Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search

The advent of noisy intermediate-scale quantum (NISQ) devices provide crucial promise for the development of quantum algorithms. Variational quantum algorithms have emerged as one of the best hopes to utilize NISQ devices. Among these is the famous variational quantum eigensolver (VQE), where one tr...

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Autores principales:	Fan-Xu Meng, Ze-Tong Li, Xu-Tao Yu, Zai-Chen Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Monto Carlo tree (MCT) quantum architecture optimization supernet variational quantum eigensolver (VQE) Atomic physics. Constitution and properties of matter QC170-197 Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/453e4bbf16d241e1ab43d905e68c6c27
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spelling	oai:doaj.org-article:453e4bbf16d241e1ab43d905e68c6c272021-11-04T23:01:00ZQuantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search2689-180810.1109/TQE.2021.3119010https://doaj.org/article/453e4bbf16d241e1ab43d905e68c6c272021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9566740/https://doaj.org/toc/2689-1808The advent of noisy intermediate-scale quantum (NISQ) devices provide crucial promise for the development of quantum algorithms. Variational quantum algorithms have emerged as one of the best hopes to utilize NISQ devices. Among these is the famous variational quantum eigensolver (VQE), where one trains a parameterized and fixed quantum circuit (or an ansatz) to accomplish the task. However, VQE also suffers from some serious challenges, which are training difficulty and accuracy reduction due to deep quantum circuit and hardware noise. Motivated by these issues, we propose a runtime and resource-efficient scheme, Monto Carlo tree (MCT) search-based quantum circuit architecture optimization, where the ansatz is built in the variable form. Our approach first models the search space with a MCT and regards it as a supernet, where we make use of layers dependence to reduce the size of the search space. Second, a two-stage scheme is proposed for the search space training, where weight sharing and warm-up strategies are employed to avoid huge computation cost. Training results are stored in nodes of the MCT for future decisions, and hierarchical node selection is presented to obtain an optimal ansatz. As a proof of principle, we carry out a series of numerical experiments for condensed matter and quantum chemistry in a quantum simulator with and without noise. Consequently, our scheme can be efficient to mitigate trainability and accuracy issues by minimizing the ansatz depth and the number of entanglement gates.Fan-Xu MengZe-Tong LiXu-Tao YuZai-Chen ZhangIEEEarticleMonto Carlo tree (MCT)quantum architecture optimizationsupernetvariational quantum eigensolver (VQE)Atomic physics. Constitution and properties of matterQC170-197Materials of engineering and construction. Mechanics of materialsTA401-492ENIEEE Transactions on Quantum Engineering, Vol 2, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Monto Carlo tree (MCT) quantum architecture optimization supernet variational quantum eigensolver (VQE) Atomic physics. Constitution and properties of matter QC170-197 Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Monto Carlo tree (MCT) quantum architecture optimization supernet variational quantum eigensolver (VQE) Atomic physics. Constitution and properties of matter QC170-197 Materials of engineering and construction. Mechanics of materials TA401-492 Fan-Xu Meng Ze-Tong Li Xu-Tao Yu Zai-Chen Zhang Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
description	The advent of noisy intermediate-scale quantum (NISQ) devices provide crucial promise for the development of quantum algorithms. Variational quantum algorithms have emerged as one of the best hopes to utilize NISQ devices. Among these is the famous variational quantum eigensolver (VQE), where one trains a parameterized and fixed quantum circuit (or an ansatz) to accomplish the task. However, VQE also suffers from some serious challenges, which are training difficulty and accuracy reduction due to deep quantum circuit and hardware noise. Motivated by these issues, we propose a runtime and resource-efficient scheme, Monto Carlo tree (MCT) search-based quantum circuit architecture optimization, where the ansatz is built in the variable form. Our approach first models the search space with a MCT and regards it as a supernet, where we make use of layers dependence to reduce the size of the search space. Second, a two-stage scheme is proposed for the search space training, where weight sharing and warm-up strategies are employed to avoid huge computation cost. Training results are stored in nodes of the MCT for future decisions, and hierarchical node selection is presented to obtain an optimal ansatz. As a proof of principle, we carry out a series of numerical experiments for condensed matter and quantum chemistry in a quantum simulator with and without noise. Consequently, our scheme can be efficient to mitigate trainability and accuracy issues by minimizing the ansatz depth and the number of entanglement gates.
format	article
author	Fan-Xu Meng Ze-Tong Li Xu-Tao Yu Zai-Chen Zhang
author_facet	Fan-Xu Meng Ze-Tong Li Xu-Tao Yu Zai-Chen Zhang
author_sort	Fan-Xu Meng
title	Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
title_short	Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
title_full	Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
title_fullStr	Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
title_full_unstemmed	Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search
title_sort	quantum circuit architecture optimization for variational quantum eigensolver via monto carlo tree search
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/453e4bbf16d241e1ab43d905e68c6c27
work_keys_str_mv	AT fanxumeng quantumcircuitarchitectureoptimizationforvariationalquantumeigensolverviamontocarlotreesearch AT zetongli quantumcircuitarchitectureoptimizationforvariationalquantumeigensolverviamontocarlotreesearch AT xutaoyu quantumcircuitarchitectureoptimizationforvariationalquantumeigensolverviamontocarlotreesearch AT zaichenzhang quantumcircuitarchitectureoptimizationforvariationalquantumeigensolverviamontocarlotreesearch
_version_	1718444587228332032

Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search

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