Quantum implementation of an artificial feed-forward neural network

Abstract

Artificial intelligence algorithms largely build on multi-layered neural networks. Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run. Quantum computing promises to solve certain tasks much more efficiently than any classical computing machine, and actual quantum processors are now becoming available through cloud access to perform experiments and testing also outside of research labs. Here we show in practice an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor using up to 7 active qubits. The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity with respect to their classical counterpart, and it is able to carry out an elementary classification task which would be impossible to achieve with a single node. We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion, thus opening the way to hybrid as well as fully quantum solutions for artificial intelligence to be run on near-term intermediate-scale quantum hardware.

arXiv comment10 pages, 5 figures

References in corpus (37)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Quantum Convolutional Neural Networks
• Quantum principal component analysis
• Error mitigation for short-depth quantum circuits
• Multi-particle entanglement of hot trapped ions
• Quantum information processing with superconducting circuits: a review
• Extending the computational reach of a noisy superconducting quantum processor
• Efficient Learning for Deep Quantum Neural Networks
• The quest for a Quantum Neural Network
• Efficient variational quantum simulator incorporating active error minimisation
• Quantum-Classical Computation of Schwinger Model Dynamics using Quantum Computers
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits
• Quantum-state preparation with universal gate decompositions
• A quantum information processor with trapped ions
• Hierarchical quantum classifiers
• Quantum generalisation of feedforward neural networks
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Implementing a distance-based classifier with a quantum interference circuit
• Perspective: Toward large-scale fault-tolerant universal photonic quantum computing
• Quantum Hopfield neural network
• Quantum Hypergraph States
• Simulating a perceptron on a quantum computer
• Quantum circuits with uniformly controlled one-qubit gates
• Unitary quantum perceptron as efficient universal approximator
• Variational Quantum Unsampling on a Quantum Photonic Processor
• Multipartite Entanglement and Global Information
• Quantum machine learning and quantum biomimetics: A perspective
• Quantum computing model of an artificial neuron with continuously valued input data

Cited by in corpus (20)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Error Mitigation
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Quantum computing models for artificial neural networks
• A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
• RGB Image Classification with Quantum Convolutional Ansaetze
• Variational learning for quantum artificial neural networks
• Algorithmic Error Mitigation Scheme for Current Quantum Processors
• Quantum neural networks force fields generation
• HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• Evolution strategies: Application in hybrid quantum-classical neural networks
• Quantum variational learning for entanglement witnessing
• Parametrized constant-depth quantum neuron
• Error mitigation, optimization, and extrapolation on a trapped ion testbed
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Quantum memristor with vacuum--one-photon qubits
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer
• Quantum neural network autoencoder and classifier applied to an industrial case study
• Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization