2014/07/31 by Markus Tiersch, M. Tiersch, E. J. Ganahl +2 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computation #Computer science #Controller (irrigation) #Field (mathematics) #Mathematics #Neural Networks and Reservoir Computing #Noise (video) #Path (computing) #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #quant-ph
paper · pdf · doi:10.1038/srep12874
published as Sci. Rep. 5, 12874 (2015) · 15 pages, 13 figures
openalex publication_date 2015/08/11 · arxiv created 2015/08/31 · arxiv updated 2015/09/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent's learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent's performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover's search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks.