arXiv subjects

Yoshihisa Yamamoto

Publications and source records attributed to Yoshihisa Yamamoto.

At least 19 recordsLinked to original sources

arXiv2026

Photonic Ising machines toward and beyond a million spins

Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.

physics.optics↗

arXiv2026

Hybrid Quantum-Classical Optimization for Multi-Objective Supply Chain Logistics

A multi-objective logistics optimization problem from a real-world supply chain is formulated as a Quadratic Unconstrained Binary Optimization Problem (QUBO) that minimizes cost, emissions, and delivery time, while maintaining target distributions of supplier workshare. The model incorporates realistic constraints, including part dependencies, double sourcing, and multimodal transport. Two hybrid quantum-classical solvers are proposed: a structure-aware informed tree search (IQTS) and a modular bilevel framework (HBS), combining quantum subroutines with classical heuristics. Experimental results on IonQ's Aria-1 hardware demonstrate a methodology to map real-world logistics problems onto emerging combinatorial optimization-specialized hardware, yielding high-quality, Pareto-optimal solutions.

math.OC↗

arXiv2025

A Benchmarking Study of Quantum Algorithms for Combinatorial Optimization

We study the performance scaling of three quantum algorithms for combinatorial optimization: measurement-feedback coherent Ising machines (MFB-CIM), discrete adiabatic quantum computation (DAQC), and the Dürr-Hoyer algorithm for quantum minimum finding (DH-QMF) that is based on Grover's search. We use MaxCut problems as a reference for comparison, and time-to-solution (TTS) as a practical measure of performance for these optimization algorithms. For each algorithm, we analyze its performance in solving two types of MaxCut problems: weighted graph instances with randomly generated edge weights attaining 21 equidistant values from $-1$ to $1$; and randomly generated Sherrington-Kirkpatrick (SK) spin glass instances. We empirically find a significant performance advantage for the studied MFB-CIM in comparison to the other two algorithms. We empirically observe a sub-exponential scaling for the median TTS for the MFB-CIM, in comparison to the almost exponential scaling for DAQC and the proven $\widetilde{O}\left(\sqrt{2^n}\right)$ scaling for DH-QMF. We conclude that the MFB-CIM outperforms DAQC and DH-QMF in solving MaxCut problems.

quant-ph↗

arXiv2024

L0-regularized compressed sensing with Mean-field Coherent Ising Machines

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. As a practical application of CIM, Aonishi et al. proposed a quantum-classical hybrid system to solve optimization problems of L0-regularization-based compressed sensing (L0RBCS). Gunathilaka et al. has further enhanced the accuracy of the system. However, the computationally expensive CIM's stochastic differential equations (SDEs) limit the use of digital hardware implementations. As an alternative to Gunathilaka et al.'s CIM SDEs used previously, we propose using the mean-field CIM (MF-CIM) model, which is a physics-inspired heuristic solver without quantum noise. MF-CIM surmounts the high computational cost due to the simple nature of the differential equations (DEs). Furthermore, our results indicate that the proposed model has similar performance to physically accurate SDEs in both artificial and magnetic resonance imaging data, paving the way for implementing CIM-based L0RBCS on digital hardware such as Field Programmable Gate Arrays (FPGAs).

cs.ET↗

arXiv2024

Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine

In recent years, quantum Ising machines have drawn a lot of attention, but due to physical implementation constraints, it has been difficult to achieve dense coupling, such as full coupling with sufficient spins to handle practical large-scale applications. Consequently, classically computable equations have been derived from quantum master equations for these quantum Ising machines. Parallel implementations of these algorithms using FPGAs have been used to rapidly find solutions to these problems on a scale that is difficult to achieve in physical systems. We have developed an FPGA implemented cyber coherent Ising machine (cyber CIM) that is much more versatile than previous implementations using FPGAs. Our architecture is versatile since it can be applied to the open-loop CIM, which was proposed when CIM research began, to the closed-loop CIM, which has been used recently, as well as to Jacobi successive over-relaxation method. By modifying the sequence control code for the calculation control module, other algorithms such as Simulated Bifurcation (SB) can also be implemented. Earlier research on large-scale FPGA implementations of SB and CIM used binary or ternary discrete values for connections, whereas the cyber CIM used FP32 values. Also, the cyber CIM utilized Zeeman terms that were represented as FP32, which were not present in other large-scale FPGA systems. Our implementation with continuous interaction realizes N=4096 on a single FPGA, comparable to the single-FPGA implementation of SB with binary interactions, with N=4096. The cyber CIM enables applications such as CDMA multi-user detector and L0 compressed sensing which were not possible with earlier FPGA systems, while enabling superior calculation speeds, more than ten times faster than a GPU implementation. The calculation speed can be further improved by increasing parallelism, such as through clustering.

cs.AR↗

arXiv2024

Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization

We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algorithm dynamically adapts the shape of the smoothing kernel to approximate the Hessian of the objective function around a local optimum. This approach significantly reduces the error in estimating the gradient from noisy evaluations through sampling. We demonstrate the efficacy of our method through numerical experiments on artificial problems. Additionally, we show improved performance when tuning NP-hard combinatorial optimization solvers compared to existing state-of-the-art heuristic derivative-free and Bayesian optimization methods.

cs.LG↗

arXiv2024

Skew-Gaussian model of small-photon-number coherent Ising machines

A Gaussian quantum theory of bosonic modes has been widely used to describe quantum optical systems, including coherent Ising machines (CIMs) that consist of $χ^{(2)}$ degenerate optical parametric oscillators (DOPOs) as nonlinear elements. However, Gaussian models have been thought to be invalid in the extremely strong-gain-saturation limit. Here, we develop an extended Gaussian model including two third-order fluctuation products, $\langle δ\hat{X}^3\rangle$ and $\langle δ\hat{X}δ\hat{P}^2\rangle$, which we call self-skewness and cross-skewness, respectively. This new model which we call skew-Gaussian model more precisely replicates the success probability predicted by the quantum master equation (QME), relative to Gaussian models. We also discuss the impact of skew variables on the performance of CIMs.

quant-ph↗

arXiv2023

Mean-field Coherent Ising Machines with artificial Zeeman terms

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In CIMs, a problem arises when attempting to realize the Zeeman term because of the mismatch in size between interaction and Zeeman terms due to the variable amplitude of the optical parametric oscillator pulses corresponding to spins. There have been three approaches proposed so far to address this problem for CIM, including the absolute mean amplitude method, the auxiliary spin method, and the chaotic amplitude control (CAC) method. This paper focuses on the efficient implementation of Zeeman terms within the mean-field CIM model, which is a physics-inspired heuristic solver without quantum noise. With the mean-field model, computation is easier than with more physically accurate models, which makes it suitable for implementation in field programmable gate arrays (FPGAs) and large-scale simulations. Firstly, we examined the performance of the mean-field CIM model for realizing the Zeeman term with the CAC method, as well as their performance when compared to a more physically accurate model. Next, we compared the CAC method to other Zeeman term realization techniques on the mean-field model and a more physically accurate model. In both models, the CAC method outperformed the other methods while retaining similar performance.

quant-ph↗

arXiv2023

Hyperspectral In-Memory Computing with Optical Frequency Combs and Programmable Optical Memories

The rapid advancements in machine learning across numerous industries have amplified the demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing architectures. To address this, researchers are currently exploring alternatives such as in-memory computing systems to develop faster and more energy-efficient hardware. In particular, there is renewed interest in computing systems based on optics, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing a highly parallel, programmable, and scalable optical computing system capable of rivaling electronic computing hardware still remains elusive. In this context, we propose a hyperspectral in-memory computing architecture that integrates space multiplexing with frequency multiplexing of optical frequency combs and uses spatial light modulators as a programmable optical memory, thereby boosting the computational throughput and the energy efficiency. We have experimentally demonstrated multiply-accumulate operations with higher than 4-bit precision in both matrix-vector and matrix-matrix multiplications, which suggests the system's potential for a wide variety of deep learning and optimization tasks. This system exhibits extraordinary modularity, scalability, and programmability, effectively transcending the traditional limitations of optics-based computing architectures. Our approach demonstrates the potential to scale beyond peta operations per second, marking a significant step towards achieving high-throughput energy-efficient optical computing.

physics.optics↗

arXiv2023

Effective implementation of $l_0$-Regularised Compressed Sensing with Chaotic-Amplitude-Controlled Coherent Ising Machines

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that can solve large-scale combinatorial optimisation problems by finding the ground state of an Ising Hamiltonian. As a practical application of CIM, Aonishi et al., proposed a quantum-classical hybrid system to solve optimisation problems of $l_0$-regularisation-based compressed sensing. In the hybrid system, the CIM was an open-loop system without an amplitude control feedback loop. In this case, the hybrid system is enhanced by using a closed-loop CIM to achieve chaotic behaviour around the target amplitude, which would enable escaping from local minima in the energy landscape. Both artificial and magnetic resonance image data were used for the testing of our proposed closed-loop system. Compared with the open-loop system, the results of this study demonstrate an improved degree of accuracy and a wider range of effectiveness.

quant-ph↗

arXiv2022

Soliton generation in AlGaAs microresonators at room temperature

Chip-integrated optical frequency combs are attractive optical sources in comb applications requiring high-repetition-rate, low power consumption, or compact size. Spontaneous soliton formation via Kerr parametric oscillation is a promising generation principle in these frequency combs, and has been demonstrated in several material platforms over the past decade. Of these materials, AlGaAs has one of the largest Kerr nonlinearity coefficients allowing low pump threshold comb generation. However, bright soliton generation using this material has only been possible at cryogenic temperature because of the large thermo-optic effect at room temperature, which hinders stable access to the soliton regime. Here, we report self-stabilized single soliton generation in AlGaAs microresonators at room temperature by utilizing a rising soliton step in large free-spectral-range resonators. With sub-milliWatt optical pump power, 1 THz repetition-rate soliton generation is demonstrated. Perfect soliton crystal formation and soliton breather states are also observed. Besides the advantages of large optical nonlinearity, the devices are natural candidates for integration with III-V pump lasers.

physics.optics↗

arXiv2022

L0 regularization-based compressed sensing with quantum-classical hybrid approach

L0-regularization-based compressed sensing (L0-RBCS) has the potential to outperform L1-regularization-based compressed sensing (L1-RBCS), but the optimization in L0-RBCS is difficult because it is a combinatorial optimization problem. To perform optimization in L0-RBCS, we propose a quantum-classical hybrid system consisting of a quantum machine and a classical digital processor. The coherent Ising machine (CIM) is a suitable quantum machine for this system because this optimization problem can only be solved with a densely connected network. To evaluate the performance of the CIM-classical hybrid system theoretically, a truncated Wigner stochastic differential equation (W-SDE) is introduced as a model for the network of degenerate optical parametric oscillators, and macroscopic equations are derived by applying statistical mechanics to the W-SDE. We show that the system performance in principle approaches the theoretical limit of compressed sensing and this hybrid system may exceed the estimation accuracy of L1-RBCS in actual situations, such as in magnetic resonance imaging data analysis.

quant-ph↗

arXiv2022

Efficient sampling of ground and low-energy Ising spin configurations with a coherent Ising machine

We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model. We formulate a general discrete-time Gaussian-state model of the MFB-CIM which faithfully captures the nonlinear dynamics present at and above system threshold. This model overcomes the limitations of both mean-field models, which neglect quantum noise, and continuous-time models, which assume long photon lifetimes. Numerical simulations of our model show that when the MFB-CIM is operated in a quantum-noise-dominated regime with short photon lifetimes (i.e., low cavity finesse), homodyne monitoring of the system can efficiently produce samples of low-energy Ising spin configurations, requiring many fewer roundtrips to sample than suggested by established high-finesse, continuous-time models. We find that sampling performance is robust to, or even improved by, turning off or altogether reversing the sign of the parametric drive, but performance is critically reduced in the absence of optical nonlinearity. For the class of MAX-CUT problems with binary-signed edge weights, the number of roundtrips sufficient to fully sample all spin configurations up to the first-excited Ising energy, including all degeneracies, scales as $1.08^N$. At a problem size of $N = 100$ with a few dozen (median of 20) such desired configurations per instance, we have found median sufficient sampling times of $6\times10^6$ roundtrips; in an experimental implementation of an MFB-CIM with a 10 GHz repetition rate, this corresponds to a wall-clock sampling time of 60 ms.

quant-ph↗

arXiv2021

Coherent Ising Machines with Optical Error Correction Circuits

We propose a network of open-dissipative quantum oscillators with optical error correction circuits. In the proposed network, the squeezed/anti-squeezed vacuum states of the constituent optical parametric oscillators below the threshold establish quantum correlations through optical mutual coupling, while collective symmetry breaking is induced above the threshold as a decision-making process. This initial search process is followed by a chaotic solution search step facilitated by the optical error correction feedback. As an optical hardware technology, the proposed coherent Ising machine (CIM) has several unique features, such as programmable all-to-all Ising coupling in the optical domain, directional coupling $(J_{ij} \neq J_{ji})$ induced chaotic behavior, and low power operation at room temperature. We study the performance of the proposed CIMs and investigate how the performance scales with different problem sizes. The quantum theory of the proposed CIMs can be used as a heuristic algorithm and efficiently implemented on existing digital platforms. This particular algorithm is derived from the truncated Wigner stochastic differential equation. We find that the various CIMs discussed are effective at solving many problem types, however the optimal algorithm is different depending on the instance. We also find that the proposed optical implementations have the potential for low energy consumption when implemented optically on a thin film LiNbO3 platform.

quant-ph↗

arXiv2020

Noise correlation and success probability in coherent Ising machines

We compared the noise correlation and the success probability of coherent Ising machines (CIMs) with optical delay-line, measurement feedback, and mean-field couplings. We theoretically studied three metrics for the noise correlations in these CIMs: quantum entanglement, quantum discord, and normalized correlation of canonical coordinates. The success probability was obtained through numerical simulations of truncated stochastic differential equations based on the Wigner distribution function. The results indicate that the success probability is more directly related to the normalized correlation function rather than entanglement or quantum discord.

physics.optics↗

arXiv2020

Coherent Ising machines with error correction feedback

A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there are two challenges in the previously proposed approach: (1) The machine can be trapped by local minima which increases exponentially with problem size and (2) the machine fails to map a target Hamiltonian correctly on the loss landscape of a neural network due to oscillator amplitude heterogeneity. Both of them lead to erroneous solutions rather than correct answers. In this paper, we show that it is possible to overcome these two problems partially but simultaneously by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low-energy excited states via its inherent migration property during a solution search process.

physics.optics↗

arXiv2020

Entanglement and quantum discord in optically coupled coherent Ising machines

We present analytical and numerical simulation results for squeezing, entanglement, and quantum discord in a dissipatively coupled coherent Ising machine (CIM). Both analytical solutions and numerical simulation results, which are obtained with positive-P, truncated-Wigner and truncated-Husimi representations for the density operator, predict the presence of entanglement and quantum discord, below and above the threshold of CIM. The entanglement criteria and quantum discord are evaluated as a function of the dissipative coupling strength relative to the background loss. For coupled two DOPOs, while entanglement disappears as the background loss exceeds the Ising coupling strength, the quantum discord remains finite even with a large linear loss. For one-dimensional lattice of DOPOs, while entanglement disappears for DOPO pair with large distance, quantum discord remains finite.

quant-ph↗

arXiv2019

Steady-state squeezing and entanglement in a dissipatively coupled NOPO network

We investigate the steady-state photon-number squeezing and quantum entanglement in a network of nondegenerate optical parametric oscillators (NOPOs). We treat each NOPO with Shen's Raman laser model, whose lasing mode provides a photon-number-squeezed state. Two dissipatively coupled NOPOs satisfy Hillery-Zubairy's $HZ1$ entanglement criterion if they are pumped far above the threshold and the dissipative coupling is sufficiently larger than the NOPO cavity loss.

quant-ph↗