Error-mitigated data-driven circuit learning on noisy quantum hardware

TL;DR

This paper demonstrates how error mitigation techniques enhance the training of quantum circuit Born machines on noisy superconducting quantum devices, improving their ability to learn target distributions despite hardware noise.

Contribution

It introduces an error-mitigated, gradient-based data-driven circuit learning method for benchmarking and improving quantum circuit training on noisy hardware.

Findings

Error mitigation improves quantum circuit Born machine training.

Superconducting devices with 28 parameters successfully trained.

Benchmarking shows enhanced performance with error mitigation.

Abstract

Application-inspired benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and for generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work we use gradient-based data-driven circuit learning to benchmark the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with $28$ tunable parameters.