Classical variational simulation of the Quantum Approximate Optimization Algorithm

Abstract A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressi...

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Autores principales:	Matija Medvidović, Giuseppe Carleo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fc58d9b990734d7cb5956860f127b092
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spelling	oai:doaj.org-article:fc58d9b990734d7cb5956860f127b0922021-12-02T17:41:32ZClassical variational simulation of the Quantum Approximate Optimization Algorithm10.1038/s41534-021-00440-z2056-6387https://doaj.org/article/fc58d9b990734d7cb5956860f127b0922021-06-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00440-zhttps://doaj.org/toc/2056-6387Abstract A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressing this question. We introduce a method to simulate layered quantum circuits consisting of parametrized gates, an architecture behind many variational quantum algorithms suitable for near-term quantum computers. A neural-network parametrization of the many-qubit wavefunction is used, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers, approximately implementing 324 RZZ gates and 216 RX gates without requiring large-scale computational resources. For larger systems, our approach can be used to provide accurate QAOA simulations at previously unexplored parameter values and to benchmark the next generation of experiments in the Noisy Intermediate-Scale Quantum (NISQ) era.Matija MedvidovićGiuseppe CarleoNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Matija Medvidović Giuseppe Carleo Classical variational simulation of the Quantum Approximate Optimization Algorithm
description	Abstract A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressing this question. We introduce a method to simulate layered quantum circuits consisting of parametrized gates, an architecture behind many variational quantum algorithms suitable for near-term quantum computers. A neural-network parametrization of the many-qubit wavefunction is used, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers, approximately implementing 324 RZZ gates and 216 RX gates without requiring large-scale computational resources. For larger systems, our approach can be used to provide accurate QAOA simulations at previously unexplored parameter values and to benchmark the next generation of experiments in the Noisy Intermediate-Scale Quantum (NISQ) era.
format	article
author	Matija Medvidović Giuseppe Carleo
author_facet	Matija Medvidović Giuseppe Carleo
author_sort	Matija Medvidović
title	Classical variational simulation of the Quantum Approximate Optimization Algorithm
title_short	Classical variational simulation of the Quantum Approximate Optimization Algorithm
title_full	Classical variational simulation of the Quantum Approximate Optimization Algorithm
title_fullStr	Classical variational simulation of the Quantum Approximate Optimization Algorithm
title_full_unstemmed	Classical variational simulation of the Quantum Approximate Optimization Algorithm
title_sort	classical variational simulation of the quantum approximate optimization algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fc58d9b990734d7cb5956860f127b092
work_keys_str_mv	AT matijamedvidovic classicalvariationalsimulationofthequantumapproximateoptimizationalgorithm AT giuseppecarleo classicalvariationalsimulationofthequantumapproximateoptimizationalgorithm
_version_	1718379683550068736

Classical variational simulation of the Quantum Approximate Optimization Algorithm

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