Quantum Circuit AutoEncoder

Fuente: arXiv
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Hauptverfasser: Wu, Jun, Fu, Hao, Zhu, Mingzheng, Zhang, Haiyue, Xie, Wei, Li, Xiang-Yang
Format: Preprint
Veröffentlicht: 2023
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author Wu, Jun
Fu, Hao
Zhu, Mingzheng
Zhang, Haiyue
Xie, Wei
Li, Xiang-Yang
author_facet Wu, Jun
Fu, Hao
Zhu, Mingzheng
Zhang, Haiyue
Xie, Wei
Li, Xiang-Yang
contents Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoder, we introduce the model of Quantum Circuit AutoEncoder (QCAE) to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum devices. This suggests that our algorithm is potentially applicable to other information processing tasks for quantum circuits.
format Preprint
id arxiv_https___arxiv_org_abs_2307_08446
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Quantum Circuit AutoEncoder
Wu, Jun
Fu, Hao
Zhu, Mingzheng
Zhang, Haiyue
Xie, Wei
Li, Xiang-Yang
Quantum Physics
Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoder, we introduce the model of Quantum Circuit AutoEncoder (QCAE) to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum devices. This suggests that our algorithm is potentially applicable to other information processing tasks for quantum circuits.
title Quantum Circuit AutoEncoder
topic Quantum Physics
url https://arxiv.org/abs/2307.08446