Building quantum neural networks based on swap test

Abstract

Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.

arXiv comment10 pages, 13 figures

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• Recent advances for quantum classifiers
• Fast multi-qubit gates through simultaneous two-qubit gates
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Quantum Dimensionality Reduction by Linear Discriminant Analysis
• A review of Quantum Neural Networks: Methods, Models, Dilemma
• Speed-up Quantum Perceptron via Shortcuts to Adiabaticity
• Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
• QDNN: DNN with Quantum Neural Network Layers
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Image Classification via Quantum Machine Learning
• Implementing arbitrary quantum operations via quantum walks on a cycle graph
• Scalable quantum neural networks by few quantum resources
• Constant-time Quantum Algorithm for Homology Detection in Closed Curves
• A hybrid quantum-classical neural network with deep residual learning
• Clustering and enhanced classification using a hybrid quantum autoencoder