2014
DOI: 10.1088/1367-2630/16/12/125004
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Assessment of optimal control mechanism complexity by experimental landscape Hessian analysis: fragmentation of CH2BrI

Abstract: Optimally shaped femtosecond laser pulses can often be effectively identified in adaptive feedback quantum control experiments, but elucidating the underlying control mechanism can be a difficult task requiring significant additional analysis. We introduce landscape Hessian analysis (LHA) as a practical experimental tool to aid in elucidating control mechanism insights. This technique is applied to the dissociative ionization of CH 2 BrI using shaped fs laser pulses for optimization of the absolute yields of i… Show more

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Cited by 16 publications
(16 citation statements)
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“…In all cases, the ionizations are determined to be single-particle processes, with the absence of important (2h1p) amplitudes in the EOM-IP case. The ionization energies are in good agreement with experiment 18,[138][139][140][141][142][143][144][145] , with typical differences being of the order of 0.1 eV. Such differences are quite far from what are the best experimental error bars avaliable 142 , which are well under 0.05 eV for both species, but given that our calculations have not been performed at the experimental structures 146 but rather on PBEoptimized ones, and still lack corrections due to basis set completeness-and probably more importantly, of higherorder electron correlation effects-we consider this accuracy to be sufficient for the purposes of this paper.…”
Section: Dihalomethanessupporting
confidence: 81%
See 1 more Smart Citation
“…In all cases, the ionizations are determined to be single-particle processes, with the absence of important (2h1p) amplitudes in the EOM-IP case. The ionization energies are in good agreement with experiment 18,[138][139][140][141][142][143][144][145] , with typical differences being of the order of 0.1 eV. Such differences are quite far from what are the best experimental error bars avaliable 142 , which are well under 0.05 eV for both species, but given that our calculations have not been performed at the experimental structures 146 but rather on PBEoptimized ones, and still lack corrections due to basis set completeness-and probably more importantly, of higherorder electron correlation effects-we consider this accuracy to be sufficient for the purposes of this paper.…”
Section: Dihalomethanessupporting
confidence: 81%
“…Theoretical approaches based on molecular quantum mechanics [1][2][3][4][5] have grown into increasingly important tools to help experimentalists understand species in their electronically excited states in the gas-phase 6 or in complex environments [7][8][9] , and with that address speciation (oxidation states of specific centers, structures) [10][11][12][13][14][15] as well as the underlying factors driving photochemical processes (reactivity, photodissociation etc.) [16][17][18][19][20] .…”
Section: Introductionmentioning
confidence: 99%
“…In 'Reduced coupling with global pulses in quantum registers' [11], Yuan et al show that the complexity of dynamical decoupling schemes can be significantly reduced by the use of global control pulses. In related work with an emphasis on reaction mechanisms for molecular fragmentation, Xing et al show in 'Assessment of optimal control mechanism complexity by experimental landscape Hessian analysis: fragmentation of CH 2 BrI' [12] how to analyze and minimize the complexity of an optimal control procedure by landscape Hessian analysis, thereby gaining additional insight into the control mechanism.…”
Section: Advances In Theoretical Methodologymentioning
confidence: 99%
“…for efficiently seeking robust quantum controls, and numerical results showed that b-GRAPE can achieve improved performance over the SLC method for remarkably enhancing the control robustness while maintaining high fidelity [30]. In other applications where we need to enhance the robustness in closed-loop learning control, we may either use the Hessian matrix information [31] or integrate the idea of SLC into the learning algorithm in searching for robust control fields.…”
Section: Learning-based Quantum Robust Controlmentioning
confidence: 99%