Quantum circuit architecture search for variational quantum algorithms

Du, Y; Huang, T; You, S; Hsieh, M-H; Tao, D

Quantum circuit architecture search for variational quantum algorithms

Du, Y Huang, T You, S Hsieh, M-H Tao, D

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Publication Type:: Journal Article
Citation:: npj Quantum Information, 2020, 8, pp. 62
Issue Date:: 2020-10-20

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Field	Value	Language
dc.contributor.author	Du, Y
dc.contributor.author	Huang, T
dc.contributor.author	You, S
dc.contributor.author	Hsieh, M-H
dc.contributor.author	Tao, D
dc.date.accessioned	2023-03-13T23:04:35Z
dc.date.available	2023-03-13T23:04:35Z
dc.date.issued	2020-10-20
dc.identifier.citation	npj Quantum Information, 2020, 8, pp. 62
dc.identifier.uri	http://hdl.handle.net/10453/167217
dc.description.abstract	Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.
dc.language	en
dc.relation.ispartof	npj Quantum Information
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Quantum circuit architecture search for variational quantum algorithms
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	recently_added	*
dc.date.updated	2023-03-13T23:04:27Z
pubs.volume	8

Abstract:

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.

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http://hdl.handle.net/10453/167217