A co-design framework of neural networks and quantum circuits towards quantum advantage

Weiwen Jiang (), Jinjun Xiong () and Yiyu Shi ()
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Weiwen Jiang: University of Notre Dame
Jinjun Xiong: IBM Thomas J. Watson Research Center
Yiyu Shi: University of Notre Dame

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Despite the pursuit of quantum advantages in various applications, the power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that effectively designs a neural network suitable for quantum circuit. Here, we present a neural network and quantum circuit co-design framework, namely QuantumFlow, to address the issue. In QuantumFlow, we represent data as unitary matrices to exploit quantum power by encoding n = 2k inputs into k qubits and representing data as random variables to seamlessly connect layers without measurement. Coupled with a novel algorithm, the cost complexity of the unitary matrices-based neural computation can be reduced from O(n) in classical computing to O(polylog(n)) in quantum computing. Results show that on MNIST dataset, QuantumFlow can achieve an accuracy of 94.09% with a cost reduction of 10.85 × against the classical computer. All these results demonstrate the potential for QuantumFlow to achieve the quantum advantage.

Date: 2021
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DOI: 10.1038/s41467-020-20729-5

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