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

TL;DR

This paper introduces a quantum neural network model called QPF, along with a polynomial-time learning algorithm SAL that leverages quantum parallelism to optimize neural network weights and architectures.

Contribution

It presents the first quantum perceptron over a field and a novel polynomial-time architecture learning algorithm using quantum parallelism.

Findings

QPF generalizes classical perceptrons and overcomes previous quantum models' limitations.

SAL efficiently searches for optimal architectures in linear time relative to training patterns.

First quantum algorithm for neural architecture search with polynomial complexity.

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.