Related papers: Iterative shaping of optical potentials for one-di…
Discrete-time domain Iterative Learning Control (ILC) schemes inspired by Repetitive control algorithms are proposed and analyzed. The well known relation between a discrete-time plant (filter) and its Markov Toeplitz matrix representation…
We exploit the variation of the atomic interaction in order to move ultra-cold atoms across an AC-driven periodic lattice. By breaking relevant symmetries, a gathering of atoms is achieved. Accurate control of the gathered atoms positions…
This work develops a quantum control application of many-body quantum chaos for ultracold bosonic gases trapped in optical lattices. It is long known how to harness exponential sensitivity to changes in initial conditions for control…
The interplay between disorder and interactions is a "leit-motiv" of condensed matter physics, since it constitutes the driving mechanism of the metal-insulator transition. Bose-Einstein condensates in optical potentials are proving to be…
Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which are related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel…
We present theoretical as well as experimental results on resonantly enhanced quantum tunneling of Bose-Einstein condensates in optical lattices both in the linear case of single particle dynamics and in the presence of atom-atom…
To achieve Bose-Einstein condensation, one may implement evaporative cooling by dynamically regulating the power of laser beams forming the optical dipole trap. We propose and experimentally demonstrate a protocol of Bayesian optimization…
We propose a method to control the optical transparency of a Bose-Einstein condensate with working energy levels of the Lambda-type. The reported effects are essentially nonlinear and are considered in the framework of an exactly solvable…
Coherent optical information storage capacity of an atomic Bose-Einstein condensate is examined. Theory of slow light propagation in atomic clouds is generalized to short pulse regime by taking into account group velocity dispersion. It is…
In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon optimal control problem with output feedback. Previously, this…
Ultracold gases promise many applications in quantum metrology, simulation and computation. In this context, optimal control theory (OCT) provides a versatile framework for the efficient preparation of complex quantum states. However, due…
The theory of ultracold, dilute Bose gases is the subject of intensive studies, driven by new experimental applications, which also motivate the study of Bose-Einstein condensation (BEC) in low dimensions. From the theoretical point of view…
As robots and other automated systems are introduced to unknown and dynamic environments, robust and adaptive control strategies are required to cope with disturbances, unmodeled dynamics and parametric uncertainties. In this paper, we…
Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In…
We explore the phase diagram of a finite-sized dysprosium dipolar Bose-Einstein condensate in a cylindrical harmonic trap. We monitor the final state after the scattering length is lowered from the repulsive BEC regime to the quantum…
We consider quantum droplets in dipolar Bose-Einstein condensates (BECs) embedded in optical lattices within the framework of Gross-Pitaevskii equations. In dipolar BECs, the long-range and anistropic dipole-dipole interaction provides an…
Before AI and neural nets, the excitement was about iterative learning control (ILC): the idea to train robots to perform repetitive tasks or train a system to reject quasi-periodic disturbances. The excitement waned after the discovery of…
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task. The system is subject to an additive disturbance, and the goal is to learn to satisfy state and…
A variety of control tasks such as inverse kinematics (IK), trajectory optimization (TO), and model predictive control (MPC) are commonly formulated as energy minimization problems. Numerical solutions to such problems are well-established.…
An iterative learning algorithm is presented for continuous-time linear-quadratic optimal control problems where the system is externally symmetric with unknown dynamics. Both finite-horizon and infinite-horizon problems are considered. It…