2014/01/31 by Jun Zhang, Mohan Sarovar · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Blind Source Separation Techniques #Computer science #Constructive #Hamiltonian (control theory) #Mathematical optimization #Mathematics #Neural Networks and Reservoir Computing #Observable #Parameterized complexity #Physics #Process (computing) #Quantum #Quantum Information and Cryptography #Quantum mechanics #Quantum system #Robustness (evolution) #Statistical physics #Theoretical computer science #quant-ph
paper · pdf · doi:10.1103/physrevlett.113.080401
published as Phys. Rev. Lett., 113, 080401 (2014) · 5 pages + Supplementary Information. Modified estimation algorithm to be robust to measurement noise. New results on noise robustness. Published version
openalex publication_date 2014/08/18 · arxiv created 2014/08/26 · arxiv updated 2014/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Precise identification of parameters governing quantum processes is a critical task for quantum information and communication technologies. In this Letter, we consider a setting where system evolution is determined by a parametrized Hamiltonian, and the task is to estimate these parameters from temporal records of a restricted set of system observables (time traces). Based on the notion of system realization from linear systems theory, we develop a constructive algorithm that provides estimates of the unknown parameters directly from these time traces. We illustrate the algorithm and its robustness to measurement noise by applying it to a one-dimensional spin chain model with variable couplings.