Learning models of quantum systems from experiments

• URL: http://arxiv.org/abs/2002.06169v1
• Date: Fri, 14 Feb 2020 18:37:50 GMT
• Title: Learning models of quantum systems from experiments
• Authors: Antonio A. Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher E. Granade, John G. Rarity, Raffaele Santagati, Anthony Laing
• Abstract summary: Hamiltonian models underpin the study and analysis of physical and chemical processes throughout science and industry. We propose and demonstrate an approach to retrieving a Hamiltonian model from experiments, using unsupervised machine learning.
• Score: 0.2740360306052669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An isolated system of interacting quantum particles is described by a Hamiltonian operator. Hamiltonian models underpin the study and analysis of physical and chemical processes throughout science and industry, so it is crucial they are faithful to the system they represent. However, formulating and testing Hamiltonian models of quantum systems from experimental data is difficult because it is impossible to directly observe which interactions the quantum system is subject to. Here, we propose and demonstrate an approach to retrieving a Hamiltonian model from experiments, using unsupervised machine learning. We test our methods experimentally on an electron spin in a nitrogen-vacancy interacting with its spin bath environment, and numerically, finding success rates up to 86%. By building agents capable of learning science, which recover meaningful representations, we can gain further insight on the physics of quantum systems.

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