通过少量量子资源实现可扩展量子神经网络

IF 0.7 4区物理与天体物理 Q3 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Quantum Information Pub Date : 2024-04-18 DOI:10.1142/s0219749924500187

Davide Pastorello, Enrico Blanzieri

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引用次数: 0

摘要

本文的重点是构建一个通用参数模型，该模型可以在几个量子比特上执行多个交换测试，并应用合适的测量协议。该模型等同于双层前馈神经网络，可结合小型量子模块实现。本文讨论了拟议量子方法的优势和前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Scalable quantum neural networks by few quantum resources

This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed.

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来源期刊

International Journal of Quantum Information 物理-计算机：理论方法

CiteScore

2.20

自引率

8.30%

发文量

审稿时长

10 months

期刊介绍： The International Journal of Quantum Information (IJQI) provides a forum for the interdisciplinary field of Quantum Information Science. In particular, we welcome contributions in these areas of experimental and theoretical research: Quantum Cryptography Quantum Computation Quantum Communication Fundamentals of Quantum Mechanics Authors are welcome to submit quality research and review papers as well as short correspondences in both theoretical and experimental areas. Submitted articles will be refereed prior to acceptance for publication in the Journal.