Reinforcement Learning for Digital Quantum Simulation

Abstract

Digital quantum simulation is a promising application for quantum computers. Their free programmability provides the potential to simulate the unitary evolution of any many-body Hamiltonian with bounded spectrum by discretizing the time evolution operator through a sequence of elementary quantum gates, typically achieved using Trotterization. A fundamental challenge in this context originates from experimental imperfections for the involved quantum gates, which critically limits the number of attainable gates within a reasonable accuracy and therefore the achievable system sizes and simulation times. In this work, we introduce a reinforcement learning algorithm to systematically build optimized quantum circuits for digital quantum simulation upon imposing a strong constraint on the number of allowed quantum gates. With this we consistently obtain quantum circuits that reproduce physical observables with as little as three entangling gates for long times and large system sizes. As concrete examples we apply our formalism to a long range Ising chain and the lattice Schwinger model. Our method makes larger scale digital quantum simulation possible within the scope of current experimental technology.

arXiv comment5 pages, 3 figures

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• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Quantum dynamics simulations beyond the coherence time on NISQ hardware by variational Trotter compression
• Optimal compression of quantum many-body time evolution operators into brickwall circuits
• Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning
• Quantum imaginary time evolution steered by reinforcement learning
• Quantum circuit synthesis with diffusion models
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Adaptive Trotterization for time-dependent Hamiltonian quantum dynamics using piecewise conservation laws
• Enhancing variational quantum state diagonalization using reinforcement learning techniques
• Machine Learning for Estimation and Control of Quantum Systems
• Entropy Regularized Reinforcement Learning Using Large Deviation Theory
• Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
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• Improved iterative quantum algorithm for ground-state preparation
• Unsupervised identification of Floquet topological phase boundaries
• Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization
• Time-Optimal Quantum Driving by Variational Circuit Learning
• Improving robustness of quantum feedback control with reinforcement learning
• Dynamical chaos in the integrable Toda chain induced by time discretization
• Weighted Quantum Channel Compiling through Proximal Policy Optimization
• Optimizing measurement-based cooling by reinforcement learning
• Trotter transition in BCS pairing dynamics
• An almost deterministic cooling by measurements