2011/04/30 by Marcus P. da Silva, Olivier Landon-Cardinal, David Poulin · 8 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Characterization (materials science) #Computer science #Fidelity #Mathematics #Medical physics #Optics #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum information #Quantum mechanics #Quantum state #Quantum tomography #Reduction (mathematics) #Statistical physics #Tomography #quant-ph
paper · pdf · doi:10.1103/physrevlett.107.210404
published as Phys. Rev. Lett. 107, 210404 (2011) · (v1) 11 pages, 1 table, 4 figures. (v2) See also the closely related work: arXiv:1104.4695 (v3) method extended to continuous variable systems (v4) updated to published version
openalex publication_date 2011/11/16 · arxiv created 2011/11/18 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Quantum tomography is the main method used to assess the quality of quantum information processing devices. However, the amount of resources needed for quantum tomography is exponential in the device size. Part of the problem is that tomography generates much more information than is usually sought. Taking a more targeted approach, we develop schemes that enable (i) estimating the fidelity of an experiment to a theoretical ideal description, (ii) learning which description within a reduced subset best matches the experimental data. Both these approaches yield a significant reduction in resources compared to tomography. In particular, we demonstrate that fidelity can be estimated from a number of simple experiments that is independent of the system size, removing an important roadblock for the experimental study of larger quantum information processing units.