Stochastic optimization of a cold atom experiment using a genetic algorithm

Abstract

We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces the automatic optimization outperforms a manual search.

4 pages, 3 figures

References in corpus (6)

• Matter-wave interferometry in a double well on an atom chip
• Non-equilibrium coherence dynamics in one-dimensional Bose gases
• Squeezing and entanglement in a Bose-Einstein condensate
• Optimized U-MOT for experiments with ultracold atoms near surfaces
• Atom Chips: Fabrication and Thermal Properties
• Multi-layer atom chips for versatile atom micro manipulation

Cited by in corpus (18)

• Fast machine-learning online optimization of ultra-cold-atom experiments
• Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle
• Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists
• Non-equilibrium scale invariance and shortcuts to adiabaticity in a one-dimensional Bose gas
• Vibrational state inversion of a Bose-Einstein condensate: optimal control and state tomography
• Experimental demonstration of shaken lattice interferometry
• Optimal control of superconducting gmon qubits using Pontryagin's minimum principle: preparing a maximally entangled state with singular bang-bang protocols
• Applying machine learning optimization methods to the production of a quantum gas
• Cooling through optimal control of quantum evolution
• An atom interferometer with a shaken optical lattice
• Approaching the adiabatic timescale with machine-learning
• A single atom detector integrated on an atom chip: fabrication, characterization and application
• Evolutionary optimization of an experimental apparatus
• Simplified landscapes for optimization of shaken lattice interferometry
• Reducing number fluctuations of ultracold atomic gases via dispersive interrogation
• Optimizing Quantum Gas Production by an Evolutionary Algorithm
• Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
• Topological and geometric patterns in optimal bang-bang protocols for variational quantum algorithms: application to the $XXZ$ model on the square lattice