Machine-learning-accelerated Bose-Einstein condensation

Abstract

Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of ${}^{87}\mathrm{Rb}$ atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of $2.8\times 10^3$ optically trapped ${}^{87}\mathrm{Rb}$ atoms from a room-temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach.

arXiv comment9 pages, 5 figures + supplemental material

References in corpus (24)

• Theory of Bose-Einstein condensation in trapped gases
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Discovering Phase Transitions with Unsupervised Learning
• Fast machine-learning online optimization of ultra-cold-atom experiments
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Fast, Runaway Evaporative Cooling to Bose-Einstein Condensation in Optical Traps
• Solving the Bose-Hubbard model with machine learning
• Production of quantum degenerate strontium gases: Larger, better, faster, colder
• Multiparameter optimisation of a magneto-optical trap using deep learning
• A high-flux BEC source for mobile atom interferometers
• Continuous Bose-Einstein condensation
• Direct laser cooling to Bose-Einstein condensation in a dipole trap
• Rapid Cooling to Quantum Degeneracy in Dynamically Shaped Atom Traps
• Machine Learning for Achieving Bose-Einstein Condensation of Thulium Atoms
• A scripted control system for autonomous hardware timed experiments
• Machine learning assisted readout of trapped-ion qubits
• Eliminating light shifts in single-atom optical traps
• Applying machine learning optimization methods to the production of a quantum gas
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
• Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods
• Deep Learning-Assisted Classification of Site-Resolved Quantum Gas Microscope Images
• Active Learning Approach to Optimization of Experimental Control

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• High-dimensional reinforcement learning for optimization and control of ultracold quantum gases
• All-optical production of Bose-Einstein condensates with 2 Hz repetition rate
• Bayesian Optimization for Robust State Preparation in Quantum Many-Body Systems
• Assessing small accelerations using a bosonic Josephson junction
• Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
• Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap
• Bose-Einstein condensation by polarization gradient laser cooling
• Multimode feedback cooling of the collective modes of a Bose-Einstein condensate
• Efficient production of sodium Bose-Einstein condensates in a hybrid trap