Adaptive quantum state tomography with iterative particle filtering
Several Bayesian estimation based heuristics have been developed to perform
quantum state tomography (QST). Their ability to quantify uncertainties using
region estimators and include a priori knowledge of the experimentalists makes
this family of methods an attractive choice for QST. However, specialized
techniques for pure states do not work well for mixed states and vice versa. In
this paper, we present an adaptive particle filter (PF) based QST protocol
which improves the scaling of fidelity compared to nonadaptive Bayesian schemes
for arbitrary multi-qubit states. This is due to the protocol's unabating
perseverance to find the states's diagonal bases and more systematic handling
of enduring problems in popular PF methods relating to the subjectivity of
informative priors and the invalidity of particles produced by resamplers.
Numerical examples and implementation on IBM quantum devices demonstrate
improved performance for arbitrary quantum states and the application readiness
of our proposed scheme.
Syed Muhammad Kazim Ahmad Farooq Junaid ur Rehman Hyundong Shin