Quantum perceptron over a field and neural network architecture selection in a quantum computer

Abstract

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.

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• Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research
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• Unitary quantum perceptron as efficient universal approximator
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• Error mitigation via verified phase estimation
• Classification with Quantum Machine Learning: A Survey
• Building quantum neural networks based on swap test
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
• Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks
• A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets
• Experimental demonstration of quantum learning speed-up with classical input data
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Parametric Probabilistic Quantum Memory
• Quantum Machine Learning and its Supremacy in High Energy Physics
• A Quantum Implementation Model for Artificial Neural Networks
• Neural Networks Architecture Evaluation in a Quantum Computer
• Quantum enhanced cross-validation for near-optimal neural networks architecture selection
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• Measurement-based quantum machine learning
• Generalization Study of Quantum Neural Network
• Pseudo quantum advantages in perceptron storage capacity