论文标题
具有连续价值输入数据的人造神经元的量子计算模型
Quantum computing model of an artificial neuron with continuously valued input data
论文作者
论文摘要
已提出人工神经网络是潜在的算法，可以从实施和运行量子计算机上受益。特别是，他们有望极大地增强人工智能任务，例如图像阐述或模式识别。神经网络的基本构建块是人工神经元，即以输入向量形式对一组数据进行简单数学操作的计算单元。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量化的设计。 inf。 $ \ textbf {5} $，26]完全利用叠加状态编码二进制估值输入数据的$ \ textbf {5} $，26]可以进一步概括以接受连续的 - 而不是离散价值的输入向量，而无需增加量子的数量。这一进一步的步骤对于允许直接应用自动分化学习过程至关重要，这与二进制值数据编码不兼容。
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. $\textbf{5}$, 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 an automatic differentiation learning procedure, which would not be compatible with binary-valued data encoding.
