Bayesian inference helps detect quantum change points in photon sequences.
problem Detecting a change point in a series of quantum photon states.
method Bayesian inference applied to online photon measurement.
result Local-detection success probability improved by using machine learning.
Enhances quantum machine learning models using Fock states.
problem Data-embedding bottleneck in quantum machine learning.
method Photonic-based bosonic data-encoding scheme in Fock space.
result Controlled expressive power via photon number.
Quantum blockchain uses entangled photon states over time.
problem Secure and efficient blockchain without space-based entanglement.
method Temporal GHZ state of photons encoding blockchain, nonclassical past influence.
result Entanglement in time provides quantum advantage for blockchain.
Study on black holes and photon surfaces in 4D spacetimes, proving uniqueness theorems.
problem Uniqueness of black hole and photon surfaces in 4D spacetimes.
method Potential theory approach, self-contained proofs for known and new cases.
result Proves new results for connected photon spheres and photon surfaces in the extremal case, and super-extremal case.
The paper characterizes photon surfaces in static spacetimes and proves their uniqueness.
problem Characterizing and proving uniqueness of photon surfaces in static spacetimes.
method Local characterization and proof of uniqueness using specific spacetime properties.
result Static, vacuum, asymptotically isotropic spacetimes with equipotential photon surfaces are isometric to Schwarzschild spacetime.
Characterizes photon surfaces in static spacetimes, proving uniqueness.
problem Understanding photon surfaces in static spacetimes of arbitrary dimension.
method Complete characterization and new insights into spacetime geometry.
result Proves uniqueness of certain electrostatic spacetimes.
Derives exact gradients for linear optics with single photons.
problem Gradient estimation for linear optics with single photons.
method Generalized parameter shift rule for linear optics.
result Derives analytical formula for gradients in linear optics.
Study of trapped photons in Kerr spacetime's phase space.
problem Characterize trapped photons in Kerr spacetime.
method Explicit proof and new proof of trapped photons' set as a smooth 5D submanifold.
result Set of trapped photons forms a smooth 5D submanifold with topology SO(3)imesR2. Quantum computing at room temperature achieves high accuracy in image classification.
problem Classifying images with single photons at room temperature.
method Optical transformation of quantum state to exploit interference and entanglement.
result Theoretical accuracy of 41.27% for MNIST and 36.14% for Fashion-MNIST.
Paper proves uniqueness of black holes and photon surfaces in higher dimensions.
problem Proving uniqueness of static vacuum black holes and photon surfaces in higher dimensions.
method Combining and generalizing techniques from previous works by Müller zum Hagen, Robinson, and Seifert, the authors prove geometric inequalities for connected (n+1)-dimensional spacetimes.
result Recovering and extending known uniqueness results for black holes and photon surfaces in higher dimensions.
In a recent paper the first author established the uniqueness of photon spheres, suitably defined, in static vacuum asymptotically flat spacetimes by adapting Israel's proof of static black hole uniqueness. In this note we establish uniqueness of photon spheres by adapting the argument of Bunting and Masood-ul-Alam, wh…
The paper simplifies FLRW photon propagators using geometric embeddings.
problem Understanding Friedmann-Lemaître-Robertson-Walker (FLRW) spaces.
method Differential-geometric methods applied to FLRW spaces as submanifolds in \(\mathbb{R}^{n+2}\).
result New and simplified expressions for the photon propagator in four dimensions.
Quantum memristors created in photonic platforms with real-time control.
problem Creating quantum memristors for integrated quantum photonics.
method Designing a tunable beam splitter with real-time control, using weak measurements and classical feedback.
result Demonstrated that the tunable beam splitter behaves as a quantum memristor.
Photonic quantum reinforcement learning for control problems.
problem Solving continuous control problems with noisy quantum computers.
method Proximal policy optimization for photonic variational quantum agents.
result Photonic policy learning achieves comparable performance to classical neural networks.
Uniqueness proven for photon spheres in higher-dimensional spacetimes.
problem Proving uniqueness of photon spheres in higher-dimensional electrovacuum spacetimes.
method Combining ideas from earlier works with newer techniques.
result Proven uniqueness of subextremal Reissner-Nordström manifolds with positive mass.
MarmoNet automates analysis of marmoset brain axonal projections.
problem Automatically detect and segment axonal tracer signals in noisy, cluttered images.
method Uses machine learning, specifically CNNs and image registration, to process and map axonal projections.
result Automated pipeline extracts and maps axonal projections robustly.
Machine learning classifies topological phases in leaky photonic lattices.
problem Classifying topological phases in leaky photonic lattices using limited data.
method A fully connected neural network trained on bulk intensity measurements.
result Accurate determination of topological properties from intensity distributions.
Study improves chiral photonic metasurface design using neural networks and genetic algorithms.
problem Optimizing chiral photonic metasurfaces for high chiral dichroism and reflectivity.
method Combines neural network and genetic algorithm approaches with improved fitness functions and data augmentation.
result Demonstrates a significant increase in chiral dichroism and reflectivity.
New proofs of unique photon surfaces in 4D spacetimes, extending previous work.
problem Proving uniqueness of photon surfaces in 4D static vacuum spacetimes.
method Different proofs based on black hole uniqueness and Willmore inequality.
result Partial proof of Willmore inequality in 3D.
Adapting Israel's proof of static black hole uniqueness, we show that the Schwarzschild spacetime is the only static vacuum asymptotically flat spacetime that possesses a suitably defined photon sphere.
Photonic chip speeds up option pricing with GAN for financial efficiency.
problem Bottleneck in classical computing limits financial industry development.
method Unary approach, photonic chip, quantum amplitude estimation, GAN for asset distribution.
result Quadratic speedup over classical Monte Carlo methods.
In a recent paper, the authors established the uniqueness of photon spheres in static vacuum asymptotically flat spacetimes by adapting Bunting and Masood-ul-Alam's proof of static vacuum black hole uniqueness. Here, we establish uniqueness of suitably defined sub-extremal photon spheres in static electro-vacuum asympt…
Unified framework for photon and massive particle hypersurfaces in stationary spacetimes.
problem Understanding photon and massive particle hypersurfaces in stationary spacetimes.
method Unified framework using Killing-invariant timelike hypersurfaces and associated Finsler structures.
result Conditions for a hypersurface to be a photon or massive particle hypersurface are established.
Machine learning improves quantum experiment design.
problem Designing complex quantum experiments to produce high-dimensional entangled states.
method Projective simulation model, machine learning to create new experiments.
result Machine learning system autonomously discovers experimental techniques.
Designs chiral photonic structures using machine learning for efficient optical properties.
problem Optimizing chiral photonic nanostructures for light-matter interactions.
method Evolutionary algorithm and neural network approach for rapid optimization.
result Frequency-dependent modification in reflected light's degree of circular polarization.
The paper examines Reissner-Nordstrøm-de Sitter black holes and their photon sphere.
problem Analyzing the properties of Reissner-Nordstrøm-de Sitter black holes.
method Investigating the conditions for the existence of three horizons and the photon sphere.
result Proving the existence of only one photon sphere under specific conditions.
Photonic co-processor speeds up training of large neural networks.
problem Training large neural networks with backpropagation is inefficient and communication is a bottleneck.
method Direct Feedback Alignment (DFA) with a photonic accelerator.
result Photonic accelerator can compute random projections with trillions of parameters.
New approach confirms Kruskal-Szekeres extension for Schwarzschild spacetime.
problem Confirming the Kruskal-Szekeres extension for Schwarzschild spacetime.
method Reformulating the problem as an ODE and showing the ODE admits a solution if and only if the horizon is non-degenerate.
result Photon surfaces approaching the Killing horizon must necessarily cross it.
Study reveals optimal scaling conditions for photonic neural networks.
problem Impact of reservoir size and learning routines on convergence-speed during learning.
method Used a greedy algorithm to train a photonic neural network for chaotic signals prediction.
result Determined convergence speed of learning as a function of reservoir size and found close to linear scaling.
Paper tackles Bayesian image restoration in low-photon Poisson imaging problems.
problem Bayesian inference in challenging low-photon Poisson imaging problems.
method Plug-and-play (PnP) Langevin sampling strategies with accelerated methods and mirror sampling.
result Effective PnP Langevin sampling methods for low-photon Poisson imaging problems.
Proves a Minkowski inequality for static Einstein-Maxwell space-time.
problem Understanding the photon sphere in static Einstein-Maxwell space-time.
method Inverse mean curvature flow (IMCF) approach.
result Proves a Minkowski-like inequality for asymptotically flat static Einstein-Maxwell space-time.
Quantum Proof-of-Work uses boson sampling to secure blockchain consensus.
problem Ensuring secure and efficient blockchain consensus.
method Proposes using quantum boson sampling as a Proof-of-Work scheme for blockchain.
result Demonstrates a robust and energy-efficient PoW scheme.
Optimizes plasmonic mirror filters using multi-fidelity Gaussian processes.
problem Optimizing transmission properties of plasmonic mirror color filters.
method Combining numerical methods with FDTD simulations and multi-fidelity Gaussian processes.
result Demonstrates improved optimization performance with multi-fidelity Gaussian processes.
Quantum hybrid vision transformers improve event classification in high energy physics.
problem Excessive computational resources for training and deploying vision transformer models.
method Constructed quantum hybrid vision transformers for high energy physics event classification.
result Quantum hybrid models achieve comparable performance to classical models with fewer parameters.
Active learning method for neural population dynamics using optogenetics.
problem Efficiently selecting neurons to stimulate for identifying neural population dynamics.
method Developed active learning procedure for low-rank regression to determine informative photostimulation patterns.
result Demonstrated a two-fold reduction in data required for predictive power using low-rank linear dynamical systems model.
The paper extends IPC framework to stationary physical systems and validates it with a photonic system.
problem Characterizing the computational capabilities of stationary physical systems in a principled, data-efficient way.
method Extended IPC framework, established fundamental results, derived asymptotic bias, introduced data-efficient estimation methods.
result IPC strongly correlates with machine-learning performance and provides a reliable estimate of system dimensionality.
Deep learning improves breast cancer detection in DOT.
problem Complex physics and ill-posedness in DOT reconstruction.
method Deep learning approach that learns non-linear photon scattering physics.
result Deep neural network accurately recovers optical anomalies.
New model enhances SPIM for solving low-rank combinatorial optimization and statistical learning problems.
problem Solving large-scale combinatorial optimization problems efficiently.
method Proposed a new computing model for SPIM that can handle low-rank interaction matrices.
result Demonstrated efficient learning, classification, and sampling of MNIST images using the model.
Study maximal representations of surface groups via pleated surfaces in pseudo-Riemannian space.
problem Maximal representations of surface groups and their geometric properties.
method Introduction of ρ-invariant pleated surfaces and construction of shear cocycles. result Properties of ρ-invariant pleated surfaces, including embeddedness, acausality, and hyperbolic structure. Improved analysis of calcium imaging data with higher SNR and compression.
problem Challenges in analyzing large-scale calcium imaging datasets.
method Spatially-localized penalized matrix decomposition (PMD) for denoising and compression.
result Significant improvement in SNR and compression rates with minimal signal loss.
The paper develops methods for monitoring TPL machine health.
problem Inaccurate and untimely maintenance of TPL systems leads to poor quality and inefficiencies.
method Physics-informed data-driven predictive models integrated with statistical approaches.
result The methods achieve high accuracy across various scenarios and conditions.
oPoW proposes a new PoW algorithm to reduce mining costs and environmental impact.
problem Scalability issues, environmental concerns, and systemic risks in Bitcoin PoW.
method oPoW is a novel PoW algorithm that shifts mining costs from electricity to hardware (CAPEX).
result oPoW reduces mining costs and improves network scalability, decentralization, and issuance.
The paper solves the isoperimetric problem in Riemannian optical geometry, proving circles minimize lengths with area constraints.
problem Optical geometry of static spherically symmetric spacetimes.
method Applying isoperimetric problem results to curves in Riemannian optical geometry.
result Length-minimizing curves with area constraints are circles, with implications for photon spheres.
Study null energy condition impacts on special hypersurfaces in static spacetimes.
problem Effects of null energy condition on totally umbilic hypersurfaces.
method Characterization of embedded surfaces and photon surfaces using Alexandrov Theorem and other methods.
result Full characterization of embedded surfaces with constant spacetime mean curvature.
Optical co-processor speeds up neural network training.
problem Expensive training costs for large neural networks.
method Direct feedback alignment, optical error projection.
result Optical co-processor trains neural networks for handwritten digit recognition.
Combines spatial context priors with data term for better dendritic spine segmentation.
problem Poor segmentation results due to overlapping pixel intensity distributions.
method Combines nonparametric context priors with learned-intensity data term and nonparametric shape priors.
result Significant improvements in dendritic spine segmentation.
Researchers use statistical methods to infer transmission matrices in complex media.
problem Comprehending and exploiting photon scattering through disordered media.
method Pseudolikelihood decimation to learn the coupling matrix via random sampling.
result Transmission matrices can be inferred and used like normal optical elements.
This article is written for the online newspaper "The Photon" published by the Department of Physics, University of Maryland. The article describes econophysics research done in the group of Victor Yakovenko. It briefly surveys the subjects "Statistical Mechanics of Money, Income, and Wealth" and "Probability Distribut…