Artificial Intelligence and Machine Learning for Quantum Technologies

Abstract

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.

23 pages, 8 figures; comments welcome!

References in corpus (18)

• Deep Learning in Neural Networks: An Overview
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• An introduction to quantum machine learning
• Learning phase transitions by confusion
• Entanglement-free Heisenberg-limited phase estimation
• On scientific understanding with artificial intelligence
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
• Variational neural network ansatz for steady states in open quantum systems
• Constructing neural stationary states for open quantum many-body systems
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Deep Reinforcement Learning for Quantum Gate Control
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Quantum machine learning and quantum biomimetics: A perspective
• Natural Evolutionary Strategies for Variational Quantum Computation
• Control of Stochastic Quantum Dynamics by Differentiable Programming
• The Learnability of Quantum States
• Genetic braid optimization: A heuristic approach to compute quasiparticle braids

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• Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
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• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
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• Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
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• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
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• Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
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• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
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• Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
• Learning a quantum computer's capability
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• Learning the dynamics of Markovian open quantum systems from experimental data
• Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
• Exploring exotic configurations with anomalous features using deep learning: Application of classical and quantum-classical hybrid anomaly detection
• Data-Driven Reconstruction and Characterization of Stochastic Dynamics via Dynamical Mode Decomposition
• Bounding fidelity in quantum feedback control: theory and applications to Dicke state preparation
• Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center