Reinforcement Learning assisted Quantum Optimization

Abstract

We propose a reinforcement learning (RL) scheme for feedback quantum control within the quan-tum approximate optimization algorithm (QAOA). QAOA requires a variational minimization for states constructed by applying a sequence of unitary operators, depending on parameters living ina highly dimensional space. We reformulate such a minimum search as a learning task, where a RL agent chooses the control parameters for the unitaries, given partial information on the system. We show that our RL scheme finds a policy converging to the optimal adiabatic solution for QAOA found by Mbeng et al. arXiv:1906.08948 for the translationally invariant quantum Ising chain. In presence of disorder, we show that our RL scheme allows the training part to be performed on small samples, and transferred successfully on larger systems.

4 pages, 3 figures

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• Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond
• Model-Free Quantum Control with Reinforcement Learning
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Quantum optimization via four-body Rydberg gates
• Parameter Setting in Quantum Approximate Optimization of Weighted Problems
• Two-dimensional ${\mathbb{Z}}_2$ lattice gauge theory on a near-term quantum simulator: variational quantum optimization, confinement, and topological order
• Optimal quantum annealing: A variational shortcut to adiabaticity approach
• Quantum imaginary time evolution steered by reinforcement learning
• Reinforcement Learning Assisted Recursive QAOA
• An evolving objective function for improved variational quantum optimisation
• Quantum reinforcement learning in continuous action space
• Quantum Optimization for Training Quantum Neural Networks
• Quantum Logic Gate Synthesis as a Markov Decision Process
• Detection of entangled states supported by reinforcement learning
• Convergence of Digitized-Counterdiabatic QAOA: circuit depth versus free parameters
• Exploiting In-Constraint Energy in Constrained Variational Quantum Optimization
• Automatic Depth Optimization for Quantum Approximate Optimization Algorithm
• Machine Learning for Estimation and Control of Quantum Systems
• Robust optimization for quantum reinforcement learning control using partial observations
• Approximations in transmon simulation
• Beyond Quantum Annealing: Optimal control solutions to MaxCut problems
• Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
• Hard instance learning for quantum adiabatic prime factorization
• Maximising Quantum-Computing Expressive Power through Randomised Circuits
• Fast quantum gate design with deep reinforcement learning using real-time feedback on readout signals
• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
• Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
• Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
• Using machine learning for quantum annealing accuracy prediction
• Deep learning estimation of complex reverberant wave fields by a programmable metasurface
• Optimisation-Free Recursive QAOA for the Binary Paint Shop Problem
• Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions