Hybrid classical–quantum algorithms aim to variationally solve optimization problems using
a feedback loop between a classical computer and a quantum co-processor, while …

We apply an online optimization process based on machine learning to the production of
Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation …

We use Pontryagin's minimum principle to optimize variational quantum algorithms. We
show that for a fixed computation time, the optimal evolution has a bang-bang (square …

We apply three machine learning strategies to optimize the atomic cooling processes
utilized in the production of a Bose–Einstein condensate (BEC). For the first time, we …

We introduce a remote interface to control and optimize the experimental production of Bose–
Einstein condensates (BECs) and find improved solutions using two distinct …

We experimentally demonstrate a shaken-lattice interferometer. Atoms are trapped in the
ground Bloch state of a red-detuned optical lattice. Using a closed-loop optimization protocol …

The control and manipulation of quantum systems without excitation are challenging, due to
the complexities in fully modeling such systems accurately and the difficulties in controlling …

We present experimental evidence for scale invariant behaviour of the excitation spectrum in
phase-fluctuating quasi-1d Bose gases after a rapid change of the external trap** …

We present theoretical and experimental results on high-fidelity transfer of a trapped Bose–
Einstein condensate into its first vibrationally excited eigenstate. The excitation is driven by …

We apply the theory of optimal control to the dynamics of two “gmon” qubits, with the goal of
preparing a desired entangled ground state from an initial unentangled one. Given an initial …