2015/04/30 by Joshua Combes, Christopher Ferrie · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Decision theory #Event (particle physics) #Focus (optics) #Mathematics #Postselection #Process (computing) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Scheme (mathematics) #Statistics #physics.data-an #quant-ph
paper · pdf · doi:10.1103/physreva.92.022117
published as Phys. Rev. A 92, 022117 (2015) · V1: 9 pages, 9 figures and one coffee stain. V2: new title and some minor improvements. One major change ... the coffee stain leaked further
openalex publication_date 2015/08/19 · arxiv created 2015/08/30 · arxiv updated 2015/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Postselection is the process of discarding outcomes from statistical trials that are not the event one desires. Postselection can be useful in many applications where the cost of getting the wrong event is implicitly high. However, unless this cost is specified exactly, one might conclude that discarding all data is optimal. Here we analyze the optimal decision rules and quantum measurements in a decision theoretic setting where a prespecified cost is assigned to discarding data. Our scheme interpolates between unambiguous state discrimination (when the cost of postselection is zero) and a minimum error measurement (when the cost of postselection is maximal). We also relate our formulation to previous approaches which focus on minimizing the probability of indecision.