"Estimation of Quantum Observables using Bayesian methods"
Abstract: One of the most important tasks in quantum information and computing is the accurate estimation of quantum observables. However, due to the statistical nature of quantum mechanics, repeated measurements are required for this task. As the size of the quantum hardware increases, this becomes challenging for protocols that depend on sampling, for example variational quantum algorithms. This talk will have two parts. In the first part, I will discuss some methods that use classical pre and post-processing to reduce the number of measurements needed or obtain to more information using a fix number of measurements. In the second part, I will focus on the statistical inference aspect of the problem. Here, I will discuss the limitations of Maximum Likelihood Estimation when the number of measurements is small. Finally, I will introduce a Bayesian approach that is robust and supports the estimate with well-motivated error bars.
Bio: Rajesh Mishra is a 3rd year physics graduate student working with Bryan Clark and Patrick Draper on quantum algorithms and simulation.