arXiv subjects

Kosuke Tatsumura

Publications and source records attributed to Kosuke Tatsumura.

13 recordsLinked to original sources

arXiv2026

Co-occurrence-Aware Quadratic Assignment for Local Feature Matching in Simultaneous Localization and Mapping

Local feature matching, which associates keypoints in two images as keypoint pairs, is fundamental to Visual Simultaneous Localization and Mapping (Visual SLAM). Nearest Neighbor (NN) search is commonly used for keypoint matching, but it has difficulty selecting correct keypoint pairs when multiple candidates have similar costs. To improve matching accuracy, this paper proposes a keypoint matching method that considers the pairwise co-occurrence of two keypoint pairs. The keypoint matching is formulated as a quadratic assignment problem, which is an NP-hard combinatorial optimization problem, making it difficult to solve quickly on conventional computers. Recently, Ising machines have been developed as computing devices capable of solving hard combinatorial optimization problems. Using a simulated bifurcation based Ising machine, the proposed method improved matching accuracy by approximately 8 percentage points over a conventional method on the HPatches dataset. Furthermore, we integrated the proposed method into ORB-SLAM3, a representative academic Visual SLAM system, and achieved a 3.78-fold improvement in absolute pose error (APE) and a 2.85-fold improvement in relative pose error (RPE) on the KITTI dataset scenes where multiple same shape objects are repeatedly arranged, which are challenging for accurate self-pose estimation by the original ORB-SLAM3.

cs.ET↗

arXiv2026

Decomposition of Multi-Qubit Gates for Circuit Cutting

A large-scale quantum circuit can be partitioned into multiple subcircuits through circuit cutting, where each subcircuit is executed multiple times and the expectation value of the original circuit is reconstructed by classical post-processing from their measurement (sampling) results. In this process, appropriate cut locations are identified after the user-designed quantum circuit, including multi-qubit gates that act on three or more qubits, has been decomposed into single-qubit gates and two-qubit gates such as the CNOT gate. Here, we present a method for reducing the sampling overhead, which refers to the increase in the number of samples required due to the cutting process, by modifying the decomposition strategy of multi-qubit gates. Using MCX and CCCX gates as representatives of multi-qubit gates, we demonstrate that the proposed decomposition method, which introduces a small number of ancilla qubits according to the identified cut locations, effectively decreases the sampling overhead.

quant-ph↗

arXiv2026

Edge-of-chaos enhanced quantum-inspired algorithm for combinatorial optimization

Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables. Numerical simulations of them are also useful as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accurate than conventional approaches with discrete variables such as simulated annealing. To improve the solution accuracy of a quantum-inspired algorithm called simulated bifurcation (SB), which was found from classical simulation of a quantum nonlinear oscillator network exhibiting quantum bifurcation, here we generalize it by introducing nonlinear control of individual bifurcation parameters and show that the generalized SB (GSB) can achieve surprisingly high performance, namely, almost 100% success probabilities for some large-scale problems. As a result, the time to solution for a 2,000-variable problem is shortened to 10 ms by a GSB-based machine, which is two orders of magnitude shorter than the best known value, 1.3 s, previously obtained by an SB-based machine. To examine the reason for the ultrahigh performance, we investigated chaos in the GSB changing the nonlinear-control strength and found that the dramatic increase of success probabilities happens near the edge of chaos. That is, the GSB can find a solution with high probability by harnessing the edge of chaos. This finding suggests that dynamical-system approaches to combinatorial optimization will be enhanced by harnessing the edge of chaos, opening a broad possibility for physics-inspired approaches to combinatorial optimization.

quant-ph↗

arXiv2025

Collaborative filtering based on nonnegative/binary matrix factorization

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix factorization techniques such as nonnegative matrix factorization (NMF) are often employed. Nonnegative/binary matrix factorization (NBMF), which is an extension of NMF, approximates a nonnegative matrix as the product of nonnegative and binary matrices. While previous studies have applied NBMF primarily to dense data such as images, this paper proposes a modified NBMF algorithm tailored for collaborative filtering with sparse data. In the modified method, unrated entries in the rating matrix are masked, enhancing prediction accuracy. Furthermore, utilizing a low-latency Ising machine in NBMF is advantageous in terms of the computation time, making the proposed method beneficial.

cond-mat.stat-mech↗

arXiv2025

Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines

Many real-time systems require the optimization of discrete variables. Black-box optimization (BBO) algorithms and multi-armed bandit (MAB) algorithms perform optimization by repeatedly taking actions and observing the corresponding instant rewards without any prior knowledge. Recently, a BBO method using an Ising machine has been proposed to find the best action that is represented by a combination of discrete values and maximizes the instant reward in static environments. In contrast, dynamic environments, where real-time systems operate, necessitate MAB algorithms that maximize the average reward over multiple trials. However, due to the enormous number of actions resulting from the combinatorial nature of discrete optimization, conventional MAB algorithms cannot effectively optimize dynamic, discrete environments. Here, we show a heuristic MAB method for dynamic, discrete environments by extending the BBO method, in which an Ising machine effectively explores the actions while considering interactions between variables and changes in dynamic environments. We demonstrate the dynamic adaptability of the proposed method in a wireless communication system with moving users.

cs.AI↗

arXiv2025

Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems

Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop networks and financial trading, require solving those problems sequentially where the size and characteristics change dynamically. However, using Ising machines involves challenges to shorten system-wide latency due to the transfer of large Ising model or the cloud access and to determine the parameters for each problem. Here we show a combinatorial optimization method using embedded Ising machines, which enables solving diverse problems at high speed without runtime parameter tuning. We customize the algorithm and circuit architecture of the simulated bifurcation-based Ising machine to compress the Ising model and accelerate computation and then built a machine learning model to estimate appropriate parameters using extensive training data. In TDMA scheduling for wireless multi-hop networks, our demonstration has shown that the sophisticated system can adapt to changes in the problem and showed that it has a speed advantage over conventional methods.

cs.ET↗

arXiv2024

Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines

A cognitive function of tracking multiple objects, needed in autonomous mobile vehicles, comprises object detection and their temporal association. While great progress owing to machine learning has been recently seen for elaborating the similarity matrix between the objects that have been recognized and the objects detected in a current video frame, less for the assignment problem that finally determines the temporal association, which is a combinatorial optimization problem. Here we show an in-vehicle multiple object tracking system with a flexible assignment function for tracking through multiple long-term occlusion events. To solve the flexible assignment problem formulated as a nondeterministic polynomial time-hard problem, the system relies on an embeddable Ising machine based on a quantum-inspired algorithm called simulated bifurcation. Using a vehicle-mountable computing platform, we demonstrate a realtime system-wide throughput (23 frames per second on average) with the enhanced functionality.

cs.CV↗

arXiv2024

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET↗

arXiv2023

Efficient and Scalable Architecture for Multiple-chip Implementation of Simulated Bifurcation Machines

Ising machines are specialized computers for finding the lowest energy states of Ising spin models, onto which many practical combinatorial optimization problems can be mapped. Simulated bifurcation (SB) is a quantum-inspired parallelizable algorithm for Ising problems that enables scalable multi-chip implementations of Ising machines. However, the computational performance of a previously proposed multi-chip architecture tends to saturate as the number of chips increases for a given problem size because both computation and communication are exclusive in the time domain. In this paper, we propose a streaming architecture for multi-chip implementations of SB-based Ising machines with full spin-to-spin connectivity. The data flow in in-chip computation is harmonized with the data flow in inter-chip communication, enabling the computation and communication to overlap and the communication time to be hidden. Systematic experiments demonstrate linear strong scaling of performance up to the vicinity of the ideal communication limit determined only by the latency of chip-to-chip communication. Our eight-FPGA (field-programmable gate array) cluster can compute a 32,768-spin problem with a high pipeline efficiency of 97.9%. The performance of a 79-FPGA cluster for a 100,000-spin problem, projected using a theoretical performance model validated on smaller experimental clusters, is comparable to that of a state-of-the-art 100,000-spin optical Ising machine.

cs.ET↗

arXiv2023

Correlation-diversified portfolio construction by finding maximum independent set in large-scale market graph

Correlation-diversified portfolios can be constructed by finding the maximum independent sets (MISs) in market graphs with edges corresponding to correlations between two stocks. The computational complexity to find the MIS increases exponentially as the size of the market graph increases, making the MIS selection in a large-scale market graph difficult. Here we construct a diversified portfolio by solving the MIS problem for a large-scale market graph with a combinatorial optimization solver (an Ising machine) based on a quantum-inspired algorithm called simulated bifurcation (SB) and investigate the investment performance of the constructed portfolio using long-term historical market data. Comparisons using stock universes of various sizes [TOPIX 100, Nikkei 225, TOPIX 1000, and TOPIX (including approximately 2,000 constituents)] show that the SB-based solver outperforms conventional MIS solvers in terms of computation-time and solution-accuracy. By using the SB-based solver, we optimized the parameters of a MIS portfolio strategy through iteration of the backcast simulation that calculates the performance of the MIS portfolio strategy based on a large-scale universe covering more than 1,700 Japanese stocks for a long period of 10 years. It has been found that the best MIS portfolio strategy (Sharpe ratio = 1.16, annualized return/risk = 16.3%/14.0%) outperforms the major indices such as TOPIX (0.66, 10.0%/15.2%) and MSCI Japan Minimum Volatility Index (0.64, 7.7%/12.1%) for the period from 2013 to 2023.

cs.ET↗

arXiv2023

Pairs-trading System using Quantum-inspired Combinatorial Optimization Accelerator for Optimal Path Search in Market Graphs

Pairs-trading is a trading strategy that involves matching a long position with a short position in two stocks aiming at market-neutral profits. While a typical pairs-trading system monitors the prices of two statistically correlated stocks for detecting a temporary divergence, monitoring and analyzing the prices of more stocks would potentially lead to finding more trading opportunities. Here we report a stock pairs-trading system that finds trading opportunities for any two stocks in an $N$-stock universe using a combinatorial optimization accelerator based on a quantum-inspired algorithm called simulated bifurcation. The trading opportunities are detected through solving an optimal path search problem in an $N$-node directed graph with edge weights corresponding to the products of instantaneous price differences and statistical correlation factors between two stocks. The accelerator is one of Ising machines and operates consecutively to find multiple opportunities in a market situation with avoiding duplicate detections by a tabu search technique. It has been demonstrated in the Tokyo Stock Exchange that the FPGA (field-programmable gate array)-based trading system has a sufficiently low latency (33 $μ$s for $N$=15 or 210 pairs) to execute the pairs-trading strategy based on optimal path search in market graphs.

cs.ET↗

arXiv2023

Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization

Financial portfolio construction problems are often formulated as quadratic and discrete (combinatorial) optimization that belong to the nondeterministic polynomial time (NP)-hard class in computational complexity theory. Ising machines are hardware devices that work in quantum-mechanical/quantum-inspired principles for quickly solving NP-hard optimization problems, which potentially enable making trading decisions based on NP-hard optimization in the time constraints for high-speed trading strategies. Here we report a real-time stock trading system that determines long(buying)/short(selling) positions through NP-hard portfolio optimization for improving the Sharpe ratio using an embedded Ising machine based on a quantum-inspired algorithm called simulated bifurcation. The Ising machine selects a balanced (delta-neutral) group of stocks from an $N$-stock universe according to an objective function involving maximizing instantaneous expected returns defined as deviations from volume-weighted average prices and minimizing the summation of statistical correlation factors (for diversification). It has been demonstrated in the Tokyo Stock Exchange that the trading strategy based on NP-hard portfolio optimization for $N$=128 is executable with the FPGA (field-programmable gate array)-based trading system with a response latency of 164 $μ$s.

cs.ET↗

arXiv2008

Diffusion and activation of n-type dopants in germanium

The diffusion and activation of $n$-type impurities (P and As) implanted into $p$-type Ge(100) substrates were examined under various dose and annealing conditions. The secondary ion mass spectrometry profiles of chemical concentrations indicated the existence of a sufficiently high number of impurities with increasing implanted doses. However, spreading resistance probe profiles of electrical concentrations showed electrical concentration saturation in spite of increasing doses and indicated poor activation of As relative to P in Ge. The relationships between the chemical and electrical concentrations of P in Ge and Si were calculated, taking into account the effect of incomplete ionization. The results indicated that the activation of P was almost the same in Ge and Si. The activation ratios obtained experimentally were similar to the calculated values, implying insufficient degeneration of Ge. The profiles of P in Ge substrates with and without damage generated by Ge ion implantation were compared, and it was clarified that the damage that may compensate the activated $n$-type dopants has no relationship with the activation of P in Ge.

cond-mat.mtrl-sci↗