Quantum state discrimination using noisy quantum neural networks

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find...

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Main Authors: Andrew Patterson, Hongxiang Chen, Leonard Wossnig, Simone Severini, Dan Browne, Ivan Rungger
Format: Article
Language:English
Published: American Physical Society 2021-01-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.3.013063
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author Andrew Patterson
Hongxiang Chen
Leonard Wossnig
Simone Severini
Dan Browne
Ivan Rungger
author_facet Andrew Patterson
Hongxiang Chen
Leonard Wossnig
Simone Severini
Dan Browne
Ivan Rungger
author_sort Andrew Patterson
collection DOAJ
description Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters is disadvantageous. By introducing a smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We present a model showing that the main effect of the noise is to increase the overlap between the states as circuit gates are applied, hence making discrimination more difficult. Our findings demonstrate that noisy quantum computers can be used for state discrimination and other applications, such as classifiers of the output of quantum generative adversarial networks.
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spelling doaj.art-601f89fc300a4efeac4d5ddc80d4acbf2024-04-12T17:06:32ZengAmerican Physical SocietyPhysical Review Research2643-15642021-01-013101306310.1103/PhysRevResearch.3.013063Quantum state discrimination using noisy quantum neural networksAndrew PattersonHongxiang ChenLeonard WossnigSimone SeveriniDan BrowneIvan RunggerNear-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters is disadvantageous. By introducing a smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We present a model showing that the main effect of the noise is to increase the overlap between the states as circuit gates are applied, hence making discrimination more difficult. Our findings demonstrate that noisy quantum computers can be used for state discrimination and other applications, such as classifiers of the output of quantum generative adversarial networks.http://doi.org/10.1103/PhysRevResearch.3.013063
spellingShingle Andrew Patterson
Hongxiang Chen
Leonard Wossnig
Simone Severini
Dan Browne
Ivan Rungger
Quantum state discrimination using noisy quantum neural networks
Physical Review Research
title Quantum state discrimination using noisy quantum neural networks
title_full Quantum state discrimination using noisy quantum neural networks
title_fullStr Quantum state discrimination using noisy quantum neural networks
title_full_unstemmed Quantum state discrimination using noisy quantum neural networks
title_short Quantum state discrimination using noisy quantum neural networks
title_sort quantum state discrimination using noisy quantum neural networks
url http://doi.org/10.1103/PhysRevResearch.3.013063
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