基于单量子神经元的多变量函数逼近
Multivariable functions approximation using a single quantum neuron
摘要：
我们描述了一个模型元素，能够执行通用的随机逼近的连续多变量函数的神经元和量子形式。该模型的实现形式的多障碍，多缝系统的建议，它表明，这种单神经元的模型是能够执行XOR功能无法实现与单一的经典神经元。对于该模型的简化波导变体，证明了对于没有相关可调参数的不同干涉量子替代物，系统可以近似为多变量的任何连续函数。这个定理适用于2-输入量子神经模型的基础上使用的计划开发的控制非线性多光子吸收的光量子系统。讨论了量子神经计算与量子控制的关系。
英文摘要：
We describe a model element able to perform universal stochastic approximations of continuous multivariable functions in both neuron-like and quantum form. The implementation of this model in the form of a multi-barrier, multiple-slit system is proposed and it is demonstrated that this single neuron-like model is able to perform the XOR function unrealizable with single classical neuron. For the simplified waveguide variant of this model it is proved for different interfering quantum alternatives with no correlated adjustable parameters, that the system can approximate any continuous function of many variables. This theorem is applied to the 2-input quantum neural model based on the use of the schemes developed for controlled nonlinear multiphoton absorption of light by quantum systems. The relation between the field of quantum neural computing and quantum control is discussed.
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