Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{nois...

Full description

Saved in:

Bibliographic Details
Main Authors:	Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, Lukasz Cincio
Format:	article
Language:	EN
Published:	Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2021
Subjects:	Physics QC1-999
Online Access:	https://doaj.org/article/d13fd1910b05422d996c43b26d729e77
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:doaj.org-article:d13fd1910b05422d996c43b26d729e77
record_format	dspace
spelling	oai:doaj.org-article:d13fd1910b05422d996c43b26d729e772021-11-26T11:59:59ZError mitigation with Clifford quantum-circuit data2521-327X10.22331/q-2021-11-26-592https://doaj.org/article/d13fd1910b05422d996c43b26d729e772021-11-01T00:00:00Zhttps://quantum-journal.org/papers/q-2021-11-26-592/pdf/https://doaj.org/toc/2521-327XAchieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.Piotr CzarnikAndrew ArrasmithPatrick J. ColesLukasz CincioVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenarticlePhysicsQC1-999ENQuantum, Vol 5, p 592 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio Error mitigation with Clifford quantum-circuit data
description	Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.
format	article
author	Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio
author_facet	Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio
author_sort	Piotr Czarnik
title	Error mitigation with Clifford quantum-circuit data
title_short	Error mitigation with Clifford quantum-circuit data
title_full	Error mitigation with Clifford quantum-circuit data
title_fullStr	Error mitigation with Clifford quantum-circuit data
title_full_unstemmed	Error mitigation with Clifford quantum-circuit data
title_sort	error mitigation with clifford quantum-circuit data
publisher	Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
publishDate	2021
url	https://doaj.org/article/d13fd1910b05422d996c43b26d729e77
work_keys_str_mv	AT piotrczarnik errormitigationwithcliffordquantumcircuitdata AT andrewarrasmith errormitigationwithcliffordquantumcircuitdata AT patrickjcoles errormitigationwithcliffordquantumcircuitdata AT lukaszcincio errormitigationwithcliffordquantumcircuitdata
_version_	1718409438753193984

Error mitigation with Clifford quantum-circuit data

Similar Items