Deep Learning of Quantum Many-Body Dynamics via Random Driving

Abstract

Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is \textit{based purely on monitoring expectation values of observables under random driving}. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state. This allows our approach to be applied eventually to actual experimental data generated from a quantum many-body system that might be open, noisy, or disordered, without any need for a detailed understanding of the system. This scheme provides considerable speedup for rapid explorations and pulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit.

References in corpus (35)

• Sequence to Sequence Learning with Neural Networks
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
• Machine learning phases of matter
• Evolution of Entanglement Entropy in One-Dimensional Systems
• Collapse and Revival of the Matter Wave Field of a Bose-Einstein Condensate
• NMR Techniques for Quantum Control and Computation
• Atomic quantum gases in periodically driven optical lattices
• Light-cone-like spreading of correlations in a quantum many-body system
• Many-body quantum state tomography with neural networks
• Real-time dynamics of lattice gauge theories with a few-qubit quantum computer
• Non-local propagation of correlations in long-range interacting quantum systems
• Learning phase transitions by confusion
• Discovering Phase Transitions with Unsupervised Learning
• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
• Controlled exchange interaction between pairs of neutral atoms in an optical lattice
• Efficient Representation of Quantum Many-body States with Deep Neural Networks
• Engineering Ising-XY spin models in a triangular lattice via tunable artificial gauge fields
• Active learning machine learns to create new quantum experiments
• Quantum simulation of time-dependent Hamiltonians and the convenient illusion of Hilbert space
• Quantum many-body dynamics in two dimensions with artificial neural networks
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Quantum quench in an atomic one-dimensional Ising chain
• Neural-Network Quantum States, String-Bond States, and Chiral Topological States
• A Neural Decoder for Topological Codes
• Machine learning vortices at the Kosterlitz-Thouless transition
• Robust Dynamic Hamiltonian Engineering of Many-Body Spin Systems
• Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
• Machine learning enables completely automatic tuning of a quantum device faster than human experts
• Quenches near Ising quantum criticality as a challenge for artificial neural networks
• Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
• Pulse engineering for population control under dephasing and dissipation
• Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning
• Rapid Exploration of Topological Band Structures using Deep Learning
• Robust and fast post-processing of single-shot spin qubit detection events with a neural network

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• Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
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• Theory of Moment Propagation for Quantum Dynamics in Single-Particle Description
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• Fourier Neural Operators for Time-Periodic Quantum Systems: Learning Floquet Hamiltonians, Observable Dynamics, and Operator Growth
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• A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes