  The control and manipulation of quantum systems without excitation is
challenging, due to the complexities in fully modeling such systems accurately
and the difficulties in controlling these inherently fragile systems
experimentally. For example, while protocols to decompress Bose-Einstein
condensates (BEC) faster than the adiabatic timescale (without excitation or
loss) have been well developed theoretically, experimental implementations of
these protocols have yet to reach speeds faster than the adiabatic timescale.
In this work, we experimentally demonstrate an alternative approach based on a
machine learning algorithm which makes progress towards this goal. The
algorithm is given control of the coupled decompression and transport of a
metastable helium condensate, with its performance determined after each
experimental iteration by measuring the excitations of the resultant BEC. After
each iteration the algorithm adjusts its internal model of the system to create
an improved control output for the next iteration. Given sufficient control
over the decompression, the algorithm converges to a novel solution that sets
the current speed record in relation to the adiabatic timescale, beating out
other experimental realizations based on theoretical approaches. This method
presents a feasible approach for implementing fast state preparations or
transformations in other quantum systems, without requiring a solution to a
theoretical model of the system. Implications for fundamental physics and
cooling are discussed.
