Optimizing qubit Hamiltonian parameter estimation algorithm using PSO

2012/06/01 by Alexandr Sergeevich, Stephen D. Bartlett
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Computer science #Estimation theory #Hamiltonian (control theory) #Mathematical optimization #Mathematics #Neural Networks and Reservoir Computing #Particle swarm optimization #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum mechanics #Qubit #quant-ph

paper · pdf · doi:10.1109/cec.2012.6252948

published as Proceedings of 2012 IEEE Conference on Evolutionary Computation (CEC), 10-15 June 2012 · 3 pages, 2 figures, presented at 2012 IEEE Congress on Evolutionary Computation, to be published in the proceedings

openalex publication_date 2012/06/01 · arxiv created 2012/06/18 · arxiv updated 2012/10/17 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05

Abstract

In this paper we develop qubit Hamiltonian single parameter estimation techniques using Bayesian approach. The algorithms considered are restricted to projective measurements in a fixed basis, and are derived under the assumption that the qubit measurement is much slower than the characteristic qubit evolution. The non-adaptive algorithm is optimized using particle swarm optimization and compared to previously developed locally-optimal scheme.

Citations

• Complete Characterization of a Quantum Process: The Two-Bit Quantum Gate
• Machine Learning for Precise Quantum Measurement
• Identifying an experimental two-state Hamiltonian to arbitrary accuracy
• Entanglement-free Heisenberg-limited phase estimation
• Quantum Algorithms Revisited
• Characterization of a qubit Hamiltonian using adaptive measurements in a fixed basis
• The particle swarm - explosion, stability, and convergence in a multidimensional complex space