The quest for a Quantum Neural Network

Abstract

With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. It establishes requirements for a meaningful QNN and reviews existing literature against these requirements. It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks. An outlook on possible ways forward is given, emphasizing the idea of Open Quantum Neural Networks based on dissipative quantum computing.

Review of Quantum Neural Networks research; 21 pages, 5 figs, 71 Refs

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Cited by in corpus (189)

• Noisy intermediate-scale quantum (NISQ) algorithms
• An introduction to quantum machine learning
• Parameterized quantum circuits as machine learning models
• The power of quantum neural networks
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Efficient Learning for Deep Quantum Neural Networks
• Challenges and Opportunities in Quantum Machine Learning
• The quest for a Quantum Neural Network
• Data re-uploading for a universal quantum classifier
• Quantum machine learning: a classical perspective
• Generalization in quantum machine learning from few training data
• Quantum-enhanced machine learning
• Continuous-variable quantum neural networks
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Hierarchical quantum classifiers
• Quantum generalisation of feedforward neural networks
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Experimental Two-dimensional Quantum Walk on a Photonic Chip
• Effect of barren plateaus on gradient-free optimization
• Quantum Hopfield neural network
• Trainability of Dissipative Perceptron-Based Quantum Neural Networks
• Simulating a perceptron on a quantum computer
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Equivalence of quantum barren plateaus to cost concentration and narrow gorges
• Machine Learning for Quantum Matter
• Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
• Machine learning method for state preparation and gate synthesis on photonic quantum computers
• Quantum autoencoders to denoise quantum data
• Quantum computing models for artificial neural networks
• Quantum Neuron: an elementary building block for machine learning on quantum computers
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Theory for Equivariant Quantum Neural Networks
• Unitary quantum perceptron as efficient universal approximator
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Biology and medicine in the landscape of quantum advantages
• Quantum Algorithms for Deep Convolutional Neural Networks
• Quantum algorithm for association rules mining
• Quantum algorithm for association rules mining
• QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
• Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
• Quantum Neural Network for Quantum Neural Computing
• Quantum implementation of an artificial feed-forward neural network
• Subtleties in the trainability of quantum machine learning models
• Learning quantum data with the quantum Earth Mover's distance
• Concentric transmon qubit featuring fast tunability and an anisotropic magnetic dipole moment
• Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
• Harnessing symmetry to control quantum transport
• Bayesian Deep Learning on a Quantum Computer
• Building quantum neural networks based on swap test
• Enhancing Quantum Support Vector Machines through Variational Kernel Training
• FedQNN: Federated Learning using Quantum Neural Networks
• On the statistical complexity of quantum circuits
• A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
• Quantum machine learning and quantum biomimetics: A perspective
• Quantum algorithm for the Navier Stokes equations by using the streamfunction vorticity formulation and the lattice Boltzmann method
• MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform
• Open quantum generalisation of Hopfield neural networks
• Quantum Computing: An Overview Across the System Stack
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
• Supervised Quantum Learning without Measurements
• Advances in Quantum Deep Learning: An Overview
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Machine learning \& artificial intelligence in the quantum domain
• Quantum Error Correction with Quantum Autoencoders
• Pattern recognition on the quantum Bloch sphere
• Variational Quantum Classifiers Through the Lens of the Hessian
• Advances in quantum machine learning
• A semi-agnostic ansatz with variable structure for quantum machine learning
• Quantum Superposition Inspired Spiking Neural Network
• Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
• Quantum reservoir computation utilising scale-free networks
• Quantum neural networks to simulate many-body quantum systems
• Resummation for Nonequilibrium Perturbation Theory and Application to Open Quantum Lattices
• A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets
• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
• Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
• Quantum machine learning with adaptive linear optics
• Quantum computing model of an artificial neuron with continuously valued input data
• A Simple Quantum Neural Net with a Periodic Activation Function
• Entanglement-guided architectures of machine learning by quantum tensor network
• Noisy Quantum Kernel Machines
• A review of Quantum Neural Networks: Methods, Models, Dilemma
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
• Towards a feasible implementation of quantum neural networks using quantum dots
• Quantum Neural Networks for Power Flow Analysis
• Weightless neural network parameters and architecture selection in a quantum computer
• Harnessing Quantum Extreme Learning Machines for image classification
• Entanglement-based quantum deep learning
• Speed-up Quantum Perceptron via Shortcuts to Adiabaticity
• Phase diagram of quantum generalized Potts-Hopfield neural networks
• The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0
• Networked Quantum Services
• Supervised Learning with Quantum Measurements
• A Historical Interaction between Artificial Intelligence and Philosophy
• On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
• Entanglement in a quantum neural network based on quantum dots
• Quantum Machine Learning Implementations: Proposals and Experiments
• Quantum machine learning with glow for episodic tasks and decision games
• Fully quantum algorithm for lattice Boltzmann methods with application to partial differential equations
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols
• Entanglement assisted training algorithm for supervised quantum classifiers
• Optimal storage capacity of quantum Hopfield neural networks
• A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor
• Direct implementation of a perceptron in superconducting circuit quantum hardware
• Quantum correlated cluster mean-field theory applied to the transverse Ising model
• Approximation of Quantum States Using Decision Diagrams
• Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers
• Resource frugal optimizer for quantum machine learning
• Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
• Two component quantum walk in one-dimensional lattice with hopping imbalance
• Distributed Quantum Neural Networks via Partitioned Features Encoding
• Implementing perceptron models with qubits
• Critically excited states with enhanced memory and pattern recognition capacities in quantum brain networks: Lesson from black holes
• The dilemma of quantum neural networks
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Noncompact uniform universal approximation
• Quantum Machine Learning and its Supremacy in High Energy Physics
• Classicalization Clearly: Quantum Transition into States of Maximal Memory Storage Capacity
• Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Learning Algebraic Models of Quantum Entanglement
• Image Compression and Classification Using Qubits and Quantum Deep Learning
• EQC : Ensembled Quantum Computing for Variational Quantum Algorithms
• Quantum Algorithm For Estimating Eigenvalue
• Automatic Post-selection by Ancillae Thermalisation
• Experimental demonstration of Quantum Overlapping Tomography
• Minimizing state preparation times in pulse-level variational molecular simulations
• A Quantum Implementation Model for Artificial Neural Networks
• Neural Networks Architecture Evaluation in a Quantum Computer
• Classifying quantum data by dissipation
• Quantum density peak clustering
• Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks
• Classifying Data with Local Hamiltonians
• Liouvillian skin effect in quantum neural networks
• Quantum Neural Network and Soft Quantum Computing
• Quantum Algorithm for Estimating Betti Numbers Using Cohomology Approach
• Quantum enhanced cross-validation for near-optimal neural networks architecture selection
• Direct-dynamical entanglement-discord relations
• Implementing arbitrary quantum operations via quantum walks on a cycle graph
• Learning Simon's quantum algorithm
• Quantum neural networks with multi-qubit potentials
• The role of coherence theory in attractor quantum neural networks
• Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier
• Multi-Valued Quantum Neurons
• Estimating many properties of a quantum state via quantum reservoir processing
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• Tunable Quantum Neural Networks for Boolean Functions
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer
• Learning Fourier series with parametrized quantum circuits
• Equilibrium Propagation: the Quantum and the Thermal Cases
• Non-linear classification capability of quantum neural networks due to emergent quantum metastability
• qlbm -- A Quantum Lattice Boltzmann Software Framework
• Almost fault-tolerant quantum machine learning with drastic overhead reduction
• Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm
• Ethical Quantum Computing: A Roadmap
• Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication
• Quantum Annealing Formulation for Binary Neural Networks
• Batched quantum state exponentiation and quantum Hebbian learning
• Constant-time Quantum Algorithm for Homology Detection in Closed Curves
• A Unified Framework for Quantum Supervised Learning
• Quantum Hopfield Model with Dilute Memories
• Defining Quantum Neural Networks via Quantum Time Evolution
• Quantum Adiabatic Evolution for Global Optimization in Big Data
• The Learnability of Unknown Quantum Measurements
• Simulation of exceptional-point systems on quantum computers for quantum sensing
• Quantum neural network autoencoder and classifier applied to an industrial case study
• The Kraus representation for the dynamics of open quantum systems
• Symmetry and decoherence-free subspaces in quantum neural networks
• On the capacity of a quantum perceptron for storing biased patterns
• Single-Qudit Quantum Neural Networks for Multiclass Classification
• A hybrid quantum-classical neural network with deep residual learning
• Enhancing Small Dataset Classification Using Projected Quantum Kernels with Convolutional Neural Networks
• On the Feasibility of Deep Learning Classification from Raw Signal Data in Radiology, Ultrasonography and Electrophysiology
• Autonomous Perceptron Neural Network Inspired from Quantum computing
• Tunable Quantum Neural Networks in the QPAC-Learning Framework
• High-expressibility Quantum Neural Networks using only classical resources
• A Quantum Convolutional Neural Network for Image Classification
• Clustering and enhanced classification using a hybrid quantum autoencoder
• Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
• Optical Quantum Computing
• Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage
• Does Quantum Mechanics Breed Larger, More Intricate Quantum Theories? The Case for Experience-Centric Quantum Theory and the Interactome of Quantum Theories
• Variational Quantum Circuit Model for Knowledge Graphs Embedding
• Quantum Algorithms for State Preparation and Data Classification based on Stabilizer Codes
• Superior resilience to poisoning and amenability to unlearning in quantum machine learning
• Quantum Algorithm For Solving Nonlinear Algebraic Equations