Machine learning design of a trapped-ion quantum spin simulator

TL;DR

This paper applies machine learning to efficiently determine laser control parameters for simulating complex spin interactions in trapped-ion quantum systems, enabling scalable quantum simulations of larger ion arrays.

Contribution

It introduces a machine learning approach to solve the inverse problem of designing laser controls for arbitrary spin interaction graphs in trapped-ion quantum simulators.

Findings

Able to produce interaction graphs for up to 50 ions on a single GPU

Method scales to hundreds of ions with moderate computational effort

Facilitates scalable quantum simulation of complex spin models

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.