2019/04/12 by Fernando Martínez-García, F. Martínez-García, Davide Vodola +3
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Inference #Observable #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum mechanics #Qubit #Statistical physics #quant-ph
paper · pdf · doi:10.1088/1367-2630/ab5c51
published as New J. Phys. 21 123027 (2019) · 23 pages including appendices, 11 color figures
arxiv created 2019/04/12 · openalex publication_date 2019/11/27 · arxiv updated 2020/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Abstract Realisation of experiments even on small and medium-scale quantum computers requires an optimisation of several parameters to achieve high-fidelity operations. As the size of the quantum register increases, the characterisation of quantum states becomes more difficult since the number of parameters to be measured grows as well and finding efficient observables in order to estimate the parameters of the model becomes a crucial task. Here we propose a method relying on application of Bayesian inference that can be used to determine systematic, unknown phase shifts of multi-qubit states. This method offers important advantages as compared to Ramsey-type protocols. First, application of Bayesian inference allows the selection of an adaptive basis for the measurements which yields the optimal amount of information about the phase shifts of the state. Secondly, this method can process the outcomes of different observables at the same time. This leads to a substantial decrease in the resources needed for the estimation of phases, speeding up the state characterisation and optimisation in experimental implementations. The proposed Bayesian inference method can be applied in various physical platforms that are currently used as quantum processors.