Presentation + Paper
13 September 2016 Single-shot adaptive measurement for quantum-enhanced metrology
Author Affiliations +
Abstract
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.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pantita Palittpongarnpim, Peter Wittek, and 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
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Particles

Metrology

Optimization (mathematics)

Evolutionary algorithms

Interferometry

Phase interferometry

Quantum information

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