1Univ. of Calgary (Canada) 2ICFO - Institut de Ciències Fotòniques (Spain) 3Univ. of Boras (Sweden) 4Canadian Institute for Advanced Research (Canada) 5Univ. of Science and Technology of China (China)
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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 Cram´er- 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.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Pantita Palittpongarnpim, Peter Wittek, Barry C. Sanders, "Single-shot adaptive measurement for quantum-enhanced metrology," Proc. SPIE 9980, Quantum Communications and Quantum Imaging XIV, 99800H (13 September 2016); https://doi.org/10.1117/12.2237355