Bayesian estimation for collisional thermometry and time-optimal holonomic quantum computation
Gabriel O. Alves

TL;DR
This thesis introduces a Bayesian framework for collisional quantum thermometry, enhancing temperature estimation precision, and explores non-adiabatic holonomic quantum gates, balancing speed and decoherence effects.
Contribution
It develops a practical Bayesian approach for collisional thermometry that saturates the Cramér-Rao bound and analyzes the optimal conditions for holonomic quantum gates in three-level systems.
Findings
Bayesian thermometry framework saturates the Cramér-Rao bound in long-time limit.
Analysis of prior information impacts on temperature estimation accuracy.
Identification of optimal regimes balancing RWA validity and decoherence for quantum gates.
Abstract
In this thesis we deal with two different topics. In the first half we investigate how the Bayesian formalism can be introduced into the problem of quantum thermometry -- a field which exploits the high level of control in coherent devices to offer enhanced precision for temperature estimation. In particular, we investigate concrete estimation strategies, with focus on collisional thermometry, a protocol where a series of ancillae are sent sequentially to probe the system's temperature. We put forth a complete framework for analyzing collisional thermometry using Bayesian inference. The approach is easily implementable and experimentally friendly. Moreover, it is guaranteed to always saturate the Cram\'er-Rao bound in the long-time limit. Subtleties concerning the prior information about the system's temperature are also discussed and analyzed in terms of a modified Cram\'er-Rao bound…
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Taxonomy
TopicsSpectroscopy and Quantum Chemical Studies · Spectroscopy and Laser Applications · Laser-Matter Interactions and Applications
