Learning Quantum Systems

Abstract

The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics. Although the full simulation of large-scale quantum systems may only be possible on a quantum computer, classical characterization and optimization methods still play an important role. Here, we review different approaches that use classical post-processing techniques, possibly combined with adaptive optimization, to learn quantum systems, their correlation properties, dynamics and interaction with the environment. We discuss theoretical proposals and successful implementations across different multiple-qubit architectures such as spin qubits, trapped ions, photonic and atomic systems, and superconducting circuits. This Review provides a brief background of key concepts recurring across many of these approaches with special emphasis on the Bayesian formalism and neural networks.

Review. 20 pages, 4 figures and 2 boxes (reformatted)

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Cited by in corpus (75)

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• Entanglement-Based Quantum Information Technology
• Deep learning of quantum entanglement from incomplete measurements
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• Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
• Roadmap on Nanoscale Magnetic Resonance Imaging
• Shadow tomography on general measurement frames
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• Adaptive cold-atom magnetometry mitigating the trade-off between sensitivity and dynamic range
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• Gradient-descent methods for fast quantum state tomography
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• Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
• High-dimentional Multipartite Entanglement Structure Detection with Low Cost
• Noise Classification in Three-Level Quantum Networks by Machine Learning
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• Non-Hermitian Parent Hamiltonian from Generalized Quantum Covariance Matrix
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