Single-shot Adaptive Measurement for Quantum-enhanced Metrology
This work addresses the challenge of improving precision in quantum metrology through adaptive feedback, but it is incremental as it builds on existing adaptive approaches and does not surpass the standard quantum limit with non-entangled particles.
The paper tackled the problem of estimating an unknown parameter in quantum-enhanced metrology by developing a formal framework for single-shot adaptive quantum-enhanced metrology (AQEM), modeling it as a decision-making process and deriving imprecision and Cramér-Rao lower bounds; the result showed that applying a learning algorithm based on differential evolution achieved imprecision at the standard quantum limit (SQL) when using non-entangled particles in adaptive interferometric phase estimation.
Quantum-enhanced metrology aims to estimate an unknown parameter such that the precision scales better than the shot-noise bound. Single-shot adaptive quantum-enhanced metrology (AQEM) is a promising approach that uses feedback to tweak the quantum process according to previous measurement outcomes. Techniques and formalism for the adaptive case are quite different from the usual non-adaptive quantum metrology approach due to the causal relationship between measurements and outcomes. We construct a formal framework for AQEM by modeling the procedure as a decision-making process, and we derive the imprecision and the Cramér-Rao lower bound with explicit dependence on the feedback policy. We also explain the reinforcement learning approach for generating quantum control policies, which is adopted due to the optimal policy being non-trivial to devise. Applying a learning algorithm based on differential evolution enables us to attain imprecision for adaptive interferometric phase estimation, which turns out to be SQL when non-entangled particles are used in the scheme.