Ansatz Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows

Basheer, A; Feng, Y; Ferrie, C; Li, S

Ansatz Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows

Basheer, A Feng, Y Ferrie, C

Li, S

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Publisher:: International Joint Conferences on Artificial Intelligence
Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2024, pp. 3706-3714
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Basheer, A
dc.contributor.author	Feng, Y
dc.contributor.author	Ferrie, C https://orcid.org/0000-0003-2736-9943
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546
dc.date.accessioned	2025-03-05T03:58:41Z
dc.date.available	2025-03-05T03:58:41Z
dc.date.issued	2024-01-01
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2024, pp. 3706-3714
dc.identifier.issn	1045-0823
dc.identifier.uri	http://hdl.handle.net/10453/185514
dc.description.abstract	Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard training model requires a significant amount of quantum resources, which led researchers to explore classical shadows as an alternative that consumes exponentially fewer quantum resources. However, the existing approach only works when dealing with local observables and shallow Alternating Layered Ansatz (ALA), thus severely limiting its potential in solving problems such as quantum state preparation, where the ideal state might not be approximable with an ALA. In this work, we present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature, when combined with observables of low Frobenius norm. We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis, are compatible with our protocol. We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model.
dc.language	en
dc.publisher	International Joint Conferences on Artificial Intelligence
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Ansatz Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows
dc.type	Conference Proceeding
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Quantum Software and Information (QSI)
utslib.copyright.status	open_access	*
dc.date.updated	2025-03-05T03:58:39Z
pubs.publication-status	Published

Abstract:

Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard training model requires a significant amount of quantum resources, which led researchers to explore classical shadows as an alternative that consumes exponentially fewer quantum resources. However, the existing approach only works when dealing with local observables and shallow Alternating Layered Ansatz (ALA), thus severely limiting its potential in solving problems such as quantum state preparation, where the ideal state might not be approximable with an ALA. In this work, we present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature, when combined with observables of low Frobenius norm. We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis, are compatible with our protocol. We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model.

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http://hdl.handle.net/10453/185514