Journal article
Francesco Pio Barone, Oriel Kiss, Michele Grossi, Sofia Vallecorsa, Antonio Mandarino
New Journal of Physics, vol. 26, 2024 Mar, p. 033031
New Journal of Physics, vol. 26, 2024 Mar, p. 033031
arXiv
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APA
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Barone, F. P., Kiss, O., Grossi, M., Vallecorsa, S., & Mandarino, A. (2024). Counterdiabatic optimized driving in quantum phase sensitive models. New Journal of Physics, 26, 033031. https://doi.org/10.1088/1367-2630/ad313e
Chicago/Turabian
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Barone, Francesco Pio, Oriel Kiss, Michele Grossi, Sofia Vallecorsa, and Antonio Mandarino. “Counterdiabatic Optimized Driving in Quantum Phase Sensitive Models.” New Journal of Physics 26 (March 2024): 033031.
MLA
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Barone, Francesco Pio, et al. “Counterdiabatic Optimized Driving in Quantum Phase Sensitive Models.” New Journal of Physics, vol. 26, Mar. 2024, p. 033031, doi:10.1088/1367-2630/ad313e.
BibTeX Click to copy
@article{barone2024a,
title = {Counterdiabatic optimized driving in quantum phase sensitive models},
year = {2024},
month = mar,
journal = {New Journal of Physics},
pages = {033031},
volume = {26},
doi = {10.1088/1367-2630/ad313e},
author = {Barone, Francesco Pio and Kiss, Oriel and Grossi, Michele and Vallecorsa, Sofia and Mandarino, Antonio},
month_numeric = {3}
}
This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by phase transitions: the axial next-nearest neighbor Ising (ANNNI), XXZ, and Haldane-Shastry (HS) models. We perform quantum optimal control protocols by optimizing the energy cost function. This protocol consistently surpasses standard annealing schedules, often achieving performance improvements of several orders of magnitude. Notably, the ANNNI model stands out as a notable example, where fidelities exceeding 0.5 are attainable in most cases. Furthermore, the optimized paths exhibits promising generalization capabilities to higher-dimensional systems, allowing for the extension of parameters from smaller models. This opens up possibilities for applying the protocol to higher-dimensional systems.
