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.

中文翻译：

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通过少量量子资源可扩展的量子神经网络

本文重点介绍了通用参数模型的构建，该模型可以通过在少数量子位上执行多个交换测试并应用合适的测量协议来实现。该模型相当于一个两层前馈神经网络，可以通过组合小型量子模块来实现。讨论了所提出的量子方法的优点和前景。