Quantum state preparation control in noisy environment via most-likely paths
1
Issued Date
2026-01-01
Resource Type
ISSN
15700755
eISSN
15731332
Scopus ID
2-s2.0-105027450573
Journal Title
Quantum Information Processing
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Quantum Information Processing Vol.25 No.1 (2026)
Suggested Citation
Kokaew W., Chotibut T., Chantasri A. Quantum state preparation control in noisy environment via most-likely paths. Quantum Information Processing Vol.25 No.1 (2026). doi:10.1007/s11128-025-05034-8 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114717
Title
Quantum state preparation control in noisy environment via most-likely paths
Author(s)
Author's Affiliation
Corresponding Author(s)
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Abstract
Finding controls for open quantum systems needs to take into account effects from unwanted environmental noise. Since actual realisations or states of the noise are typically unknown, the usual treatment for the quantum system’s decoherence dynamics is via the so-called Lindblad master equation, which in essence describes an average evolution (mean path) of the system’s state affected by the unknown noise. We here consider an alternative view of a noise-affected open quantum system, where the average dynamics can be unravelled into hypothetical noisy quantum trajectories, and propose a control strategy for the state preparation problem based on the likelihood of noise occurrence. We formulate a stochastic path integral for noise variables whose extremum yields control functions associated with a most-likely noise to achieve target states. As a proof of concept, we apply our method to a qubit state preparation under dephasing noise and analytically solve for controlled Rabi drives for arbitrary target states. Since the method is constructed based on the probability of noise, we also introduce a fidelity success rate as a measure of the state preparation. We benchmark against the mean-path approaches, e.g., GRAPE and CRAB controls, using both average fidelity and a success rate metric. While standard mean-path controls maximise average fidelity, most-likely controls achieve higher success rates, especially at strong dephasing.