Preparation of ordered states in ultra-cold gases using Bayesian optimization
Autor: | Robert Löw, Harry Xie, Frederic Sauvage, Florian Mintert, Rick Mukherjee |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Quantum decoherence Atomic Physics (physics.atom-ph) Fluids & Plasmas Degrees of freedom (statistics) General Physics and Astronomy FOS: Physical sciences Machine Learning (stat.ML) 01 natural sciences 010305 fluids & plasmas Physics - Atomic Physics Quantum state Statistics - Machine Learning 0103 physical sciences Statistical physics 010306 general physics Quantum Physics Condensed Matter::Quantum Gases Quantum Physics 02 Physical Sciences Bayesian optimization Probabilistic logic Optimal control Controllability Quantum Physics (quant-ph) |
Popis: | Ultra-cold atomic gases are unique in terms of the degree of controllability, both for internal and external degrees of freedom. This makes it possible to use them for the study of complex quantum many-body phenomena. However in many scenarios, the prerequisite condition of faithfully preparing a desired quantum state despite decoherence and system imperfections is not always adequately met. To path the way to a specific target state, we explore quantum optimal control framework based on Bayesian optimization. The probabilistic modeling and broad exploration aspects of Bayesian optimization is particularly suitable for quantum experiments where data acquisition can be expensive. Using numerical simulations for the superfluid to Mott-insulator transition for bosons in a lattice as well for the formation of Rydberg crystals as explicit examples, we demonstrate that Bayesian optimization is capable of finding better control solutions with regards to finite and noisy data compared to existing methods of optimal control. 29 pages, 10 figures |
Databáze: | OpenAIRE |
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