Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration or pattern recognition. The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector. Here we show how the design for the implementation of a previously introduced quantum artificial neuron [ npj Quant. Inf. 5 , 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous- instead of discrete-valued input vectors, without increasing the number of qubits. This further step is crucial to allow for a direct application of gradient descent based learning procedures, which would not be compatible with binary-valued dat...

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具有连续值输入数据的人工神经元的量子计算模型

已经提出了人工神经网络作为潜在的算法，可以从在量子计算机上实现和运行中受益。特别是，它们有望大大增强人工智能的任务，例如图像细化或模式识别。神经网络的基本组成部分是人工神经元，即对输入数据形式的一组数据执行简单数学运算的计算单元。在这里，我们展示了如何实现先前引入的量子人工神经元[npj Quant]的设计。Inf。图5、26]充分利用了叠加状态的使用来对二进制值的输入数据进行编码，可以进一步推广为接受连续值而不是离散值的输入向量，而不会增加量子位的数量。