Quantum approximate Bayesian computation for NMR model inference.

ABSTRACT: Recent technological advances may lead to the development of small scale quantum computers capable of solving problems that cannot be tackled with classical computers. A limited number of algorithms has been proposed and their relevance to real world problems is a subject of active investigation. Analysis of many-body quantum system is particularly challenging for classical computers due to the exponential scaling of Hilbert space dimension with the number of particles. Hence, solving problems relevant to chemistry and condensed matter physics are expected to be the first successful applications of quantum computers. In this paper, we propose another class of problems from the quantum realm that can be solved efficiently on quantum computers: model inference for nuclear magnetic resonance

SUBMITTER: Sels D 

PROVIDER: S-EPMC7643990 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature