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Genetic and Evolutionary Computation Conference Companion (GECCO Companion)

Benchmarks for Digital Annealer with Quadratic Constrained Binary Optimization Problems

Hiroaki Kameyama, Yutaka Takita, Matthieu Parizy, Shinichi Sazawa, Pablo Caballero

Published Jul 14, 2024• 2 citations
Publisher

Abstract

In this paper, we evaluate the performance of Fujitsu's Digital Annealer, an original computing technology inspired by quantum phenomena and developed to solve practical combinatorial optimization problems, using the public benchmark QPLIB. As a result of the performance evaluation, it was confirmed that the best published solution was obtained in more than 90% of the instances at high speed.

DOI
10.1145/3638530.3664114

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In this paper, we evaluate the performance of Fujitsu's Digital Annealer, an original computing technology inspired by quantum phenomena and developed to solve practical combinatorial optimization problems, using the public benchmark QPLIB. As a result of the performance evaluation, it was confirmed that the best published solution was obtained in more than 90% of the instances at high speed.
Abstract Quadratic unconstrained binary optimization (QUBO) formulations of combinatorial optimization problems allow for solving them using various quantum heuristics. While large-scale quantum computations are currently still out of reach, we can already numerically test such QUBO formulations on a perhaps surprisingly large scale. In this work, we benchmark Fujitsu’s Digital Annealer (DA) on the Max-Cut problem, which captures the main complexity of the QUBO problem. We make a comprehensive benchmark against leading other heuristic algorithms on graphs with up to 53,000 variables by focusing on the wall-clock time. Moreover, we compare the DA performance against published performance results of the D-Wave hybrid quantum–classical annealer and the recently proposed QIS3 heuristic. Based on performance statistics for over 2000 graphs from the MQLib, we find that the DA yields competitive results. We hope that this benchmark demonstrates the extent to which large QUBO instances can be heuristically solved today, yielding consistent results across different solvers.
This paper presents a digital annealing engine capable of high-speed solving the Binary Quadratic Programming Problem (BQP) with equality and inequality constraints. While many Quadratic Unconstrained Binary Optimization (QUBO) solvers have been developed for combinatorial optimization problems, they are specialized for “unconstrained” BQP and struggle to solve “constrained” BQP at high speed. To address this issue, we developed a digital annealing engine on GPUs that leverages constraints to narrow the search space and directly evaluates inequality violations during Markov Chain Monte Carlo based searches. We evaluated the performance of the Digital Annealer (DA) system with the developed engine on benchmark problems with one-hot (a type of equality constraint) and inequality constraints. The results showed that the developed DA system achieved superior solving performance compared to the previous generation of DA. It also showed comparable performance to state-of-the-art dedicated solvers. Our results suggest that this digital annealing engine is a promising solution for high-speed solving constrained BQP.
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.
The Quadratic Assignment Problem is a a classical constrained optimization problem used to model many real-life applications. We present experiments in solving the Quadratic Assignment Problem by means of Quantum Annealing and Quantum-inspired Annealing. We describe how to model this classical combinatorial problem in terms of QUBO (Quadratic Unconstrained Binary optimization) for implementing it on hardware solvers based on quantum or quantum-inspired annealing (D-Wave, Fujitsu Digital Annealing Unit and Fixstars Amplify Annealing Engine). We present performance result for these implementations and compare them with well established metaheuristic solvers on classical hardware, such as Robust Tabu Search and External Optimization.
QUBO formulations of combinatorial optimization problems allow for solving them using various quantum heuristics. While large-scale quantum computations are currently still out of reach, we can already numerically test such QUBO formulations on a perhaps surprisingly large scale. In this work, we benchmark Fujitsu’s Digital Annealer (DA) on the Max-Cut problem, which captures the main complexity of the QUBO problem. We make a comprehensive benchmark against leading other heuristic algorithms on graphs with up to 53,000 variables by focusing on the wall-clock time. Moreover, we compare the DA performance against published performance results of the D-Wave hybrid quantum-classical annealer and the recently proposed QIS3 heuristic. Based on performance statistics for over 2,000 graphs from the MQLib, we find that the DA yields competitive results. We hope that this benchmark demonstrates the extent to which large QUBO instances can be heuristically solved today, yielding consistent results across different solvers.
Annealers leverage quadratic unconstrained binary optimization (QUBO) formulas to address combinatorial optimization problems (COPs) and have shown potential to outperform classical computers. This paper examines three prominent types of annealers: quantum, digital, and GPU annealers. Quantum annealers (QAs) are exemplified by the D-Wave Advantage, which relies on the quantum tunneling phenomenon to rapidly locate the minimum-energy system state corresponding to the optimal solution to a COP. Digital annealers (DAs) are typified by the Fujitsu Digital Annealing Unit (DAU), which is based on a quantum-inspired digital architecture to perform parallel and real-time optimization calculations to solve a COP. GPU annealers (GPUAs) are exemplified by the Compal Quantix solver, which harnesses graphics processing units (GPUs) to conduct the diverse adaptive bulk search for the optimal COP solution. This paper first provides an introductory overview of the QA, DA, and GPUA, and then proceeds to benchmark their performance on solving various well-known COPs such as the subset sum, maximum cut, vertex cover, 0/1 knapsack, graph coloring, Hamiltonian cycle, traveling salesperson, and job-shop scheduling problems. Their performance is also compared with that of state-of-the-art algorithms running on classical computers. Through comprehensive performance benchmarks in terms of the solution quality and the execution time, we identify the strengths and weaknesses of each annealer. In addition, we also provide recommendations to improve the performance of each annealer. Specifically, we recommend using the genetic algorithm, the improved particle swarm optimization algorithm, and the ant colony optimization algorithm in all annealers, using quantum pausing, quenching, and reverse annealing in the QA, using built-in separated penalty terms, one-way/two-way one-hot constraints, and linear inequality constraints in the DA, and implementing some parallel algorithms in the GPUA for performance improvement.