2011/04/19 by Alexander Hentschel, Barry C. Sanders · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Computer engineering #Computer science #Limit (mathematics) #Mathematics #Metrology #Neural Networks and Reservoir Computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Quantum decoherence #Quantum limit #Quantum mechanics #Quantum metrology #Quantum network #SQL #quant-ph
paper · pdf · doi:10.1103/physrevlett.107.233601
published as A. Hentschel and B. C. Sanders, An efficient algorithm for optimizing adaptive quantum metrology processes, Physical Review Letters 107(23): 233601 (4 pp.), 30 November 2011
arxiv created 2011/04/19 · openalex publication_date 2011/11/30 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.