Multi-Axis Control of a Qubit in the Presence of Unknown Non-Markovian Quantum Noise
arXiv:2208.03058 · doi:10.1088/2058-9565/aca711
Abstract
In this paper, we consider the problem of open-loop control of a qubit that is coupled to an unknown fully quantum non-Markovian noise (either bosonic or fermionic). A graybox model that is empirically obtained from measurement data is employed to approximately represent the unknown quantum noise. The estimated model is then used to calculate the open-loop control pulses under constraints on the pulse amplitude and timing. For the control pulse optimization, we explore the use of gradient descent and genetic optimization methods. We consider the effect of finite sampling on estimating expectation values of observables and show results for single- and multi-axis control of a qubit.
References in corpus (7)
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- Model-Free Quantum Control with Reinforcement Learning
- Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Optimal control of quantum thermal machines using machine learning
- Quantum-tailored machine-learning characterization of a superconducting qubit
- Quantum Geometric Machine Learning for Quantum Circuits and Control