Quantum State Discrimination Using Noisy Quantum Neural Networks

TL;DR

This paper explores the impact of noise on quantum neural networks for state discrimination, demonstrating that smaller circuits and training at higher noise levels enable effective use on near-term noisy quantum devices.

Contribution

Introduces a smaller circuit ansatz for quantum neural networks, improving performance under noise and enabling practical applications on current noisy quantum hardware.

Findings

Smaller circuit ansatz mitigates noise effects in QNNs.

Networks trained at higher noise levels still converge to useful parameters.

QNNs can effectively perform state discrimination on noisy quantum devices.

Abstract

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, applicable on near-term quantum devices as it fulfils the above criteria. We find that when simulating gradient calculation on a noisy device, a large number of parameters is disadvantageous. By introducing a new smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We also show that networks trained at higher noise levels can still converge to useful parameters. Our findings show that noisy quantum computers can be used in applications for state discrimination and for classifiers of the output of quantum generative adversarial networks.