Optimizing qubit Hamiltonian parameter estimation algorithms using PSO

Abstract

We develop qubit Hamiltonian single parameter estimation techniques using a 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. We optimize a non-adaptive algorithm using particle swarm optimization (PSO) and compare with a previously-developed locally-optimal scheme.

3 pages, 2 figures, presented at 2012 IEEE Congress on Evolutionary Computation, to be published in the proceedings

References in corpus (3)

• Entanglement-free Heisenberg-limited phase estimation
• Universal quantum control of two-electron spin quantum bits using dynamic nuclear polarization
• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy

Cited by in corpus (1)

• Robust Online Hamiltonian Learning