A Monte Carlo Tree Search Framework for Quantum Circuit Transformation

Zhou, X; Feng, Y; Li, S

A Monte Carlo Tree Search Framework for Quantum Circuit Transformation

Zhou, X Feng, Y

Li, S

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2020, 2020-November, pp. 1-7
Issue Date:: 2020-11-02

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Field	Value	Language
dc.contributor.author	Zhou, X
dc.contributor.author	Feng, Y https://orcid.org/0000-0002-3097-3896
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546
dc.date	2020-11-02
dc.date.accessioned	2021-04-09T06:18:27Z
dc.date.available	2021-04-09T06:18:27Z
dc.date.issued	2020-11-02
dc.identifier.citation	IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2020, 2020-November, pp. 1-7
dc.identifier.isbn	978-1-4503-8026-3
dc.identifier.issn	1092-3152
dc.identifier.uri	http://hdl.handle.net/10453/147959
dc.description.abstract	In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit executable, a circuit transformation process is necessary to transform it into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. Due to the high branching factor and vast search space, almost all existing algorithms search very shallowly and thus, very often, can reach (at most) locally optimal solutions. We propose a Monte Carlo Tree Search framework to tackle this problem, which enables the search process to go deeper. In particular, we design, by taking both short- and long-term rewards into consideration, a scoring mechanism, and propose to use a fast random strategy for simulation. The thus designed search algorithm is polynomial in all relevant parameters and empirical results on extensive realistic circuits show that it can reduce, in average, the size of the output circuits by at least 30% when compared with the state-of-the-art algorithms on IBM Q20.
dc.language	en
dc.publisher	ACM
dc.relation	http://purl.org/au-research/grants/arc/DP180100691
dc.relation.ispartof	IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
dc.relation.ispartof	International Conference on Computer-Aided Design
dc.relation.isbasedon	10.1145/3400302.3415621
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Monte Carlo Tree Search Framework for Quantum Circuit Transformation
dc.type	Conference Proceeding
utslib.citation.volume	2020-November
utslib.location.activity	Virtual Conference
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-09T06:18:25Z
pubs.finish-date	2020-11-05
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-11-02
pubs.volume	2020-November
dc.location	USA

Abstract:

In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit executable, a circuit transformation process is necessary to transform it into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. Due to the high branching factor and vast search space, almost all existing algorithms search very shallowly and thus, very often, can reach (at most) locally optimal solutions. We propose a Monte Carlo Tree Search framework to tackle this problem, which enables the search process to go deeper. In particular, we design, by taking both short- and long-term rewards into consideration, a scoring mechanism, and propose to use a fast random strategy for simulation. The thus designed search algorithm is polynomial in all relevant parameters and empirical results on extensive realistic circuits show that it can reduce, in average, the size of the output circuits by at least 30% when compared with the state-of-the-art algorithms on IBM Q20.

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http://hdl.handle.net/10453/147959