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Enhancing VQE Convergence for Optimization problems with problem-specific parameterized quantum circuits

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2023年第11期E106D卷 1772-1782页

作者： Matsuo, Atsushi Suzuki, Yudai Hamamura, Ikko Yamashita, Shigeru IBM Res IBM Quantum Tokyo 1038510 Japan Ritsumeikan Univ Kusatsu 5258577 Japan Keio Univ Yokohama 2238521 Japan

The Variational quantum Eigensolver (VQE) algorithm is gaining interest for its potential use in near-term quantum devices. In the VQE algorithm, parameterized quantum circuits (PQCs) are employed to prepare quantum states, which are then utilized to compute the expectation value of a given Hamiltonian. Designing efficient PQCs is crucial for improving convergence speed. In this study, we introduce problem-specific PQCs tailored for optimization problems by dynamically generating PQCs that incorporate problem constraints. This approach reduces a search space by focusing on unitary transformations that benefit the VQE algorithm, and accelerate convergence. Our experimental results demonstrate that the convergence speed of our proposed PQCs outperforms state-of-the-art PQCs, highlighting the potential of problem-specific PQCs in optimization problems.

关键词： key VQE algorithm optimization problem problem-specific parameterized quantum circuit

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Variational quantum Eigensolver and Its Applications 13th

Variational Quantum Eigensolver and Its Applications

引用

13th International Conference on Reversible Computation (RC)

作者： Matsuo, Atsushi IBM Quantum IBM Research - Tokyo Tokyo 103-8510 Japan Graduate School of Information Science and Engineering Ritsumeikan University Shiga 525-8577 Japan

ISBN: (纸本)9783030798369;9783030798376

The Variational quantum Eigensolver (VQE) algorithm is attracting much attention to utilize current limited quantum devices. The VQE algorithm requires a quantum circuit with parameters, called a parameterized quantum circuit (PQC), to prepare a quantum state, and the quantum state is used to calculate the expectation value of a given Hamiltonian. Creating sophisticated PQCs is important from the perspective of the convergence speed. Thus, we propose problem-specific PQCs of the VQE algorithm for optimization problems. Our idea is to dynamically create a PQC that reflects the constraints of an optimization problem. With a problem-specific PQC, it is possible to reduce a search space by restricting unitary transformations in favor of the VQE algorithm. As a result, we can speed up the convergence of the VQE algorithm. Experimental results show that the convergence speed of the proposed PQCs is significantly faster than that of the state-of-the-art PQC.

关键词： VQE algorithm Optimization problem problem-specific parameterized quantum circuit

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