Stochastic optimization for learning quantum state feedback control

Abstract

High fidelity state preparation represents a fundamental challenge in the application of quantum technology. While the majority of optimal control approaches use feedback to improve the controller, the controller itself often does not incorporate explicit state dependence. Here, we present a general framework for training deep feedback networks for open quantum systems with quantum nondemolition measurement that allows a variety of system and control structures that are prohibitive by many other techniques and can in effect react to unmodeled effects through nonlinear filtering. We demonstrate that this method is efficient due to inherent parallelizability, robust to open system interactions, and outperforms landmark state feedback control results in simulation.

arXiv comment8 pages, 3 figures, 9 pages supplement

References in corpus (30)

• Laser cooling of a nanomechanical oscillator into its quantum ground state
• QuTiP: An open-source Python framework for the dynamics of open quantum systems
• Optimal Control at the Quantum Speed Limit
• Quantum feedback control and classical control theory
• Path integrals and symmetry breaking for optimal control theory
• Quantum Brachistochrone
• Continuous quantum error correction via quantum feedback control
• Quantum non-demolition measurement of a superconducting two-level system
• Time Minimal Trajectories for a Spin 1/2 Particle in a Magnetic Field
• Optimal Control-Based Efficient Synthesis of Building Blocks of Quantum Algorithms Seen in Perspective from Network Complexity towards Time Complexity
• Optimal Control of Spin Dynamics in the Presence of Relaxation
• Stabilization of Ultracold Molecules Using Optimal Control Theory
• Optimal Control of Coupled Josephson Qubits
• Quantum control of bosonic modes with superconducting circuits
• How to project qubits faster using quantum feedback
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Adaptive importance sampling for control and inference
• Controlled-phase Gate using Dynamically Coupled Cavities and Optical Nonlinearities
• Nondemolition Principle of Quantum Measurement Theory
• Protecting coherence in Optimal Control Theory: State dependent constraint approach
• Optimal quantum control of Bose-Einstein condensates in magnetic microtraps: Comparison of GRAPE and Krotov optimization schemes
• Steering the optimization pathway in the control landscape using constraints
• Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
• Learning Deep Stochastic Optimal Control Policies using Forward-Backward SDEs
• Geometric analysis of minimum time trajectories for a two-level quantum system
• Controlled-Phase Gate by Dynamic Coupling of Photons to a Two-Level Emitter
• Solving quantum trajectories for systems with linear Heisenberg-picture dynamics and Gaussian measurement noise
• Quantum Pontryagin Principle under Continuous Measurements and Feedback
• Twenty open problems in quantum control
• NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control