The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
VQE(Variational Quantum Eigensolver) 알고리즘은 단기 양자 장치에서의 잠재적인 사용으로 관심을 끌고 있습니다. VQE 알고리즘에서는 매개변수화된 양자 회로(PQC)를 사용하여 양자 상태를 준비한 다음 주어진 해밀턴의 기대값을 계산하는 데 사용합니다. 효율적인 PQC를 설계하는 것은 수렴 속도를 향상시키는 데 중요합니다. 본 연구에서는 문제 제약 조건을 포함하는 PQC를 동적으로 생성하여 최적화 문제에 맞는 문제별 PQC를 소개합니다. 이 접근 방식은 VQE 알고리즘에 도움이 되는 단일 변환에 중점을 두고 검색 공간을 줄이고 수렴을 가속화합니다. 우리의 실험 결과는 우리가 제안한 PQC의 수렴 속도가 최첨단 PQC보다 뛰어나다는 것을 보여 주며 최적화 문제에서 문제별 PQC의 잠재력을 강조합니다.
Atsushi MATSUO
IBM Research,Ritsumeikan University
Yudai SUZUKI
Keio University
Ikko HAMAMURA
IBM Research
Shigeru YAMASHITA
Ritsumeikan University
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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},}
부
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 -