Seminar: Timothy Evans, University of Sydney
Scalable Bayesian learning of local Hamiltonians
As the size of intermediate scale quantum devices continues to grow, the development of scalable methods to characterise and diagnose noisy devices is becoming an increasingly important problem.
Image: Shahadat Shemul/Unsplash
Presenter: Timothy Evans, University of Sydney
Title: Scalable Bayesian learning of local Hamiltonians
Abstract: As the size of intermediate scale quantum devices continues to grow, the development of scalable methods to characterise and diagnose noisy devices is becoming an increasingly important problem.
Recent results demonstrate how a local Hamiltonian can be reconstructed with knowledge of a single, arbitrary eigenstate (or Gibbs state) and with a number of measurements that scales linearly in the size of the system. These methods, however, can only characterise a Hamiltonian up to scalar factor and lack sufficient robustness to noise, both of which are imperative to be of practical use.
In this talk, I will present a Bayesian method that addresses both of these issues by making use of the following: experimental control, the preparation of multiple states and the availability of prior information of the Hamiltonian couplings.
Hosted by: Centre for Quantum Software and Information
