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

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Jiang W,Xiong J,Shi Y

doi

10.1038/s41467-020-20729-5

subject

Has Abstract

pub_date

2021-01-25 00:00:00

pages

579

issue

1

issn

2041-1723

pii

10.1038/s41467-020-20729-5

journal_volume

12

pub_type

杂志文章

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