Instrumental distortions in quantum optimal control
Instrumental distortions in quantum optimal control
Quantum optimal control methods, such as gradient ascent pulse engineering (GRAPE), are used for precise manipulation of quantum states. Many of those methods were pioneered in magnetic resonance spectroscopy, where instrumental distortions are often negligible. However, that is not the case elsewhere: the usual jumble of cables, resonators, modulators, splitters, amplifiers, and filters can and would distort control signals. Those distortions may be non-linear; their inverse functions may be ill-defined and unstable; they may even vary from one day to the next and across the sample. Here we introduce the response-aware gradient ascent pulse engineering framework, which accounts for any cascade of differentiable distortions within the GRAPE optimization loop, does not require filter function inversion, and produces control sequences that are resilient to user-specified distortion cascades with user-specified parameter ensembles. The framework is implemented into the optimal control module supplied with versions 2.10 and later of the open-source Spinach library; the user needs to provide function handles returning the actions by the distortions and, optionally, parameter ensembles for those actions.
control theory, nuclear magnetic resonance spectroscopy
Rasulov, Uluk
c31a7c8c-3838-4357-833a-1aae8e119171
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065
28 April 2025
Rasulov, Uluk
c31a7c8c-3838-4357-833a-1aae8e119171
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065
Rasulov, Uluk and Kuprov, Ilya
(2025)
Instrumental distortions in quantum optimal control.
Journal of Chemical Physics, 162 (16), [164107].
(doi:10.1063/5.0264092).
Abstract
Quantum optimal control methods, such as gradient ascent pulse engineering (GRAPE), are used for precise manipulation of quantum states. Many of those methods were pioneered in magnetic resonance spectroscopy, where instrumental distortions are often negligible. However, that is not the case elsewhere: the usual jumble of cables, resonators, modulators, splitters, amplifiers, and filters can and would distort control signals. Those distortions may be non-linear; their inverse functions may be ill-defined and unstable; they may even vary from one day to the next and across the sample. Here we introduce the response-aware gradient ascent pulse engineering framework, which accounts for any cascade of differentiable distortions within the GRAPE optimization loop, does not require filter function inversion, and produces control sequences that are resilient to user-specified distortion cascades with user-specified parameter ensembles. The framework is implemented into the optimal control module supplied with versions 2.10 and later of the open-source Spinach library; the user needs to provide function handles returning the actions by the distortions and, optionally, parameter ensembles for those actions.
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164107_1_5.0264092
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Accepted/In Press date: 24 March 2025
Published date: 28 April 2025
Keywords:
control theory, nuclear magnetic resonance spectroscopy
Identifiers
Local EPrints ID: 502447
URI: http://eprints.soton.ac.uk/id/eprint/502447
ISSN: 0021-9606
PURE UUID: 50f56a95-27d0-4dc0-b734-eef86aa9f196
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Date deposited: 26 Jun 2025 16:59
Last modified: 22 Aug 2025 02:06
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Author:
Uluk Rasulov
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