Solving Inequality-Constrained Binary Optimization Problems on Quantum Annealer

We propose a new method for solving binary optimization problems under inequality constraints using a quantum annealer. To deal with inequality constraints, we often use slack variables, as in previous approaches. When we use slack variables, we usually conduct a binary expansion, which requires num...

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Bibliographic Details
Published inarXiv.org
Main Authors Yonaga, Kouki, Miyama, Masamichi J, Ohzeki, Masayuki
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.12.2020
Subjects
Algorithms
Annealing
Computing time
Constraints
Inequality
Optimization
Qubits (quantum computing)
Sampling
Slack variables
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Summary:We propose a new method for solving binary optimization problems under inequality constraints using a quantum annealer. To deal with inequality constraints, we often use slack variables, as in previous approaches. When we use slack variables, we usually conduct a binary expansion, which requires numerous physical qubits. Therefore, the problem of the current quantum annealer is limited to a small scale. In this study, we employ the alternating direction method of multipliers. This approach allows us to deal with various types using constraints in the current quantum annealer without slack variables. To test the performance of our algorithm, we use quadratic knapsack problems (QKPs). We compared the accuracy obtained by our method with a simulated annealer and the optimization and sampling mode of a D-Wave machine. As a result of our experiments, we found that the sampling mode shows the best accuracy. We also found that the computational time of our method is faster than that of the exact solver when we tackle various QKPs defined on dense graphs.
ISSN:2331-8422