Quantum Filtering and Optimal Control

Prateek Tandon, Stanley Lam, Ben Shih +3 more

Simon Edwards, V. P. Belavkin

The multi-dimensional quality open linear dynamical system with observation and feedback along a quantum linear transmission line is studied in discrete time. The linear least squares filtering and optimal control strategies are obtained as quantum analogies of the Kalman filter and Bellman dynamical programming. The duality of quantum filtering and optimal feedback control is observed for this particular case.

Georg Jäger, Ulrich Hohenester

We theoretically investigate protocols based on optimal control theory (OCT) for manipulating Bose-Einstein condensates in magnetic microtraps, using the framework of the Gross-Pitaevskii equation. In our approach we explicitly account for filter functions that distort the computed optimal control, a situation inherent to many experimental OCT implementations. We apply our scheme to the shakeup process of a condensate from the ground to the first excited state, following a recent experimental and theoretical study, and demonstrate that the fidelity of OCT protocols is not significantly deteriorated by typical filters.

Hirmay Sandesara, Alok Shukla, Prakash Vedula

Isabel Nha Minh Le, Julian D. Teske, Tobias Hangleiter +2 more

Autocorrelated noise appears in many solid-state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of autocorrelated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly 2 orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.