Stochastic learning control of inhomogeneous quantum ensembles
This work addresses robustness in quantum control for applications like quantum computing, but it is incremental as it adapts existing stochastic methods to a known bottleneck.
The paper tackled the problem of robust quantum control under parameter uncertainty by testing stochastic search procedures (Stochastic Gradient Descent and Adam) that sample from uncertainty distributions instead of using fixed grids, showing good performance in benchmarks and successfully implementing high-dimensional cases up to 6D.
In quantum control, the robustness with respect to uncertainties in the system's parameters or driving field characteristics is of paramount importance and has been studied theoretically, numerically and experimentally. We test in this paper stochastic search procedures (Stochastic gradient descent and the Adam algorithm) that sample, at each iteration, from the distribution of the parameter uncertainty, as opposed to previous approaches that use a fixed grid. We show that both algorithms behave well with respect to benchmarks and discuss their relative merits. In addition the methodology allows to address high dimensional parameter uncertainty; we implement numerically, with good results, a 3D and a 6D case.