Quantum Circuit AutoEncoder

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 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.