Quantum state discrimination using noisy quantum neural networks

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, 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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