Quantum Circuit AutoEncoder
Jun Wu, Hao Fu, Mingzheng Zhu, Haiyue Zhang, Wei Xie, Xiang-Yang Li

TL;DR
This paper introduces Quantum Circuit AutoEncoder (QCAE), a novel quantum neural network model designed to compress, encode, and process information within quantum circuits, with applications in anomaly detection and noise mitigation.
Contribution
The work generalizes autoencoder concepts to quantum circuits, providing a comprehensive protocol and a variational algorithm for effective information compression and processing in quantum computing.
Findings
QCAE can effectively compress quantum circuit information
QCAE detects anomalies in quantum circuits
QCAE mitigates depolarizing noise in quantum devices
Abstract
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
