Population Transfer in a Nitrogen-Vacancy Spin Qutrit via Shortcuts to Adiabaticity with Simplified Drivings

Yan, Ran; Feng, Zhi‐Bo

doi:10.1134/s0021364021180028

Cited by 1 publication

(1 citation statement)

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“…The rapidly evolving field of quantum information processing has generated much interest in quantum optimal control for tasks such as state preparation [1,2], error correction [3,4] and realization of logical gates [5][6][7][8]. This methodology has been implemented in various quantum systems [9], such as nuclear magnetic resonance [10][11][12][13][14][15], trapped ions [16,17], nitrogen-vacancy centers in diamonds [18][19][20][21][22][23][24], Bose-Einstein condensation [25][26][27] and neutral atoms [28,29].…”

Section: Introductionmentioning

confidence: 99%

Optimal control of large quantum systems: assessing memory and runtime performance of GRAPE

Lu,

Joshi,

San Dinh

et al. 2024

J. Phys. Commun.

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Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum optimal control, and can be combined with automatic differentiation (AD) to facilitate on-the-fly evaluation of cost-function gradients. We illustrate that the convenience of AD comes at a significant memory cost due to the cumulative storage of a large number of states and propagators. For quantum systems of increasing Hilbert space size, this imposes a significant bottleneck. We revisit the strategy of hard-coding gradients in a scheme that fully avoids propagator storage and significantly reduces memory requirements. Separately, we present improvements to numerical state propagation to enhance runtime performance. We benchmark runtime and memory usage and compare this approach to AD-based implementations, with a focus on pushing towards larger Hilbert space sizes. The results confirm that the AD-free approach facilitates the application of optimal control for large quantum systems which would otherwise be difficult to tackle.

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