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 (9)

• Quantum Algorithms Revisited
• Prescription for experimental determination of the dynamics of a quantum black box
• Complete Characterization of a Quantum Process: the Two-Bit Quantum Gate
• Entanglement-free Heisenberg-limited phase estimation
• Universal quantum control of two-electron spin quantum bits using dynamic nuclear polarization
• Suppressing Spin Qubit Dephasing by Nuclear State Preparation
• Machine Learning for Precise Quantum Measurement
• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
• Characterization of a qubit Hamiltonian using adaptive measurements in a fixed basis

Cited by in corpus (3)

• Robust Online Hamiltonian Learning
• Machine learning \& artificial intelligence in the quantum domain
• Quantum computation of molecular structure using data from challenging-to-classically-simulate nuclear magnetic resonance experiments