Training Quantum Neural Networks on NISQ Devices

TL;DR

This paper evaluates the noise tolerance of two quantum neural network architectures, DQNN and QAOA, on IBM's NISQ devices, finding DQNN to be more reliable and less noise-sensitive.

Contribution

It provides a comparative analysis of DQNN and QAOA architectures' performance on NISQ devices, highlighting the superior noise resilience of DQNN.

Findings

DQNN learns unknown unitaries more reliably than QAOA.

DQNN is less susceptible to gate noise.

Both architectures succeed in the learning task.

Abstract

The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial opportunities for the development of quantum algorithms. Here we evaluate the noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ devices, namely, dissipative QNN (DQNN) whose building-block perceptron is a completely positive map, and the quantum approximate optimization algorithm (QAOA). We compare these two approaches to learning an unknown unitary. While both networks succeed in this learning task, we find that a DQNN learns an unknown unitary more reliably than QAOA and is less susceptible to gate noise.