Gradient Ascent Pulse Engineering with Feedback
arXiv:2203.04271 · doi:10.1103/PRXQuantum.4.030305
Abstract
Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully solved using optimization techniques, including methods like gradient-ascent pulse engineering (GRAPE), relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. In this work, we introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power considering various scenarios based on cavity QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multi-qubit devices to linear-optics quantum computation strategies, quantum-enhanced sensing with adaptive measurements, and quantum error correction.
arXiv comment31 pages, 16 figures
References in corpus (51)
- Barren plateaus in quantum neural network training landscapes
- Circuit Quantum Electrodynamics
- Encoding a qubit in an oscillator
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- Training Schrödinger's cat: quantum optimal control
- Quantum jumps of light recording the birth and death of a photon in a cavity
- Connecting ansatz expressibility to gradient magnitudes and barren plateaus
- Real-time quantum feedback prepares and stabilizes photon number states
- Feedback-control of quantum systems using continuous state-estimation
- Quantum feedback control of a superconducting qubit: Persistent Rabi oscillations
- Progressive field-state collapse and quantum non-demolition photon counting
- An initialization strategy for addressing barren plateaus in parametrized quantum circuits
- Quantum-assisted quantum compiling
- High fidelity spin entanglement using optimal control
- Implementing a Universal Gate Set on a Logical Qubit Encoded in an Oscillator
- Reinforcement Learning in Different Phases of Quantum Control
- Second order gradient ascent pulse engineering
- Controlling open quantum systems: Tools, achievements, and limitations
- Quantum feedback: theory, experiments, and applications
- Comparing, Optimising and Benchmarking Quantum Control Algorithms in a Unifying Programming Framework
- Continuous quantum error correction via quantum feedback control
- Repeated quantum error correction on a continuously encoded qubit by real-time feedback
- Differentiable Programming Tensor Networks
- Reinforcement Learning with Neural Networks for Quantum Feedback
- Silicon qubit fidelities approaching incoherent noise limits via pulse engineering
- Gradient optimization of analytic controls: the route to high accuracy quantum optimal control
- Universal Control of an Oscillator with Dispersive Coupling to a Qubit
- Optimal Control-Based Efficient Synthesis of Building Blocks of Quantum Algorithms Seen in Perspective from Network Complexity towards Time Complexity
- Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
- Large gradients via correlation in random parameterized quantum circuits
- Model-Free Quantum Control with Reinforcement Learning
- Adaptive hybrid optimal quantum control for imprecisely characterized systems
- Speedup for quantum optimal control from automatic differentiation based on graphics processing units
- Optimal Control for Generating Quantum Gates in Open Dissipative Systems
- Optimal Control of Coupled Josephson Qubits
- Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
- Learning Robust and High-Precision Quantum Controls
- An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
- Entanglement Stabilization using Parity Detection and Real-Time Feedback in Superconducting Circuits
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Coherent Transport of Quantum States by Deep Reinforcement Learning
- Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
- Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
- A Data-Driven Gradient Algorithm for High-Precision Quantum Control
- Universal gates for protected superconducting qubits using optimal control
- A differentiable programming method for quantum control
- Resonator reset in circuit QED by optimal control for large open quantum systems
- Optimal control of circuit quantum electrodynamics in one and two dimensions
- Variational quantum algorithm with information sharing
- Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
- Control of Stochastic Quantum Dynamics by Differentiable Programming
Cited by in corpus (20)
- Introduction to Theoretical and Experimental aspects of Quantum Optimal Control
- Artificial Intelligence for Quantum Computing
- GRAPE optimization for open quantum systems with time-dependent decoherence rates driven by coherent and incoherent controls
- Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications
- Supervised learning for robust quantum control in composite-pulse systems
- Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
- Non-Markovian feedback for optimized quantum error correction
- Controlling NMR spin systems for quantum computation
- Optimal control in large open quantum systems: the case of transmon readout and reset
- Preparing Schrödinger cat states in a microwave cavity using a neural network
- Stabilizing two-qubit entanglement with dynamically decoupled active feedback
- Entanglement engineering of optomechanical systems by reinforcement learning
- Applications of model-aware reinforcement learning in Bayesian quantum metrology
- Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
- Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation
- Application of machine learning to experimental design in quantum mechanics
- Quantum feedback control with a transformer neural network architecture
- Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
- Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse
- CDJ-Pontryagin Optimal Control for General Continuously Monitored Quantum Systems