Approaching the adiabatic timescale with machine learning

Abstract

Significance Engineering the fast evolution of a quantum system between states is a key problem to be solved in the development of quantum technologies, such as quantum computing. We experimentally demonstrate a general approach using a machine-learning algorithm that develops a model of the system, based on previous performance, to create further educated guesses on how to improve. Applied to a system similar to moving a cup of liquid between two locations (while blindfolded), the algorithm reaches a speed faster than that of previous approaches, dealing well with the complex dynamics and experimental imperfections present with its empirical approach. The resulting fast dynamics open the door to understanding how quantum mechanical systems reach equilibrium while the method provides a tool for taming complex quantum systems. 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 these inherently fragile systems experimentally. For example, while protocols to decompress Bose–Einstein condensates (BECs) 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 toward 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 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.