Machine learning design of a trapped-ion quantum spin simulator. (21st January 2020)

• Record Type: Journal Article
• Authors: Teoh, Yi Hong; Drygala, Marina; Melko, Roger G; Islam, Rajibul
• Is Part Of: Quantum science and technology. Volume 5:Number 2(2020)
• Publication Date: 2020-01-21
• Subjects: trapped ions -- quantum simulation -- machine learning -- deep learning; Quantum theory -- Periodicals; Quantum theory; Periodicals; 530
• DOI: 10.1088/2058-9565/ab657a
• Languages: English
• ISSNs: 2058-9565

Abstract

Abstract: Trapped ions have emerged as one of the highest quality platforms for the quantum simulation of interacting spin models of interest to various fields of physics. In such simulators, two effective spins can be made to interact with arbitrary strengths by coupling to the collective vibrational or phonon states of ions, controlled by precisely tuned laser beams. However, the task of determining laser control parameters required for a given spin–spin interaction graph is a type of inverse problem, which can be highly mathematically complex. In this paper, we adapt a modern machine learning technique developed for similar inverse problems to the task of finding the laser control parameters for a number of interaction graphs. We demonstrate that typical graphs, forming regular lattices of interest to physicists, can easily be produced for up to 50 ions using a single GPU workstation. The scaling of the machine learning method suggests that this can be expanded to hundreds of ions with moderate additional computational effort.