Quantum control experiments as a testbed for evolutionary multi-objective algorithms

Experimental multi-objective Quantum Control is an emerging topic within the broad physics and chemistry applications domain of controlling quantum phenomena. This realm offers cutting edge ultrafast laser laboratory applications, which pose multiple objectives, noise, and possibly constraints on th...

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Vydáno v:Genetic programming and evolvable machines Ročník 13; číslo 4; s. 445 - 491
Hlavní autoři: Shir, Ofer M., Roslund, Jonathan, Leghtas, Zaki, Rabitz, Herschel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.12.2012
Témata:
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Electrical Engineering
Interpreters
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Diffraction grating
Multi-objective evolution strategies
Experimental Pareto optimization
Quantum control experiments
Covariance matrix adaptation
Robustness to noise
ISSN:1389-2576, 1573-7632
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Shrnutí:Experimental multi-objective Quantum Control is an emerging topic within the broad physics and chemistry applications domain of controlling quantum phenomena. This realm offers cutting edge ultrafast laser laboratory applications, which pose multiple objectives, noise, and possibly constraints on the high-dimensional search. In this study we introduce the topic of multi-observable quantum control (MOQC), and consider specific systems to be Pareto optimized subject to uncertainty, either experimentally or by means of simulated systems. The latter include a family of mathematical test-functions with a practical link to MOQC experiments, which are introduced here for the first time. We investigate the behavior of the multi-objective version of the covariance aatrix adaptation evolution strategy (MO-CMA-ES) and assess its performance on computer simulations as well as on laboratory closed-loop experiments. Overall, we propose a comprehensive study on experimental evolutionary Pareto optimization in high-dimensional continuous domains, draw some practical conclusions concerning the impact of fitness disturbance on algorithmic behavior, and raise several theoretical issues in the broad evolutionary multi-objective context.
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-012-9164-7