Model predictive control for robust quantum state preparation
arXiv:2201.05266 · doi:10.22331/q-2022-10-13-837
Abstract
A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.
arXiv comment25 pages, 7 figures
References in corpus (35)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- A Quantum Engineer's Guide to Superconducting Qubits
- OSQP: An Operator Splitting Solver for Quadratic Programs
- Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
- Simple pulses for elimination of leakage in weakly nonlinear qubits
- Training Schrödinger's cat: quantum optimal control
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
- Quantum certification and benchmarking
- Modeling and Control of Quantum Systems: An Introduction
- Optimal quantum control using randomized benchmarking
- Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
- Koopman operator-based model reduction for switched-system control of PDEs
- Detecting crosstalk errors in quantum information processors
- Effective Hamiltonian models of the cross-resonance gate
- Gradient optimization of analytic controls: the route to high accuracy quantum optimal control
- Leakage reduction in fast superconducting qubit gates via optimal control
- Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers
- Theory of quantum system certification: a tutorial
- Adaptive hybrid optimal quantum control for imprecisely characterized systems
- Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
- Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
- Krotov: A Python implementation of Krotov's method for quantum optimal control
- Data-Driven Model Predictive Control using Interpolated Koopman Generators
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Optimal control theory for a unitary operation under dissipative evolution
- Deep Model Predictive Control with Online Learning for Complex Physical Systems
- Model-Based Control Using Koopman Operators
- Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
- Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
- High-fidelity software-defined quantum logic on a superconducting qudit
- Pulse-level noisy quantum circuits with QuTiP
- Accounting for Classical Hardware in the Control of Quantum Devices
- Bilinear dynamic mode decomposition for quantum control