2018/08/06 by Ignacio Perito, Augusto J. Roncaglia, Augusto Roncaglia +1 · 10 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Computer science #Dimension (graph theory) #Discrete mathematics #Hilbert space #Low-power high-performance VLSI design #Mathematics #Measure (data warehouse) #Mutually unbiased bases #Open quantum system #Physics #Pure mathematics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum mechanics #Quantum operation #Quantum state #Quantum tomography #SIC-POVM #quant-ph
paper · pdf · doi:10.1103/physreva.98.062303
published in Physical Review A 98(6) (American Physical Society) · 8 pages, 3 figures
arxiv created 2018/08/06 · openalex publication_date 2018/12/03 · arxiv updated 2018/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The characterization of quantum processes is a key tool in quantum information processing tasks for several reasons: on one hand, it allows one to acknowledge errors in the implementations of quantum algorithms; on the other, it allows one to characterize unknown processes occurring in nature. Bendersky, Pastawski, and Paz [A. Bendersky, F. Pastawski, and J. P. Paz, Phys. Rev. Lett. 100, 190403 (2008); Phys. Rev. A 80, 032116 (2009)] introduced a method to selectively and efficiently measure any given coefficient from the matrix description of a quantum channel. However, this method heavily relies on the construction of maximal sets of mutually unbiased bases (MUBs), which are known to exist only when the dimension of the Hilbert space is the power of a prime number. In this article, we lift the requirement on the dimension by presenting two variations of the method that work on arbitrary finite dimensions: one uses tensor products of maximal sets of MUBs, and the other uses a dimensional cutoff of a higher prime power dimension.