IEICE TRANSACTIONS on Information
Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.11 pp.1772-1782
- Publication Date
- 2023/11/01
- Publicized
- 2023/08/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDP7071
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Atsushi MATSUO
IBM Research,Ritsumeikan University
Yudai SUZUKI
Keio University
Ikko HAMAMURA
IBM Research
Shigeru YAMASHITA
Ritsumeikan University
Keyword
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Atsushi MATSUO, Yudai SUZUKI, Ikko HAMAMURA, Shigeru YAMASHITA, "Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 11, pp. 1772-1782, November 2023, doi: 10.1587/transinf.2023EDP7071.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7071/_p
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@ARTICLE{e106-d_11_1772,
author={Atsushi MATSUO, Yudai SUZUKI, Ikko HAMAMURA, Shigeru YAMASHITA, },
journal={IEICE TRANSACTIONS on Information},
title={Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits},
year={2023},
volume={E106-D},
number={11},
pages={1772-1782},
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.},
keywords={},
doi={10.1587/transinf.2023EDP7071},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits
T2 - IEICE TRANSACTIONS on Information
SP - 1772
EP - 1782
AU - Atsushi MATSUO
AU - Yudai SUZUKI
AU - Ikko HAMAMURA
AU - Shigeru YAMASHITA
PY - 2023
DO - 10.1587/transinf.2023EDP7071
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2023
AB - 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.
ER -
English
