Enhancing VQE Convergence for Optimization Problems with Problem-specific Parameterized Quantum Circuits
Abstract
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.
Cite
@article{arxiv.2006.05643,
title = {Enhancing VQE Convergence for Optimization Problems with Problem-specific Parameterized Quantum Circuits},
author = {Atsushi Matsuo and Yudai Suzuki and Ikko Hamamura and Shigeru Yamashita},
journal= {arXiv preprint arXiv:2006.05643},
year = {2023}
}