Quantum computing model of an artificial neuron with continuously valued input data

Abstract

Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration or pattern recognition. The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector. Here we show how the design for the implementation of a previously introduced quantum artificial neuron [npj Quant. Inf. $\text{5}$, 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous -- instead of discrete-valued input vectors, without increasing the number of qubits. This further step is crucial to allow for a direct application of an automatic differentiation learning procedure, which would not be compatible with binary-valued data encoding.

References in corpus (20)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum machine learning in feature Hilbert spaces
• Quantum Convolutional Neural Networks
• Parameterized quantum circuits as machine learning models
• Quantum random access memory
• Preparation of Entangled States by Quantum Markov Processes
• The quest for a Quantum Neural Network
• Quantum autoencoders for efficient compression of quantum data
• Continuous-variable quantum neural networks
• Quantum Boltzmann Machine
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Learning the quantum algorithm for state overlap
• Implementing a distance-based classifier with a quantum interference circuit
• Simulating a perceptron on a quantum computer
• Unitary quantum perceptron as efficient universal approximator
• Multipartite Entanglement and Global Information
• Quantum autoencoders via quantum adders with genetic algorithms

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• Universal quantum gates, artificial neurons and pattern recognition simulated by \textit{LC} resonators
• Parametrized constant-depth quantum neuron
• Multi-Valued Quantum Neurons
• Quantum optical classifier with superexponential speedup
• Quantum optical shallow networks