{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/single-shot-adaptive-measurement-for-quantum","title":"Single-shot Adaptive Measurement for Quantum-enhanced Metrology","arxiv_id":"1608.06238","date":"2016-08-22","proceeding":null,"authors":["Pantita Palittapongarnpim","Peter Wittek","Barry C. Sanders"],"abstract":"Quantum-enhanced metrology aims to estimate an unknown parameter such that\nthe precision scales better than the shot-noise bound. Single-shot adaptive\nquantum-enhanced metrology (AQEM) is a promising approach that uses feedback to\ntweak the quantum process according to previous measurement outcomes.\nTechniques and formalism for the adaptive case are quite different from the\nusual non-adaptive quantum metrology approach due to the causal relationship\nbetween measurements and outcomes. We construct a formal framework for AQEM by\nmodeling the procedure as a decision-making process, and we derive the\nimprecision and the Cram\\'{e}r-Rao lower bound with explicit dependence on the\nfeedback policy. We also explain the reinforcement learning approach for\ngenerating quantum control policies, which is adopted due to the optimal policy\nbeing non-trivial to devise. Applying a learning algorithm based on\ndifferential evolution enables us to attain imprecision for adaptive\ninterferometric phase estimation, which turns out to be SQL when non-entangled\nparticles are used in the scheme.","url_abs":"http://arxiv.org/abs/1608.06238v1","url_pdf":"http://arxiv.org/pdf/1608.06238v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"single-shot-adaptive-measurement-for-quantum","repo_url":"https://github.com/PanPalitta/phase_estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}