Deep quantum neural networks equipped with backpropagation on a superconducting processor

TL;DR

This paper demonstrates the first experimental training of deep quantum neural networks using backpropagation on a superconducting processor, achieving high fidelities in learning quantum channels and molecular states.

Contribution

It presents the first experimental implementation of backpropagation in deep quantum neural networks on a superconducting platform, advancing quantum machine learning capabilities.

Findings

Deep quantum neural networks can be trained efficiently with high fidelity.

Backpropagation enables effective learning of quantum channels and molecular states.

Deep quantum neural networks require less coherence time than previously thought.

Abstract

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.