Advances in Quantum Deep Learning: An Overview

Abstract

The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. In this work, we present an overview of advances in the intersection of quantum computing and deep learning by discussing the technical contributions, strengths and similarities of various research works in this domain. To this end, we review and summarise the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs). We also briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.

References in corpus (34)

• Adam: A Method for Stochastic Optimization
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Quantum support vector machine for big data classification
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Deep Learning in Spiking Neural Networks
• Quantum principal component analysis
• On the Number of Linear Regions of Deep Neural Networks
• Efficient Learning for Deep Quantum Neural Networks
• The quest for a Quantum Neural Network
• Quantum algorithms for supervised and unsupervised machine learning
• Quantum generalisation of feedforward neural networks
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Recurrent Neural Network Wave Functions
• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Quantum Entanglement in Deep Learning Architectures
• Quantum Hopfield neural network
• Application of Quantum Annealing to Training of Deep Neural Networks
• Quantum gradient descent for linear systems and least squares
• Quantum Neuron: an elementary building block for machine learning on quantum computers
• Quantum neural network
• Quantum Cellular Neural Networks
• Mathematical Foundations for a Compositional Distributional Model of Meaning
• Quantum Algorithms for Compositional Natural Language Processing
• Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets
• Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
• A Simple Quantum Neural Net with a Periodic Activation Function
• Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
• Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks
• A Quantum Model for Multilayer Perceptron
• QDNN: DNN with Quantum Neural Network Layers
• Quantum neural networks driven by information reservoir

Cited by in corpus (10)

• Solving nonlinear differential equations with differentiable quantum circuits
• A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
• QuGAN: A Quantum State Fidelity based Generative Adversarial Network
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
• QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
• Speeding up qubit control with bipolar single-flux-quantum pulse sequences
• Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers
• Quantum Neural Networks: Concepts, Applications, and Challenges
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• On quantum neural networks