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

Ofer M. Shir, Jonathan Roslund, Zaki Leghtas, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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 highdimensional 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.

Original languageEnglish (US)
Pages (from-to)445-491
Number of pages47
JournalGenetic Programming and Evolvable Machines
Issue number4
StatePublished - Dec 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications


  • Covariance matrix adaptation
  • Diffraction grating
  • Experimental pareto optimization
  • Multi-objective evolution strategies
  • Quantum Control experiments
  • Robustness to noise


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