Deep quantum neural networks on a superconducting processor

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 an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. We experimentally perform the forward process of the backpropagation algorithm and classically simulate the backward process. 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 indicate that the number of coherent qubits required to maintain does not scale with the depth of the deep quantum neural network, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices. Experimental studies about the trainability and generalization capacities of quantum neural networks are highly in need. Here, the authors implement a previously proposed parametrization and training scheme using a 6-qubit superconducting quantum processor.

Automated summary

Deep learning and quantum computing have made significant progress in recent years. This work demonstrates the training of deep quantum neural networks using the backpropagation algorithm with a six-qubit programmable superconducting processor. The experimental results show the efficient training of three-layer and six-layer deep quantum neural networks for learning two-qubit and single-qubit quantum channels. This research provides valuable guidance for quantum machine learning applications with current and future quantum devices.