English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Finding optimal controls for open quantum systems needs to take into accounteffects from unwanted environmental noise. Since actual realizations or statesof the noise are typically unknown, the usual treatment for the quantumsystem's decoherence dynamics is via the Lindblad master equation, which inessence describes an average evolution (mean path) of the system's stateaffected by the unknown noise. We here consider an alternative view of anoise-affected open quantum system, where the average dynamics can beunravelled into hypothetical noisy quantum trajectories, and propose a newcontrol strategy for the state-preparation problem based on the likelihood ofnoise occurrence. We adopt the most-likely path technique for quantumstate-preparation, constructing a stochastic path integral for noise variablesand finding control functions associated with the most-likely noise to achievetarget states. As a proof of concept, we apply the method to a qubit-statepreparation under a dephasing noise and analytically solve for controlled Rabidrives for arbitrary target states. Since the method is constructed based onthe probability of noise, we also introduce a fidelity success rate as a newmeasure of the state preparation and benchmark our most-likely path controlsagainst the existing mean-path approaches.

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