low costs of this spectroscopy scheme could prove extremely valuable for out-of-the-lab applications like LIDAR, gas spectroscopy and environmental monitoring.
Dynamic Brillouin cooling for continuous optomechanical systems
Changlong Zhu, Birgit Stiller
Materials for Quantum Technology
3
015003
(2023)
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Up until now, ground state cooling using optomechanical interaction is realized in the regime where optical dissipation is higher than mechanical dissipation. Here, we demonstrate that optomechanical ground state cooling in a continuous optomechanical system is possible by using backward Brillouin scattering while mechanical dissipation exceeds optical dissipation which is the common case in optical waveguides. The cooling is achieved in an anti-Stokes backward Brillouin process by modulating the intensity of the optomechanical coupling via a pulsed pump to suppress heating processes in the strong coupling regime. With such dynamic modulation, a significant cooling factor can be achieved, which can be several orders of magnitude lower than for the steady-state case. This modulation scheme can also be applied to Brillouin cooling generated by forward intermodal Brillouin scattering.
Multiphoton non-local quantum interference controlled by an undetected photon
Kaiyi Qian, Kai Wang, Leizhen Chen, Hou Zhaohua, Mario Krenn, Shining Zhu, Xiao-Song Ma
Nature Communications
14
1480 (2023)
(2023)
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The interference of quanta lies at the heart of quantum physics. The multipartite generalization<br>of single-quanta interference creates entanglement, the coherent superposition of states shared by several quanta. Entanglement allows non-local correlations between many quanta and hence is a key resource for quantum information technology. Entanglement is typically considered to be essential for creating non-local correlations, manifested by multipartite interference. Here, we show that this is not the case and demonstrate multiphoton non-local quantum interference without entanglement of any intrinsic properties of the photons. We harness the superposition of the physical origin of a four-photon product state, which leads to constructive and destructive interference of the photons’ mere existence. With the intrinsic indistinguishability in the generation process of photons, we realize four-photon frustrated quantum interference. We furthermore establish non-local control of multipartite quantum interference, in which we tune the phase of one undetected photon and observe the interference of the other three photons. Our work paves the way for fundamental studies of non-locality and potential applications in quantum technologies.
Impact of crowding on the diversity of expanding populations
Carl F. Schreck, Diana Fusco, Yuya Karita, Stephen Martis, Jona Kayser, Marie-Cécilia Duvernoy, Oskar Hallatschek
Proceedings of the National Academy of Sciences of the United States of America
120(11)
e2208361120
(2023)
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Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population’s range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria–Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically “pushed out” of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter—the characteristic growth layer thickness—and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.
Classical Phase Space Crystals in Open Environment
It was recently discovered that a crystalline many-body state can exist in the phase space of a closed dynamical system. Phase space crystal can be anomalous Chern insulator that supports chiral topological transport without<br>breaking physical time-reversal symmetry [L. Guo et al., Phys. Rev. B 105, 094301 (2022)]. In this work, we further study the effects of open dissipative environment with thermal noise, and identify the existence condition of<br>classical phase space crystals in realistic scenarios. By defining a crystal order parameter, we plot the phase diagram in the parameter space of dissipation rate, interaction and temperature. Our present work paves the way to realise phase space crystals and explore anomalous chiral transport in<br>experiments.
PT symmetry-protected exceptional cones and analogue Hawking radiation
Marcus Stålhammar, Jorge Larana-Aragon, Lucas Rødland, Flore K. Kunst
New Journal of Physics
25
043012
(2023)
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Non-Hermitian Hamiltonians, which effectively describe dissipative systems, and analogue gravity models, which simulate properties of gravitational objects, comprise seemingly different areas of current research. Here, we investigate the interplay between the two by relating parity-time- symmetric dissipative Weyl-type Hamiltonians to analogue Schwarzschild black holes emitting Hawking radiation. We show that the exceptional points of these Hamiltonians form tilted cones mimicking the behavior of the light cone of a radially infalling observer approaching a black hole horizon. We further investigate the presence of tunneling processes, reminiscent of those happening in black holes, in a concrete example model. We interpret the non-trivial result as the purely thermal contribution to analogue Hawking radiation in a Schwarzschild black hole. Assuming that our particular Hamiltonian models a photonic crystal, we discuss the concrete nature of the analogue Hawking radiation in this particular setup.
Quench-drive spectroscopy and high-harmonic generation in BCS superconductors
Matteo Puviani, Rafael Haenel, Dirk Manske
Physical Review B (107)
094501
(2023)
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In pump-probe spectroscopies, THz pulses are used to quench a system, which is subsequently probed by either a THz or optical pulse. In contrast, third-harmonic generation experiments employ a single multicycle driving pulse and measure the induced third harmonic. In this work, we analyze a spectroscopy setup where both a quench and a drive are applied and two-dimensional spectra as a function of time and quench-drive delay are recorded. We calculate the time evolution of the nonlinear current generated in the superconductor within an Anderson-pseudospin framework and characterize all experimental signatures using a quasiequilibrium approach. We analyze the superconducting response in Fourier space with respect to both the frequencies corresponding to the real time and the quench-drive delay time. In particular, we show the presence of a transient modulation of higher harmonics, induced by a wave mixing process of the drive with the quench pulse, which probes both quasiparticle and collective excitations of the superconducting condensate.
Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa
Mahyar Dahmardeh, Houman Mirzaalian Dastjerdi, Hisham Mazal, Harald Köstler, Vahid Sandoghdar
Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing<br>their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise<br>so that collection of more photons should allow its detection sensitivity to biomolecules of arbitrarily low mass. However, a<br>number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in<br>iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the<br>mass sensitivity limit by a factor of four to below 10 kDa. We implement this scheme both with a user-defined feature matrix<br>and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal<br>reflection mode. Our work opens the door to the optical detection of small traces of disease markers such as alpha-synuclein,<br>chemokines, and cytokines.
Crystal superlattices for versatile and sensitive quantum spectroscopy
Zi S. D. Toa, Maria V. Chekhova, Leonid A. Krivitsky, Anna V. Paterova
Optics Express
31(5)
7265-7276
(2023)
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Nonlinear interferometers with quantum correlated photons have been demonstrated to improve optical characterization and metrology. These interferometers can be used in gas spectroscopy, which is of particular interest for monitoring greenhouse gas emissions, breath analysis and industrial applications. Here, we show that gas spectroscopy can be further enhanced via the deployment of crystal superlattices. This is a cascaded arrangement of nonlinear crystals forming interferometers, allowing the sensitivity to scale with the number of nonlinear elements. In particular, the enhanced sensitivity is observed via the maximum intensity of interference fringes that scales with low concentration of infrared absorbers, while for high concentration the sensitivity is better in interferometric visibility measurements. Thus, a superlattice acts as a versatile gas sensor since it can operate by measuring different observables, which are relevant to practical applications. We believe that our approach offers a compelling path towards further enhancements for quantum metrology and imaging using nonlinear interferometers with correlated photons.
A new hyperelastic lookup table for RT-DC
Lukas Daniel Wittwer, Felix Reichel, Paul Mueller, Jochen Guck, Sebastian Aland
Soft Matter
19(11)
2064-2073
(2023)
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Journal
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Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real-time. A lookup table (LUT) disentangles the experimentally derived steady-state cell deformation and the projected area to extract the cell stiffness in the form of the Young's modulus. So far, two lookup tables exist but are limited to simple linear material models and cylindrical channel geometries. Here, we present two new lookup tables for RT-DC based on a neo-Hookean hyperelastic material numerically derived by simulations based on the finite element method in square and cylindrical channel geometries. At the same time, we quantify the influence of the shear-thinning behavior of the surrounding medium on the stationary deformation of cells in RT-DC and discuss the applicability and impact of the proposed LUTs regarding past and future RT-DC data analysis. Additionally, we provide insights about the cell strain and stresses, as well as the influence resulting from the rotational symmetric assumption on the cell deformation and volume estimation. The new lookup tables and the numerical cell shapes are made freely available.
In situ Detection of Cobaloxime Intermediates During Photocatalysis Using Hollow-Core Photonic Crystal Fiber Microreactors
Takashi Lawson, Alexander S. Gentleman, Jonathan Pinnell, Annika Eisenschmidt, Daniel Antón-García, Michael Frosz, Erwin Reisner, Tijmen G. Euser
Angewandte Chemie, International Edition in English
62(9)
e202214788
(2023)
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Hollow-core photonic crystal fibers (HC-PCFs) provide a novel approach for in situ UV/Vis spectroscopy with enhanced detection sensitivity. Here, we demonstrate that longer optical path lengths than afforded by conventional cuvette-based UV/Vis spectroscopy can be used to detect and identify the CoI and CoII states in hydrogen-evolving cobaloxime catalysts, with spectral identification aided by comparison with DFT-simulated spectra. Our findings show that there are two types of signals observed for these molecular catalysts; a transient signal and a steady-state signal, with the former being assigned to the CoI state and the latter being assigned to the CoII state. These observations lend support to a unimolecular pathway, rather than a bimolecular pathway, for hydrogen evolution. This study highlights the utility of fiber-based microreactors for understanding these and a much wider range of homogeneous photocatalytic systems in the future.
Machine learning assisted inverse design of
microresonators
Optics Express
31(5)
8020-8028
(2023)
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The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities, and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ∼460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest yields the best results. The average error on the simulated data is well below 15%.
Multicolor super-resolution imaging to study human coronavirus RNA during cellular infection
Anish R. Roy, Jiarui Wang, Mengting Han, Haifeng Wang, Leonhard Möckl, Leiping Zeng, William E. Moerner, Lei S. Qi
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third human coronavirus within 20 years that gave rise to a life-threatening disease and the first to reach pandemic spread. While the scientific community has studied coronavirus biology using genomics, cryoelectron microscopy, and electron tomography, how coronavirus RNA is spatially organized in the cell at the different stages of the viral replication cycle at nanoscale resolution is largely unknown. To make therapeutic headway against current and future coronaviruses, the biology of coronavirus RNA during infection must be precisely understood. Here, we introduce a multicolor super-resolution (SR) fluorescence imaging framework to examine the spatial interactions between viral RNA and other viral factors during host cell infection. We demonstrate the efficacy of our approach using the HCoV-229E coronavirus in MRC5 lung fibroblasts and specifically label two key oligonucleotide viral players: viral genomic RNA (gRNA) and double-stranded RNA (dsRNA). The 10-nm resolution achieved by our approach uncovers a striking spatial organization of gRNA and dsRNA into three distinct RNA structures: (1) large gRNA clusters, (2) very tiny nanoscale gRNA puncta containing a single copy of the genome, and (3) round intermediate-sized puncta highlighted by the dsRNA label. Furthermore, we use our two-color SR approach to visualize the nanoscale spatial relationships between viral gRNA and the endoplasmic reticulum (ER), dsRNA and ER, gRNA and the spike protein, and gRNA and dsRNA. In particular, we observe two striking observations that provide insight into viral replication and export. First, spike proteins and gRNA rarely assemble into an assembled virion in the MRC5 cytoplasm. Second, in contrast to previous observations, dsRNA and gRNA spatially separate. Our approach provides a comprehensive imaging framework that will enable future investigations of coronavirus fundamental biology and the effects of therapeutics.
Selective phase filtering of charged beams with laser-driven antiresonant hollow-core fibers
Luca Genovese, Max Kellermeier, Frank Mayet, Klaus Floettmann, Gordon Wong, Michael Frosz, Ralph Assmann, Philip Russell, Francois Lemery
Physical Review Research
5(1)
013096
(2023)
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Emerging accelerator concepts increasingly rely on the combination of high-frequency electromagnetic radiation with electron beams, enabling longitudinal phase space manipulation which supports a variety of advanced applications. The handshake between electron beams and radiation is conventionally provided by magnetic undulators which unfortunately require a balance between the electron beam energy, undulator parameters, and laser wavelength. Here we propose a scheme using laser-driven large-core antiresonant optical fibers to manipulate electron beams. We explore two general cases using TM01 and HE11 modes. In the former, we show that large energy modulations O(100 keV). can be achieved while maintaining the overall electron beam quality. Further, we show that by using larger field strengths O(100 MV/m) the resulting transverse forces can be exploited with beam-matching conditions to filter arbitrary phases from the modulated electron bunch, leading to the production of ≈100 attosecond FWHM microbunches. Finally, we also investigate the application of the transverse dipole HE11 mode and find it suitable for supporting time-resolved electron beam measurements with sub-attosecond resolution. We expect the findings to be widely appealing to high-charge pump-probe experiments, metrology, and accelerator science.
Deep learning of many-body observables and quantum information scrambling
Naeimeh Mohseni, Junheng Shi, Tim Byrnes, Michael Hartmann
Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibited remarkable power in modeling quantum many-body systems. Here, we explore how the capacity of data-driven deep neural networks in learning the dynamics of physical observables is correlated with the scrambling of quantum information. We train a neural network to find a mapping from the parameters of a model to the evolution of observables in random quantum circuits for various regimes of quantum<br>scrambling and test its \textit{generalization} and \textit{extrapolation} capabilities in applying it to unseen circuits. Our results show that a particular type of recurrent neural network is extremely powerful in generalizing its predictions within the system size and time window that it has been trained on for both, localized and scrambled regimes. These include<br>regimes where classical learning approaches are known to fail in sampling from a representation of the full wave function. Moreover, the considered neural network succeeds in \textit{extrapolating} its predictions beyond the time window and system size that it has been trained on for models that show localization, but not in scrambled regimes.
Learning Quantum Systems
Valentin Gebhart, Raffaele Santagati, Antonio Andrea Gentile, Erik Gauger, David Craig, Natalia Ares, Leonardo Banchi, Florian Marquardt, Luca Pezzè, et al.
Quantum technologies hold the promise to revolutionise our society with<br>ground-breaking applications in secure communication, high-performance<br>computing and ultra-precise sensing. One of the main features in scaling up<br>quantum technologies is that the complexity of quantum systems scales<br>exponentially with their size. This poses severe challenges in the efficient<br>calibration, benchmarking and validation of quantum states and their dynamical<br>control. While the complete simulation of large-scale quantum systems may only<br>be possible with a quantum computer, classical characterisation and<br>optimisation methods (supported by cutting edge numerical techniques) can still<br>play an important role.<br> Here, we review classical approaches to learning quantum systems, their<br>correlation properties, their dynamics and their interaction with the<br>environment. We discuss theoretical proposals and successful implementations in<br>different physical platforms such as spin qubits, trapped ions, photonic and<br>atomic systems, and superconducting circuits. This review provides a brief<br>background for key concepts recurring across many of these approaches, such as<br>the Bayesian formalism or Neural Networks, and outlines open questions.<br>
We introduce a general method to engineer arbitrary Hamiltonians in the Floquet phase space of a periodically driven oscillator, based on the non-commutative Fourier transformation (NcFT) technique. We establish the relationship between an arbitrary target Floquet Hamiltonian in phase space and the periodic driving potential in real space. We obtain analytical expressions for the driving potentials in real space that can generate novel Hamiltonians in phase space, e.g., rotational lattices and sharp-boundary well. Our protocol<br>can be realised in a range of experimental platforms for nonclassical states generation and bosonic quantum computation.
Tunneling-induced fractal transmission in Valley Hall waveguides
Tirth Shah, Florian Marquardt, Vittorio Peano
Physical Review B
10.1103/PhysRevB.107.054304
(2023)
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The Valley Hall effect provides a popular route to engineer robust waveguides<br>for bosonic excitations such a photons and phonons. The almost complete absence<br>of backscattering in many experiments has its theoretical underpinning in a<br>smooth-envelope approximation that neglects large momentum transfer and is<br>accurate only for small bulk band gaps and/or smooth domain walls. For larger<br>bulk band gaps and hard domain walls backscattering is expected to become<br>significant. Here, we show that in this experimentally relevant regime, the<br>reflection of a wave at a sharp corner becomes highly sensitive on the<br>orientation of the outgoing waveguide relative to the underlying lattice.<br>Enhanced backscattering can be understood as being triggered by resonant<br>tunneling transitions in quasimomentum space. Tracking the resonant tunneling<br>energies as a function of the waveguide orientation reveals a self-repeating<br>fractal pattern that is also imprinted in the density of states and the<br>backscattering rate at a sharp corner.<br>
Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks
Alexander Luce, Ali Mahdavi, Heribert Wankerl, Florian Marquardt
Machine Learning: Science and Technology
4(1)
015014
(2023)
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The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical<br>targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for<br>out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most<br>favorable results.
Proposal for a hybrid clock system consisting of passive and active optical clocks and a fully stabilized microcomb
Deshui Yu, Frank Vollmer, Pascal Del'Haye, Shougang Zhang
Optics Express
31(4)
6228-6240
(2023)
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Optical atomic clocks produce highly stable frequency standards and frequency combs bridge clock frequencies with hundreds of terahertz difference. In this paper, we propose a hybrid clock scheme, where a light source pumps an active optical clock through a microresonator-based nonlinear third harmonic process, serves as a passive optical clock via indirectly locking its frequency to an atomic transition, and drives a chip-scale microcomb whose mode spacing is stabilized using the active optical clock. The operation of the whole hybrid system is investigated through simulation analysis. The numerical results show: (i) The short-term frequency stability of the passive optical clock follows an Allan deviation of σy(τ) = 9.3 × 10−14τ−1/2 with the averaging time τ, limited by the population fluctuations of interrogated atoms. (ii) The frequency stability of the active optical clock reaches σy(τ) = 6.2 × 10−15τ−1/2, which is close to the quantum noise limit. (iii) The mode spacing of the stabilized microcomb has a shot-noise-limited Allan deviation of σy(τ) = 1.9 × 10−11τ−1/2. Our hybrid scheme may be realized using recently developed technologies in (micro)photonics and atomic physics, paving the way towards on-chip optical frequency comparison, synthesis, and synchronization.
Shear rheology of methyl cellulose based solutions for cell mechanical measurements at high shear rates
Beyza Büyükurganci, Santanu Kumar Basu, Markus Neuner, Jochen Guck, Andreas Wierschem, Felix Reichel
Methyl cellulose (MC) is a widely used material in various microfluidic applications in biology. Due to its biocompatibility, it has become a popular crowding agent for microfluidic cell deformability measurements, which usually operate at high shear rates (>10 000 s−1). However, a full rheological characterization of methyl cellulose solutions under these conditions has not yet been reported. With this study, we provide a full shear-rheological description for solutions of up to 1% MC dissolved in phosphate-buffered saline (PBS) that are commonly used in real-time deformability cytometry (RT-DC). We characterized three different MC-PBS solutions used for cell mechanical measurements in RT-DC with three different shear rheometer setups to cover a range of shear rates from 0.1–150 000 s−1. We report viscosities and normal stress differences in this regime. Viscosity functions can be well described using a Carreau–Yasuda model. Furthermore, we present the temperature dependency of shear viscosity and first normal stress difference of these solutions. Our results show that methyl cellulose solutions behave like power-law liquids in viscosity and exhibit first normal stress difference at shear rates between 5000–150 000 s−1. We construct a general viscosity equation for each MC solution at a certain shear rate and temperature. Furthermore, we investigated how MC concentration influences the rheology of the solutions and found the entanglement concentration at around 0.64 w/w%. Our results help to better understand the viscoelastic behavior of MC solutions, which can now be considered when modelling stresses in microfluidic channels.
Linear optical elements based on cooperative subwavelength emitter arrays
Nico S. Baßler, Michael Reitz, Kai P. Schmidt, Claudiu Genes
We describe applications of two-dimensional subwavelength quantum emitter<br>arrays as efficient optical elements in the linear regime. For normally<br>incident light, the cooperative optical response, stemming from emitter-emitter<br>dipole exchanges, allows the control of the array's transmission, its resonance<br>frequency, and bandwidth. Operations on fully polarized incident light, such as<br>generic linear and circular polarizers as well as phase retarders can be<br>engineered and described in terms of Jones matrices. Our analytical approach<br>and accompanying numerical simulations identify optimal regimes for such<br>operations and reveal the importance of adjusting the array geometry and of the<br>careful tuning of the external magnetic fields amplitude and direction.<br>
Optical Vortex Brillouin Laser
Xinglin Zeng, Philip Russell, Yang Chen, Zheqi Wang, Gordon Wong, Paul Roth, Michael Frosz, Birgit Stiller
Laser & Photonics Reviews
2200277
(2023)
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Optical vortices, which have been extensively studied over the last decades, offer an additional degree of freedom useful in many applications, such as optical tweezers and quantum control. Stimulated Brillouin scattering (SBS), providing a narrow linewidth and a strong nonlinear response, has been used to realize quasi-continuous wave lasers. Here, stable oscillation of optical vortices and acoustic modes in a Brillouin laser based on chiral photonic crystal fiber (PCF) is reported, which robustly supports helical Bloch modes (HBMs) that carry circularly polarized optical vortex and display circular birefringence. A narrow-linewidth Brillouin fiber laser that stably emits 1st- and 2nd-order vortex-carrying HBMs is implemented. Angular momentum conservation selection rules dictate that pump and backward Brillouin signals have opposite topological charge and spin. Additionally, it is shown that when the chiral PCF is placed within a laser ring cavity, the linewidth-narrowing associated with lasing permits the peak of the Brillouin gain that corresponds to acoustic mode to be measured with resolution of 10 kHz and accuracy of 520 kHz. The results pave the way to a new generation of vortex-carrying SBS systems with applications in optical tweezers, quantum information processing, and vortex-carrying nonreciprocal systems.
From Dyson Models to Many-Body Quantum Chaos
Alexei Andreanov, Matteo Carrega, Jeff Murugan, Jan Olle, Dario Rosa, Ruth Shir
Understanding the mechanisms underlying many-body quantum chaos is one of the big challenges in theoretical physics. We tackle this problem by considering a set of perturbed quadratic Sachdev-Ye-Kitaev (SYK) Hamiltonians defined on graphs. This allows to disambiguate between operator growth and<br>\emph{delocalization}, showing that the latter is the dominant process in the single-particle to many-body chaotic transition. Our results are verified numerically with state-of-the-art numerical techniques, capable of extracting<br>eigenvalues in a desired energy window of very large Hamiltonians, in this case up to dimension $2^{19}\times 2^{19}$. Our approach essentially provides a new way of viewing many-body chaos from a single-particle perspective.
A quantum trajectory analysis of singular wave functions
The Schrödinger equation admits smooth and finite solutions that spontaneously evolve into a singularity, even for a free particle. This blowup is generally ascribed to the intrinsic dispersive character of the associated time evolution. We resort to the notion of quantum trajectories to reinterpret this singular behavior. We show that the blowup can be directly related to local phase variations, which generate an underlying velocity field responsible for driving the quantum flux toward the singular region.
Embracing the diversity of model systems to deconstruct the basis
of regeneration and tissue repair
The eighth EMBO conference in the series ‘The Molecular and Cellular Basis of Regeneration and Tissue Repair’ took place in Barcelona (Spain) in September 2022. A total of 173 researchers from across the globe shared their latest advances in deciphering the molecular and cellular basis of wound healing, tissue repair and regeneration, as well as their implications for future clinical applications. The conference showcased an ever-expanding diversity of model organisms used to identify mechanisms that promote regeneration. Over 25 species were discussed, ranging from invertebrates to humans. Here, we provide an overview of the exciting topics presented at the conference, highlighting novel discoveries in regeneration and perspectives for regenerative medicine.
AI-discovery of a new charging protocol in a micromaser quantum battery
We propose a general computational framework for optimizing model-dependent<br>parameters in quantum batteries (QB). We apply this method to two different<br>charging scenarios in the micromaser QB and we discover a new charging protocol<br>for stabilizing the battery in upper-laying Hilbert space chambers in a<br>controlled and automatic way. This protocol is found to be stable and robust,<br>and it leads to an improved charging efficiency in micromaser QBs. Moreover,<br>our optimization framework is highly versatile and efficient, holding great<br>promise for the advancement of QB technologies at all scales.<br>
No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography
The effectiveness of measurement-based feedback control (MBFC) protocols is hindered by the presence of measurement noise, which impairs the ability to accurately infer the underlying dynamics of a quantum system from noisy continuous measurement records. To circumvent this limitation, a real-time stochastic state estimation approach is proposed in this work, that enables noise-free monitoring of the conditional dynamics, including the full density matrix of the quantum system, despite using noisy measurement data. This, in turn, enables the development of precise MBFC strategies that leads to effective control of quantum systems by essentially mitigating the constraints imposed by measurement noise, and has potential applications in various feedback quantum control scenarios. This approach is particularly important for machine learning-based control, where the AI controller can be trained with arbitrary conditional averages of observables, including the full density matrix, to quickly and accurately learn control strategies.
On-chip quantum interference between the origins of a multi-photon state
Lan-Tian Feng, Ming Zhang, Di Liu, Yu-Jie Cheng, Guo-Ping Guo, Dao-Xin Dai, Guang-Can Guo, M. Krenn, Xi-Feng Ren
Optica
10(1)
2103.14277
105-109
(2023)
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Quantum mechanically, multiple particles can jointly be in a coherent superposition of two or more different states at the same time. This property is called quantum entanglement, and gives rise to characteristic nonlocal interference and stays at the heart of quantum information process. Here, rather than interference of different intrinsic properties of particles, we experimentally demonstrated coherent superposition of two different birthplaces of a four-photon state. The quantum state is created in four probabilistic photon-pair sources, two combinations of which can create photon quadruplets. Coherent elimination and revival of distributed 4-photons can be fully controlled by tuning a phase. The stringent coherence requirements are met by using a silicon-based integrated photonic chip that contains four spiral waveguides for producing photon pairs via spontaneous four-wave mixing. The experiment gives rise to peculiar nonlocal phenomena without any obvious involvement of entanglement. Besides several potential applications that exploit the new on-chip technology, it opens up the possibility for fundamental studies on nonlocality with spatially separated locations.
Quantum coherent control in pulsed waveguide optomechanics
Junyin Zhang, Changlong Zhu, Christian Wolff, Birgit Stiller
Physical Review Research
5(1)
013010
(2023)
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Coherent control of traveling acoustic excitations in a waveguide system is an interesting way to manipulate and transduce classical and quantum information. So far, these interactions, often based on optomechanical resonators or Brillouin scattering, have been studied in the steady-state regime using continuous waves. However, waveguide experiments are often based on optical pump pulses, which require treatment in a dynamic framework. In this paper, we present an effective Hamiltonian formalism in the dynamic regime using optical pulses that links waveguide optomechanics and cavity optomechanics, which can be used in the classical and quantum regime including quantum noise. Based on our formalism, a closed solution for coupled-mode equation under the undepleted assumption is provided and we found that the strong coupling regime is already accessible in current Brillouin waveguides by using pulses. We further investigate several possible experiments within waveguide optomechanics, including Brillouin-based coherent transfer, Brillouin cooling, and optoacoustic entanglement.
Image-based cell sorting using focused travelling surface acoustic waves
Sorting cells is an essential primary step in many biological and clinical applications such as high-throughput drug screening, cancer research and cell transplantation. Cell sorting based on their mechanical properties has long been considered as a promising label-free biomarker that could revolutionize the isolation of cells from heterogeneous populations. Recent advances in microfluidic image-based cell analysis combined with subsequent label-free sorting by on-chip actuators demonstrated the possibility of sorting cells based on their physical properties. However, the high purity of sorting is achieved at the expense of a sorting rate that lags behind the analysis throughput. Furthermore, stable and reliable system operation is an important feature in enabling the sorting of small cell fractions from a concentrated heterogeneous population. Here, we present a label-free cell sorting method, based on the use of focused travelling surface acoustic wave (FTSAW) in combination with real-time deformability cytometry (RT-DC). We demonstrate the flexibility and applicability of the method by sorting distinct blood cell types, cell lines and particles based on different physical parameters. Finally, we present a new strategy to sort cells based on their mechanical properties. Our system enables the sorting of up to 400 particles per s. Sorting is therefore possible at high cell concentrations (up to 36 million per ml) while retaining high purity (>92%) for cells with diverse sizes and mechanical properties moving in a highly viscous buffer. Sorting of small cell fraction from a heterogeneous population prepared by processing of small sample volume (10 μl) is also possible and here demonstrated by the 667-fold enrichment of white blood cells (WBCs) from raw diluted whole blood in a continuous 10-hour sorting experiment. The real-time analysis of multiple parameters together with the high sensitivity and high-throughput of our method thus enables new biological and therapeutic applications in the future.
Epithelial RAC1-dependent cytoskeleton dynamics controls cell mechanics, cell shedding and barrier integrity in intestinal inflammation
Luz del Carmen Martínez-Sánchez, Phuong Anh Ngo, Rashmita Pradhan, Lukas-Sebastian Becker, David Boehringer, Despina Soteriou, Markéta Kubánková, Christine Schweitzer, Tatyana Koch, et al.
OBJECTIVE: Increased apoptotic shedding has been linked to intestinal barrier dysfunction and development of inflammatory bowel diseases (IBD). In contrast, physiological cell shedding allows the renewal of the epithelial monolayer without compromising the barrier function. Here, we investigated the role of live cell extrusion in epithelial barrier alterations in IBD. DESIGN: Taking advantage of conditional GGTase and RAC1 knockout mice in intestinal epithelial cells (Pggt1biΔIEC and Rac1iΔIEC mice), intravital microscopy, immunostaining, mechanobiology, organoid techniques and RNA sequencing, we analysed cell shedding alterations within the intestinal epithelium. Moreover, we examined human gut tissue and intestinal organoids from patients with IBD for cell shedding alterations and RAC1 function. RESULTS: Epithelial Pggt1b deletion led to cytoskeleton rearrangement and tight junction redistribution, causing cell overcrowding due to arresting of cell shedding that finally resulted in epithelial leakage and spontaneous mucosal inflammation in the small and to a lesser extent in the large intestine. Both in vivo and in vitro studies (knockout mice, organoids) identified RAC1 as a GGTase target critically involved in prenylation-dependent cytoskeleton dynamics, cell mechanics and epithelial cell shedding. Moreover, inflamed areas of gut tissue from patients with IBD exhibited funnel-like structures, signs of arrested cell shedding and impaired RAC1 function. RAC1 inhibition in human intestinal organoids caused actin alterations compatible with arresting of cell shedding. CONCLUSION: Impaired epithelial RAC1 function causes cell overcrowding and epithelial leakage thus inducing chronic intestinal inflammation. Epithelial RAC1 emerges as key regulator of cytoskeletal dynamics, cell mechanics and intestinal cell shedding. Modulation of RAC1 might be exploited for restoration of epithelial integrity in the gut of patients with IBD.
Photon pairs bi-directionally emitted from a resonant metasurface
Changjin Son, Vitaliy Sultanov, Tomas Santiago-Cruz, Aravind P. Anthur, Haizhong Zhang, Ramon Paniagua-Dominguez, Leonid Krivitsky, Arseniy I. Kuznetsov, Maria V. Chekhova
Metasurfaces are artificially structured surfaces able to control the properties of light at subwavelength scales. While, initially, they have been proposed as means to control classical optical fields, they are now emerging as nanoscale sources of quantum light, in particular of entangled photons with versatile properties. Geometric resonances in metasurfaces have been recently used to engineer the frequency spectrum of entangled photons, but the emission directivity was so far less studied. Here, we generate photon pairs via spontaneous parametric down conversion from a metasurface supporting a quasi-bound state in the continuum (BIC) leading to remarkable emission directivities. The pair generation rate is enhanced 67 times compared to the case of an unpatterned film of the same thickness and material. At the wavelength of the quasi-BIC resonance, photons are mostly emitted backwards, while their partners, spectrally detuned by only 8 nm, are emitted forwards. This behavior demonstrates fine spectral splitting of entangled photons and their bi-directional emission, never before observed in nanoscale sources. We expect this work to be a starting point for the efficient demultiplexing of photons in nanoscale quantum optics.
Artificial Intelligence and Machine Learning for Quantum Technologies
Mario Krenn, Jonas Landgraf, Thomas Fösel, Florian Marquardt
Physical Review A (107)
010101
(2023)
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In recent years the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feed- back strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.
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