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!

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• Framework for Learning and Control in the Classical and Quantum Domains
• Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• Characterization of a driven two-level quantum system by Supervised Learning
• Noise Classification in Three-Level Quantum Networks by Machine Learning
• Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
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• Supervised Learning Guarantee for Quantum AdaBoost
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
• Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Machine learning non-Markovian two-level quantum noise spectroscopy
• Revealing molecule-internal mechanisms that control phonon heat transport through single-molecule junctions by a genetic algorithm
• Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
• Unveiling the nonclassicality within quasi-distribution representations through deep learning
• Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
• Learning a quantum computer's capability
• Learning the dynamics of Markovian open quantum systems from experimental data
• Artificial intelligence for representing and characterizing quantum systems
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• Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
• Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center
• 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