Quantum Neuron: an elementary building block for machine learning on quantum computers

2017/11/30 by Yudong Cao, Cao, Yudong, Gian Giacomo Guerreschi +4 · 1 voice · 13 citations
Computer Science · #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #cs.NE #quant-ph

paper · pdf · doi:10.48550/arxiv.1711.11240

openalex publication_date 2017/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear function to the resulting weighted sum. Several attempts to generalize neurons to the quantum regime have been proposed, but all proposals collided with the difficulty of implementing non-linear activation functions, which is essential for classical neurons, due to the linear nature of quantum mechanics. Here we propose a solution to this roadblock in the form of a small quantum circuit that naturally simulates neurons with threshold activation. Our quantum circuit defines a building block, the "quantum neuron", that can reproduce a variety of classical neural network constructions while maintaining the ability to process superpositions of inputs and preserve quantum coherence and entanglement. In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately. When arranged to mimic Hopfield networks, quantum neural networks exhibit properties of associative memory. Patterns are encoded using the simple Hebbian rule for the weights and we demonstrate attractor dynamics from corrupted inputs. Finally, the fact that our quantum model closely captures (traditional) neural network dynamics implies that the vast body of literature and results on neural networks becomes directly relevant in the context of quantum machine learning.

Citations

• Dynamical principles in neuroscience
• A Simplex Method for Function Minimization

Cited by

• Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)
• FRQI Pairs method for image classification using Quantum Recurrent Neural Network
• Pseudo quantum advantages in perceptron storage capacity
• Hybrid Quantum-Classical Recurrent Neural Networks
• Canonical quantization of neurons
• Advances in Quantum Deep Learning: An Overview
• Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction
• Collaborative Filtering using Variational Quantum Hopfield Associative Memory
• Perspectives on Utilization of Measurements in Quantum Algorithms
• Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
• On quantum neural networks
• Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
• A review of Quantum Neural Networks: Methods, Models, Dilemma

Discussions

• Quantum Neuron: a building block for machine learning on quantum computers [pdf] [hn, 10 points, 0 comments]

Related

• Quantum Neural Machine Learning - Backpropagation and Dynamics
• Development and Training of Quantum Neural Networks, Based on the Principles of Grover's Algorithm
• Quantum activation functions for quantum neural networks
• Information-driven Nonlinear Quantum Neuron
• A Quick Introduction to Quantum Machine Learning for Non-Practitioners