Enhancing real-time parameters estimation precision by quantum jump-based feedback control

H S Smitha Rao

Chang Wang, Mao-Fa Fang

Jincheng Lu, Chen Wang

Feedback can convert continuously monitored quantum jumps into a thermodynamic resource. We formulate full counting statistics for open quantum systems under unital jump feedback by incorporating the feedback maps into the tilted generator. The resulting trajectory ensemble determines both current fluctuations and the Fisher information of the measurement record. We show that feedback can enhance reservoir-parameter estimation and clock precision without necessarily changing average thermodynamic currents. This enhanced precision is not bounded by reservoir entropy production alone. By embedding the reduced dynamics in an enlarged measurement-feedback process, we derive a feedback-modified thermodynamic uncertainty relation in which the information entropy production of the feedback apparatus supplies the missing cost. A charge-monitored double quantum dot illustrates the framework: jump-conditioned feedback improves thermometry and chemical-potential sensing, and stabilizes a quantum clock defined by output-current ticks.

Yue Tian, Xiujuan Lu, Sen Kuang +1 more

In this paper, we consider the real-time parameter estimation problem for a ZZ-coupled system composed of two qubits in the presence of spontaneous emission. To enhance the estimation precision of the coupling coefficient, we first propose two different control strategies, where the first one is feedback control based on quantum-jump detection, and the second one is hybrid control combining Markovian feedback and Hamiltonian control. The simulation results show that compared with free evolution, both control strategies can improve parameter precision and extend system coherence time. Then, on the basis of the two control strategies, we propose a practical single-parameter quantum recovery protocol, which includes the preparation of an optimal initial probe state, the active control and optimization of a system evolution process, the design of an adaptive measurement scheme, and the parameter estimation algorithm based on Bayesian estimation theory. In particular, by employing a batch-style adaptive measurement scheme, the parameter recovery is conducted and the effectiveness of the two control strategies is verified.

M. R. James, Robert L. Kosut

James James