arXiv subjects

Nikita Stroev

Publications and source records attributed to Nikita Stroev.

10 recordsLinked to original sources

arXiv2026

Percolation with coupled lasers: effect of non-linearities on the phase transition

Controlled experimental studies of percolation are challenging due to difficulties in tuning site connectivity, isolating local interactions, and mitigating finite-size effects. In this work, we experimentally investigate percolation with a platform of coupled lasers, where connectivity, interaction strength, and system size can be controlled. Using a square array of 100 lasers with astronomical number of possible cluster configurations, we show that the emergence of a percolating cluster corresponds to the onset of phase locking among the lasers. We also show that the percolation probability undergoes a second-order alike transition as a function of the site-occupation probability, with a threshold consistent with classical theoretical predictions. Surprisingly, we find that at low pump level, amplified mode competition (nonlinear regime) alters the effective behavior of the lasing sites and modify the nature of the percolation transition. The experimental results are interpreted by the means of a theoretical toy model with connectivity rules to the classical percolation.

physics.optics↗

arXiv2025

Programmable k-local Ising interactions and shallow optical Kolmogorov--Arnold networks through repeated data encounters

Photonic processors are naturally suited to linear transformations, but independently programmable higher-order interactions usually require nonlinear media or a reduction to pairwise models. We introduce a repeated-encounter architecture that combines linear optical propagation with square-law detection to evaluate sparse and structured $k$-local Ising objectives. Each hyperedge is routed to a resolved channel, returned through the spin-dependent mask, and reconstructed from calibrated encounter-order signals. Within the stated architecture class, a $k$-spin Walsh term requires at least $\lceil k/2\rceil$ data encounters. A reciprocal rank-one recollection attains this bound for every finite order, allowing hyperedge identity, interaction order, and signed coupling to be programmed independently without quadratization ancillas or material optical nonlinearities. We test the $R=2$, $k=4$ member in a finite discrete-Fourier model of an ideal folded $4f$ relay. Reciprocal recollection produces the four-body response, whereas a fixed-patch control does not; configuration-level calibration measures the leakage caused by finite windows. Under signed-amplitude encoding, the same encounter hierarchy spans polynomial edge functions for shallow optical Kolmogorov--Arnold networks. The arbitrary-$k$ result is analytic; the finite Fourier calculation tests its two-encounter member and does not replace experimental validation.

physics.optics↗

arXiv2024

Synchronization in coupled laser arrays with correlated and uncorrelated disorder

The effect of quenched disorder in a many-body system is experimentally investigated in a controlled fashion. It is done by measuring the phase synchronization (i.e. mutual coherence) of 400 coupled lasers as a function of tunable disorder and coupling strengths. The results reveal that correlated disorder has a non-trivial effect on the decrease of phase synchronization, which depends on the ratio of the disorder correlation length over the average number of synchronized lasers. The experimental results are supported by numerical simulations and analytic derivations.

physics.optics↗

arXiv2024

Efficient Computation Using Spatial-Photonic Ising Machines: Utilizing Low-Rank and Circulant Matrix Constraints

We explore the potential of spatial-photonic Ising machines (SPIMs) to address computationally intensive Ising problems that employ low-rank and circulant coupling matrices. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of low-rank approximation in optimization tasks, particularly in financial optimization, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimize the performance of these systems within these constraints.

physics.comp-ph↗

arXiv2024

Benchmarking the optimization optical machines with the planted solutions

We introduce universal, easy-to-reproduce generative models for the QUBO instances to differentiate the performance of the hardware/solvers effectively. Our benchmark process extends the well-known Hebb's rule of associative memory with the asymmetric pattern weights. We provide a comprehensive overview of calculations conducted across various scales and using different classes of dynamical equations. Our aim is to analyze their results, including factors such as the probability of encountering the ground state, planted state, spurious state, or states falling outside the predetermined energy range. Moreover, the generated problems show additional properties, such as the easy-hard-easy complexity transition and complicated cluster structures of planted solutions. Our method establishes a prospective platform to potentially address other questions related to the fundamental principles behind device physics and algorithms for novel computing machines.

stat.CO↗

arXiv2023

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.

physics.optics↗

arXiv2021

XY Neural Networks

The classical XY model is a lattice model of statistical mechanics notable for its universality in the rich hierarchy of the optical, laser and condensed matter systems. We show how to build complex structures for machine learning based on the XY model's nonlinear blocks. The final target is to reproduce the deep learning architectures, which can perform complicated tasks usually attributed to such architectures: speech recognition, visual processing, or other complex classification types with high quality. We developed the robust and transparent approach for the construction of such models, which has universal applicability (i.e. does not strongly connect to any particular physical system), allows many possible extensions while at the same time preserving the simplicity of the methodology.

physics.comp-ph↗

arXiv2021

Managing the Flow of Liquid Light

Strongly coupled light-matter systems can carry information over long distances and realize low threshold polariton lasing, condensation and superfluidity. These systems are highly non-equilibrium in nature, so constant nonzero fluxes manifest themselves even at the steady-state and are set by a complicated interplay between nonlinearity, dispersion, pumping, dissipation and interactions between the various constituents of the system. Based on the mean-field governing equations of lasers or polariton condensates, we develop a method for engineering and controlling the velocity profiles by manipulating the system's spatial pumping and dissipation. We present analytically exact pumping and dissipation profiles that lead to a large variety of spatially periodic density and velocity profiles. Besides these, any physically relevant velocity profiles can be engineered by finding the stationary state of the conservative nonlinear Schrodinger equation in an external potential related to the velocity. Our approach opens the way to the controllable implementation of laser or polariton flows for ultra-fast information processing, integrated circuits, and analogue simulators.

cond-mat.quant-gas↗

arXiv2020

Discrete Polynomial Optimization with Coherent Networks of Condensates and Complex Coupling Switching

Gain-dissipative platforms consisting of lasers, optical parametric oscillators and nonequilibrium condensates operating at the condensation/coherence threshold have been recently proposed as efficient analog simulators of 2-local spin Hamiltonians with continuous or discrete degrees of freedom. We show that nonequilibrium condensates above the threshold arranged in an interacting network may realise k-local Hamiltonians with k>2 and lead to nontrivial phase configurations. The principle of the operation of such a system lays the ground for physics-inspired computing and the new efficient methods for finding solutions to the higher order binary optimization problems. We show how to facilitate the search for the global solution by invoking complex couplings in the system and demonstrate the efficiency of the method on tensors with million entries. This approach offers a highly flexible new kind of computation based on gain-dissipative simulators with complex coupling switching. g.

cond-mat.dis-nn↗

arXiv2015

The simplest model for non-congruent fluid-fluid phase transition in Coulomb system

The simplest model for non-congruent phase transition of gas-liquid type was developed in frames of modified model with no associations of a binary ionic mixture (BIM) on a homogeneous compressible ideal background (or non-ideal) electron gas /BIM($\sim$)/. The analytical approximation for equation of state equation of state of Potekhin and Chabrier of fully ionized electron-ionic plasma was used for description of the ion-ion correlations (Coulomb non-ideality) in combination with ``linear mixture'' (LM) approximation. Phase equilibrium for the charged species was calculated according to the Gibbs-Guggenheim conditions. The presently considered BIM($\sim$) model allows to calculate full set of parameters for phase boundaries of non-congruent variant of phase equilibrium and to study all features for this non-congruent phase transition realization in Coulomb system in comparison with the simpler (standard) forced-congruent evaporation mode. In particular, in BIM($\sim$) there were reproduced two-dimensional remarkable (``banana-like'') structure of two-phase region $P-T$ diagram and the characteristic non-monotonic shape of caloric phase enthalpy-temperature diagram, similar to the non-congruent evaporation of reactive plasma products in high-temperature heating with the uranium-oxygen system. The parameters of critical points (CP) line were calculated on the entire range of proportions of ions $0<X<1$, including two reference values, when CP coincides with a point of extreme temperature and extreme pressure, $X_{T}$ and $X_{P}$. Finally, it is clearly demonstrated the low-temperature property of non-congruent gas--liquid transition---``distillation'', which is weak in chemically reactive plasmas.

physics.plasm-ph↗