Approaching the adiabatic timescale with machine-learning

TL;DR

This paper demonstrates a machine learning approach to control quantum systems, specifically Bose-Einstein condensates, achieving faster-than-adiabatic decompression experimentally without detailed theoretical models.

Contribution

The authors introduce an experimental machine learning method that optimizes quantum control protocols, surpassing previous theoretical speed limits in BEC decompression.

Findings

Achieved the fastest decompression of BECs faster than the adiabatic timescale.

Machine learning algorithm converges to an effective control protocol through iterative experimentation.

Method can be applied to other quantum systems for rapid state preparation.

Abstract

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.