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IEEE Global Conference on Consumer Electronics (GCCE)

Ising Machine-Based Similar Region Search Using Feature Histograms

Shohei Mori, Satoshi Nishimura

Published Oct 18, 2022• 0 citations
Publisher

Abstract

We propose a novel algorithm for finding a similar region in images using Ising machines, which are a new type of non-von Neumann computers that specialize in solving combinatorial optimization problems. Applications of Ising machine are expected to be promising in diverse fields such as media processing and delivery. To leverage Ising machine-based approaches, a problem must be formulated in a quadratic unconstrained binary optimization (QUBO) form. In this study, to unleash the potentials of Ising machines in image processing, we present a method for formulating a similar region search as a QUBO problem. Experiments show that the QUBO-based algorithm realizes the detection of similar regions in accordance with policies for the similarity between images and the clumpiness of detected regions.

DOI
10.1109/gcce56475.2022.10014240

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We propose a novel algorithm for finding a similar region in images using Ising machines, which are a new type of non-von Neumann computers that specialize in solving combinatorial optimization problems. Applications of Ising machine are expected to be promising in diverse fields such as media processing and delivery. To leverage Ising machine-based approaches, a problem must be formulated in a quadratic unconstrained binary optimization (QUBO) form. In this study, to unleash the potentials of Ising machines in image processing, we present a method for formulating a similar region search as a QUBO problem. Experiments show that the QUBO-based algorithm realizes the detection of similar regions in accordance with policies for the similarity between images and the clumpiness of detected regions.
Recently, Ising machines, which solve Quadratic Unconstrained Binary Optimization (QUBO) problems in a short computation time, have attracted attention. In this paper, we propose a mask optimization method using an Ising machine to obtain a mask with high fidelity to target patterns and high tolerance to process variation. In the proposed method, a mask pattern is improved by repeatedly solving QUBO problems by the Ising machine. In experiments, we applied the proposed method using the Ising machine to various patterns and evaluated it in comparison with existing methods in fidelity to target patterns, tolerance to process variation, and execution time.
In computer science, clustering is a technique for grouping data. Ising machines can solve distance-based clustering problems described by quadratic unconstrained binary optimization (QUBO) formulations. A typical simple method using an Ising machine makes each cluster size equal and is not suitable for clustering unevenly distributed data. We propose a new clustering method that provides better performance than the simple method, especially for unevenly distributed data. The proposed method is a hybrid algorithm including an iterative process that comprises solving a discrete optimization problem with an Ising machine and calculating parameters with a general-purpose computer. To minimize the communication overhead between the Ising machine and the general-purpose computer, we employed a low-latency Ising machine implementing the simulated bifurcation algorithm with a field-programmable gate array attached to a local server. The proposed method results in clustering 200 unevenly distributed data points with a clustering score 18% higher than that of the simple method. The discrete optimization with 2000 variables is performed 100 times per iteration, and the overhead time is reduced to approximately 20% of the total execution time. These results suggest that hybrid algorithms using Ising machines can efficiently solve practical optimization problems.
A combinatorial optimization problem is a problem finding an optimal combination of variables that maximizes or minimizes an objective function while satisfying given constraints. Such problems arise in various fields, including transportation and communication. Many combinatorial optimization problems are NP-hard, meaning that the number of possible combinations grows exponentially as the number of variables increases. As a result, it is often difficult to solve large-scale combinatorial optimization problems for conventional classical computers. Recently, Ising machines, including quantum annealers, have gained attention as a promising architecture for efficiently solving combinatorial optimization problems. One real-world example of a combinatorial optimization problem isuniversity course selection. Many students manually choose their courses, but this process is time consuming and labor intensive due to the large number of available options. To address this, in this paper, we formulate course selection as a combinatorial optimization problem, which we define as a Personalized Course Selection Problem (PCSP). To solve it efficiently, we use an Ising machine, a specialized computer designed for combinatorial optimization. We propose a Quadratic Unconstrained Binary Optimization (QUBO) formulation for solving PCSP and solve the problem with an Ising machine. Experimental evaluations demonstrate that, for the largest instance tested, the proposed method solves the problem 20X times faster than the conventional simulated annealing while achieving the same total course cost.
Quantum computing promises to have a tremendous advantage over its classical counterpart for solving computationally hard problems, yet remains in a relatively early stage for practical applications owing to the limited capabilities of today’s quantum computers. The approach to the special purposes of quantum computers by exploiting the special-purpose physics-inspired or quantum-inspired computers is emerging as a novel alternative to its quantum counterpart in tackling hard problems in high-performance computing. Inspired by physics, the Ising machine — a type of special-purpose computer that implements or emulates physics or quantum effects of the Ising model to speed up finding solutions to optimization problems — has recently become an active research area in the field of combinatorial optimization. This paper is to address the key enabling software and hardware technology underlying physics-inspired optimization using Ising machines in the unified quadratic unconstrained binary optimization (QUBO) framework for modeling and solving computationally hard combinatorial optimization problems, and with an aim to shed some light on the challenges and opportunities associated with the ever-growing landscape of this novel high-performance computing.
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