One decade of quantum optimal control in the chopped random basis

Abstract

The Chopped RAndom Basis (CRAB) ansatz for quantum optimal control has been proven to be a versatile tool to enable quantum technology applications such as quantum computing, quantum simulation, quantum sensing, and quantum communication. Its capability to encompass experimental constraints – while maintaining an access to the usually trap-free control landscape – and to switch from open-loop to closed-loop optimization (including with remote access – or RedCRAB) is contributing to the development of quantum technology on many different physical platforms. In this review article we present the development, the theoretical basis and the toolbox for this optimization algorithm, as well as an overview of the broad range of different theoretical and experimental applications that exploit this powerful technique.

Controlling the uncontrollable: Quantum control of open-system dynamics

“…The control scheme used to evaluate the optimal field is a simplified version of the Chopped RAndom Basis set optimization (CRAB) algorithm ( 28 , 29 ). For each task, a cost function was defined (see below).…”

“…The advantage of preselecting a fixed pallet of control frequencies in Eq. 12 is that they can be chosen to fit experimental constraints ( 29 ).…”

“…In the present study, the chosen algorithm used for optimal control was of the CRAB type (Eq. 12) ( 29 ). This method uses only initial- and final-state information.…”

Specialty Grand Challenge: Quantum engineering

“…At a highest level of complexity we find the overwhelming family of quantum optimal control methods. This is a field that has grown in ambition, starting from original works that aimed at controlling isolated states with arbitrary pulses-e.g., those provided by Krotov methods (Tannor et al, 1992)-, evolving to work with open systems (Koch, 2016), and incorporate new technologies that aim for experimentally-friendly controls with limited bandwidths (Romero-Isart and García-Ripoll, 2007;Motzoi et al, 2011;Koch, 2016;Machnes et al, 2018;Müller et al, 2022). In this context one must highlight the development of new paradigms in the field of control theory, covered by the umbrella term of shortcuts to adiabaticity (Guéry-Odelin et al, 2019).…”

An analog quantum variational embedding classifier

“…The Fourier expansion waveform ansatz of the control fields used for quantum optimal control is an example of chopped random basis (CRAB) [25,26]. Previous studies on CRAB show the optimization landscape of CRAB ansatz is good for trainability [25,26], and as few as three Fourier terms in the Fourier ansatz are enough for a good performance [25,26].…”