arXiv subjects

Suguru Shimomura

Publications and source records attributed to Suguru Shimomura.

5 recordsLinked to original sources

arXiv2026

Deterministic Ground-State Search in a Spatial Photonic Ising Machine by Phase Retrieval

A spatial photonic Ising machine (SPIM) solves large-scale combinatorial optimization problems by computing the Ising Hamiltonian through an optical Fourier transform. However, the ground-state search relies on the annealing process, in which spins are optimized stochastically and sequentially. We propose a ground-state search based on phase retrieval (PR), in which all spins are updated simultaneously and deterministically. By imposing an amplitude constraint in the Fourier plane, the modulated phase distribution corresponding to a spin configuration is guided toward the optimal solution. We numerically demonstrate that the proposed scheme reaches the ground state of rank-one Ising Hamiltonians with $10^4$ spins in a single iteration for all trials. Moreover, a radial rearrangement of the amplitude and the suitable design of the target pattern relaxed the search stagnation and promoted the optimization of spin configurations. The collective and deterministic spin update by phase retrieval provides a fast ground-state search for large-scale combinatorial optimization.

physics.optics↗

arXiv2025

Digital-twin imaging based on descattering Gaussian splatting

Three-dimensional imaging through scattering media is important in medical science and astronomy. We propose a digital-twin imaging method based on Gaussian splatting to observe an object behind a scattering medium. A digital twin model built through data assimilation, emulates the behavior of objects and environmental changes in a virtual space. By constructing a digital twin using point clouds composed of Gaussians and simulating the scattering process through the convolution of a point spread function, three-dimensional objects behind a scattering medium can be reproduced as a digital twin. In this study, a high-contrast digital twin reproducing a three-dimensional object was successfully constructed from degraded images, assuming that data were acquired from wavefronts disturbed by a scattering medium. This technique reproduces objects by integrating data processing with image measurements.

physics.optics↗

arXiv2025

Parallel spatial photonic Ising machine using spatial multiplexing for accelerating combinatorial optimization

A spatial photonic Ising machine (SPIM) handles large-scale combinatorial optimization problems owing to optical processing with spatial parallelism. However, iterative feedback in the search for optimal solutions limits processing speed even though the Ising Hamiltonian is computed optically. We propose a parallel spatial photonic Ising machine (pSPIM) utilizing spatial multiplexing to search for an optimal solution efficiently. By employing grating patterns and encoding multiple sets of Ising spins in a phase distribution, several Ising Hamiltonians are computed simultaneously. We demonstrated that Max-Cut problems requiring 100 Ising spins are solved faster as the number of processing units increases. In addition, combining the multicomponent model with parallel processing allows for efficient searching for optimal solutions to problems represented by using interaction matrixes with a rank greater than one. The pSPIM achieves high-speed searching of optimal solutions of large-scale combinatorial optimization problems.

physics.optics↗

arXiv2023

Spatial-photonic Ising machine by space-division multiplexing with physically tunable coefficients of a multi-component model

This paper proposes a space-division multiplexed spatial-photonic Ising machine (SDM-SPIM) that physically calculates the weighted sum of the Ising Hamiltonians for individual components in a multi-component model. Space-division multiplexing enables tuning a set of weight coefficients as an optical parameter and obtaining the desired Ising Hamiltonian at a time. We solved knapsack problems to verify the system's validity, demonstrating that optical parameters impact the search property. We also investigated a new dynamic coefficient search algorithm to enhance search performance. The SDM-SPIM would physically calculate the Hamiltonian and a part of the optimization with an electronics process.

physics.optics↗

arXiv2023

Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine

The spatial photonic Ising machine (SPIM) [D. Pierangeli et al., Phys. Rev. Lett. 122, 213902 (2019)] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.

cond-mat.dis-nn↗