Deep quantum neural networks equipped with backpropagation on a superconducting processor

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.

7 pages (main text) + 11 pages (Supplementary Information), 10 figures

References in corpus (6)

• Charge insensitive qubit design derived from the Cooper pair box
• Coupling Superconducting Qubits via a Cavity Bus
• Challenges and Opportunities in Quantum Machine Learning
• Experimental Realization of Quantum Artificial Intelligence
• Machine learning meets quantum physics
• Deep quantum neural networks equipped with backpropagation on a superconducting processor

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• Probing many-body Bell correlation depth with superconducting qubits
• Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning