Training Quantum Neural Networks on NISQ Devices

• URL: http://arxiv.org/abs/2104.06081v1
• Date: Tue, 13 Apr 2021 10:34:03 GMT
• Title: Training Quantum Neural Networks on NISQ Devices
• Authors: Kerstin Beer, Daniel List, Gabriel M\"uller, Tobias J. Osborne, Christian Struckmann
• Abstract summary: We evaluate the noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ devices. We find that a DQNN learns an unknown unitary more reliably than QAOA and is less susceptible to gate noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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