Supervised Learning Enhanced Quantum Circuit Transformation

Zhou, X; Feng, Y; Li, S

Supervised Learning Enhanced Quantum Circuit Transformation

Zhou, X Feng, Y

Li, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023, 42, (2), pp. 437-447
Issue Date:: 2023-02-01

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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.accessioned	2023-02-22T05:13:38Z
dc.date.available	2023-02-22T05:13:38Z
dc.date.issued	2023-02-01
dc.identifier.citation	IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023, 42, (2), pp. 437-447
dc.identifier.issn	0278-0070
dc.identifier.issn	1937-4151
dc.identifier.uri	http://hdl.handle.net/10453/166321
dc.description.abstract	A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). By inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constraint imposed by the QPU. Due to the nonnegligible gate error and the limited qubit coherence time of the QPU, QCT algorithms that minimize gate number or circuit depth or maximize the fidelity of output circuits are in urgent need. Unfortunately, finding optimized transformations often involve exhaustive searches, which are extremely time consuming and not practical for most circuits. In this article, we propose a framework that uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP gate. ANNs can be trained offline in a distributed way and the trained ANN can be easily incorporated into QCT algorithms to enable them to search deeper without bringing too much overhead in time complexity. Exemplary embeddings of the trained ANNs into target QCT algorithms demonstrate that the transformation performance can be consistently improved on QPUs with various connectivity structures and random or realistic quantum circuits.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180100691
dc.relation	Beijing Baidu Netcom Science and Technology Co Ltd
dc.relation	http://purl.org/au-research/grants/arc/DP220102059
dc.relation.ispartof	IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
dc.relation.isbasedon	10.1109/TCAD.2022.3179223
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 1006 Computer Hardware
dc.subject.classification	Computer Hardware & Architecture
dc.title	Supervised Learning Enhanced Quantum Circuit Transformation
dc.type	Journal Article
utslib.citation.volume	42
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1006 Computer Hardware
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	embargoed	*
utslib.copyright.embargo	2025-02-01T00:00:00+1000Z
dc.date.updated	2023-02-22T05:13:36Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	2

Abstract:

A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). By inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constraint imposed by the QPU. Due to the nonnegligible gate error and the limited qubit coherence time of the QPU, QCT algorithms that minimize gate number or circuit depth or maximize the fidelity of output circuits are in urgent need. Unfortunately, finding optimized transformations often involve exhaustive searches, which are extremely time consuming and not practical for most circuits. In this article, we propose a framework that uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP gate. ANNs can be trained offline in a distributed way and the trained ANN can be easily incorporated into QCT algorithms to enable them to search deeper without bringing too much overhead in time complexity. Exemplary embeddings of the trained ANNs into target QCT algorithms demonstrate that the transformation performance can be consistently improved on QPUs with various connectivity structures and random or realistic quantum circuits.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166321