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?=?2^k 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.

SUBMITTER: Jiang W 

PROVIDER: S-EPMC7835384 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature