Chopped random-basis quantum optimization
By T. Caneva, Tommaso Calarco, Simone Montangero. 2011
364 citations recorded by the source corpus.
In this work, we describe in detail the chopped random basis (CRAB) optimal control technique recently introduced to optimize time-dependent density matrix renormalization group simulations [P. Doria, T. Calarco, and S. Montangero, Phys. Rev. Lett. 106, 190501 (2011)]. Here, we study the efficiency of this control technique in optimizing different quantum processes and we show that in the considered cases we obtain results equivalent to those obtained via different optimal control methods while using less resources. We propose the CRAB optimization as a general and versatile optimal control technique.
Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics
View Paper: https://www.semanticscholar.org/paper/646de84de21c0a9b0a7d35aef7f94aecb6838e5e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marin Bukov
- Thomas Schulte-Herbrüggen
- Pankaj Mehta
- A. Polkovnikov
- Alexandre G. R. Day
- Ronnie Kosloff
- Simone Montangero
- Gunther Dirr
In Atomic and Molecular Physics, and Optics
- Ronnie Kosloff
- Pankaj Mehta
- Dries Sels
- A. Polkovnikov
- Phillip Weinberg
- Marin Bukov
- Thomas Schulte-Herbrüggen
- Simone Montangero
Citations in the corpus
- Reinforcement Learning in Different Phases of Quantum Control (2017, methodology): “In contrast, optimal control methods, such as CRAB [30], usually have a much more rigid framework, where the d.”
- Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe (2022, background): “optimization) [111, 195, 476], gradient-free approaches are an interesting and effi-”
- Training Schrödinger’s cat: quantum optimal control (2015, methodology): “(ii) Gradient-free optimization: For many-body quantum systems, the chopped random basis (CRAB) method uses a parametrization of the control and interfaces the tensor compression of the time-dependent density matrix renormalization group with parameter optimization [69, 107].”