Genetic Algorithm with Migration on Topology Conserving Maps for Optimal Control of Quantum Systems

Amstrup, Bjarne; Tóth, Gyula; Szabó, Gábor; Rabitz, Herschel; Lörincz, András

doi:10.1021/j100014a048

Cited by 40 publications

(33 citation statements)

References 9 publications

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“…Even for practical applications large populations can be feasible, e.g. in the context of massive parallelization or rapid serialization of experiments like in quantum control [14]. Therefore, the increased performance for larger population sizes of the proposed CMSA-ES has potential practical implications.…”

Section: Discussionmentioning

confidence: 99%

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

Beyer

Sendhoff

2008

Lecture Notes in Computer Science

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Abstract. The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or little theoretical substantiation is available. Furthermore, the CMA-ES does not work well for large population sizes. In this paper, we propose an alternative -simpler -adaptation step of the covariance matrix which is closer to the "traditional" mutative self-adaptation. We compare the newly proposed algorithm, which we term the CMSA-ES, with the CMA-ES on a number of different test functions and are able to demonstrate its superiority in particular for large population sizes.

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Section: Discussionmentioning

confidence: 99%

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

Beyer

Sendhoff

2008

Lecture Notes in Computer Science

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“…Typical representatives are evolutionary strategies [112] and genetic algorithms [113] as well as simulated annealing methods like the metropolis algorithm [85,116,117] or the threshold accepting method [118]. These algorithms are robust against noise [119,120], local sub-optimal solutions, and inaccuracy of input and output parameters [121]. Many more techniques relying on stochastic schemes have also been proposed [122,123].…”

Section: Evolutionary Algorithms Optimizationmentioning

confidence: 99%

“…A mapping between optimization parameters and experimental parameters which reflects the physical properties of the system [120] is useful in optimization experiments. If, for example, the resulting phase functions to be imposed on the pulses are expected to be chirps of various orders, it is appropriate to express the phase function as a Taylor series and to optimize the pertinent coefficients.…”

Section: The Role Of Parameterizationmentioning

confidence: 99%

Evolutionary algorithms and their application to optimal control studies

et al. 2001

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“…r n e iθn |ñ(t) (39) where the coefficients r n satisfy the normalization condition N n=1 r 2 n = 1, we need to find a unitary operatorÛ I such that…”

Section: Creation Of Arbitrary Superposition Statesmentioning

confidence: 99%

Constructive control of quantum systems using factorization of unitary operators

Schirmer

Greentree

Ramakrishna

et al. 2002

J. Phys. A: Math. Gen.

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Abstract. We demonstrate how structured decompositions of unitary operators can be employed to derive control schemes for finite-level quantum systems that require only sequences of simple control pulses such as square wave pulses with finite rise and decay times or Gaussian wavepackets. To illustrate the technique, it is applied to find control schemes to achieve population transfers for pure-state systems, complete inversions of the ensemble populations for mixed-state systems, create arbitrary superposition states and optimize the ensemble average of dynamic observables.

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Genetic Algorithm with Migration on Topology Conserving Maps for Optimal Control of Quantum Systems

Cited by 40 publications

References 9 publications

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

Evolutionary algorithms and their application to optimal control studies

Constructive control of quantum systems using factorization of unitary operators

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