Robust quantum control using hybrid pulse engineering

Ram, M. Harshanth; Krithika, V.; Batra, Priya; Mahesh, T. S.

doi:10.1103/physreva.105.042437

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…In this section, we provide a concise overview of two methods utilized in this work: the gradient ascent pulse engineering (GRAPE) method [23,29] and the simulated annealing GRAPE (SAGRAPE) method [44,45].…”

Section: Methodsmentioning

confidence: 99%

“…According to a prior work [44], the SAGRAPE method with varying numbers of cooling steps κ significantly improves the convergence efficiency in both state and gate control tasks. In this section, we demonstrate the convergence of the GRAPE and SAGRAPE methods for the hypothesis testing problem, as discussed in the main text.…”

Section: Data Availability Statementsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum hypothesis testing via robust quantum control

Xu,

Wang,

Yuan

et al. 2023

New J. Phys.

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Quantum hypothesis testing plays a pivotal role in quantum technologies, making decisions or drawing conclusions about quantum systems based on observed data. Recently, quantum control techniques have been successfully applied to quantum hypothesis testing, enabling the reduction of error probabilities in the task of distinguishing magnetic fields in presence of environmental noise. In real-world physical systems, such control is prone to various channels of inaccuracies. Therefore improving the robustness of quantum control in the context of quantum hypothesis testing is crucial. In this work, we utilize optimal control methods to compare scenarios with and without accounting for the effects of signal frequency inaccuracies. For parallel dephasing and spontaneous emission, the optimal control inherently demonstrates a certain level of robustness, while in the case of transverse dephasing with an imperfect signal, it may result in a higher error probability compared to the uncontrolled scheme. To overcome these limitations, we introduce a robust control approach optimized for a range of signal noise, demonstrating superior robustness beyond the predefined tolerance window. On average, both the optimal control and robust control show improvements over the uncontrolled schemes for various dephasing or decay rates, with the robust control yielding the lowest error probability.

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