Quantum machine learning and quantum biomimetics: A perspective

Abstract

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing "intelligent" quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.

Invited Perspective article for Machine Learning: Science and Technology, 17 pages, 6 figures, 110 references

References in corpus (75)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Quantum Mechanics helps in searching for a needle in a haystack
• Quantum algorithm for solving linear systems of equations
• Machine learning and the physical sciences
• Quantum support vector machine for big data classification
• Machine learning phases of matter
• Quantum principal component analysis
• An introduction to quantum machine learning
• Circuit elements with memory: memristors, memcapacitors and meminductors
• Quantum Games and Quantum Strategies
• A high-bias, low-variance introduction to Machine Learning for physicists
• The quest for a Quantum Neural Network
• Quantum autoencoders for efficient compression of quantum data
• Quantum-enhanced machine learning
• Quantum reinforcement learning
• Quantum Boltzmann Machine
• Reinforcement Learning in Different Phases of Quantum Control
• Active learning machine learns to create new quantum experiments
• Quantum generalisation of feedforward neural networks
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Automated Search for new Quantum Experiments
• A quantum-inspired classical algorithm for recommendation systems
• Reinforcement Learning with Neural Networks for Quantum Feedback
• A case study in programming a quantum annealer for hard operational planning problems
• Quantum Optimization of Fully-Connected Spin Glasses
• Quantum speedup for active learning agents
• Tomography and Generative Data Modeling via Quantum Boltzmann Training
• Quantum Variational Autoencoder
• Quantum machine learning for quantum anomaly detection
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• When does reinforcement learning stand out in quantum control? A comparative study on state preparation
• High-Fidelity Single-Shot Toffoli Gate via Quantum Control
• Machine learning for long-distance quantum communication
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Optimizing quantum optimization algorithms via faster quantum gradient computation
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• Machine learning non-Markovian quantum dynamics
• Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
• Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
• Unitary quantum perceptron as efficient universal approximator
• Generalizable control for quantum parameter estimation through reinforcement learning
• Basic protocols in quantum reinforcement learning with superconducting circuits
• Computer-inspired Quantum Experiments
• Quantum Artificial Life in an IBM Quantum Computer
• Quantum Memristors
• Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
• Quantum Memristors with Superconducting Circuits
• Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Creation of superposition of unknown quantum states
• Genetic Algorithms for Digital Quantum Simulations
• Measurement-based adaptation protocol with quantum reinforcement learning
• Quantum autoencoders via quantum adders with genetic algorithms
• Quantum Memristors in Quantum Photonics
• A Reinforcement Learning approach for Quantum State Engineering
• Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience
• A differentiable programming method for quantum control
• Pattern recognition techniques for Boson Sampling validation
• Supervised Quantum Learning without Measurements
• Predicting quantum advantage by quantum walk with convolutional neural networks
• Qubit-based memcapacitors and meminductors
• The Forbidden Quantum Adder
• Multiqubit and multilevel quantum reinforcement learning with quantum technologies
• Experimental Implementation of a Quantum Autoencoder via Quantum Adders
• Artificial Life in Quantum Technologies
• Biomimetic Cloning of Quantum Observables
• Supervised learning of time-independent Hamiltonians for gate design
• Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning
• Quantum Markovianity as a supervised learning task
• Reinforcement learning for semi-autonomous approximate quantum eigensolver
• Machine learning transfer efficiencies for noisy quantum walks
• Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials
• Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons

Cited by in corpus (28)

• Solving nonlinear differential equations with differentiable quantum circuits
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Quantum Machine Learning: from physics to software engineering
• Computer-inspired Quantum Experiments
• Quantum implementation of an artificial feed-forward neural network
• Variational inference with a quantum computer
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• To quantum or not to quantum: towards algorithm selection in near-term quantum optimization
• Expressivity of Quantum Neural Networks
• Scrambling Ability of Quantum Neural Networks Architectures
• On the Sample Complexity of Quantum Boltzmann Machine Learning
• Quantum Computer-Aided design of Quantum Optics Hardware
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• Benefits of Open Quantum Systems for Quantum Machine Learning
• Quantum Machine Learning Implementations: Proposals and Experiments
• Quantum Logic Gate Synthesis as a Markov Decision Process
• How to enhance quantum generative adversarial learning of noisy information
• Supervised learning of few dirty bosons with variable particle number
• Exploring super-additivity of coherent information of noisy quantum channels through Genetic algorithms
• Self-replication of a quantum artificial organism driven by single-photon pulses
• A Continuous Variable Born Machine
• Variational Quantum Support Vector Machine based on $\Gamma$ matrix expansion and Variational Universal-Quantum-State Generator
• Universal quantum gates, artificial neurons and pattern recognition simulated by \textit{LC} resonators
• Reinforcement Learning Generation of 4-Qubits Entangled States
• Quantum reinforcement learning in the presence of thermal dissipation
• Experimental semi-autonomous eigensolver using reinforcement learning
• Adaptive Random Quantum Eigensolver
• Quantum Noise Sensing by generating Fake Noise