Using Random Walks for Iterative Phase Estimation

Understand

In recent years there has been substantial development in algorithms for quantum phase estimation.

• In this work we provide a new approach to online Bayesian phase estimation that achieves Heisenberg limited scaling that requires exponentially less classical processing time with the desired error tolerance than existing Bayesian methods.
• This practically means that we can perform an update in microseconds on a CPU as opposed to milliseconds for existing particle filter methods.
• Our approach assumes that the prior distribution is Gaussian and exploits the fact, when optimal experiments are chosen, the mean of the prior distribution is given by the position of a random walker whose moves are dictated by the measurement outcomes.