Quantum state-preparation control in noisy environment via most-likely paths

• URL: http://arxiv.org/abs/2209.13164v2
• Date: Mon, 15 Jan 2024 14:58:45 GMT
• Title: Quantum state-preparation control in noisy environment via most-likely paths
• Authors: Wirawat Kokaew, Thiparat Chotibut, Areeya Chantasri
• Abstract summary: We consider an alternative view of a noise-affected open quantum system, where the average dynamics can be unravelled into hypothetical noisy trajectories. We adopt the most-likely path technique for quantum state-preparation, constructing a path for noise variables and finding control functions. As a proof of concept, we apply the method to a qubit-state preparation under a dephasing noise and analytically solve for controlled Rabi drives for arbitrary target states.
• Score: 1.9260081982051918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding optimal controls for open quantum systems needs to take into account effects from unwanted environmental noise. Since actual realizations or states of the noise are typically unknown, the usual treatment for the quantum system's decoherence dynamics is via the 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 new control strategy for the state-preparation problem based on the likelihood of noise occurrence. We adopt the most-likely path technique for quantum state-preparation, constructing a stochastic path integral for noise variables and finding control functions associated with the most-likely noise to achieve target states. As a proof of concept, we apply the method to a qubit-state preparation under a 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 new measure of the state preparation and benchmark our most-likely path controls against the existing mean-path approaches.

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