Departamento de Estatística e Informática, Universidade Federal Rural de Pernambuco, Brazil; Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
Neural Netw. 2016 Apr;76:55-64. doi: 10.1016/j.neunet.2016.01.002. Epub 2016 Jan 27.
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.
在这项工作中，我们提出了一种名为量子感知机的量子神经网络。量子计算机尚未成为现实，因此，这项工作中提出的模型和算法无法在实际（或经典）计算机上进行模拟。QPF 是经典感知机的直接推广，解决了先前量子感知机模型中发现的一些缺点。我们还提出了一种名为基于叠加的架构学习算法（SAL）的学习算法，该算法可以优化神经网络的权重和架构。SAL 在线性时间内搜索具有最佳架构的神经网络，该时间随训练集中的模式数量呈线性增长。SAL 是第一个可以在多项式时间内确定神经网络架构的学习算法。这种加速是通过使用量子并行性和非线性量子运算符来实现的。
