Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification

TL;DR

This paper demonstrates the use of a 256-qubit neutral atom quantum simulator to extract graph features for classification, showing improved performance over classical methods even with noise.

Contribution

It presents the first implementation of a quantum evolution kernel on a large neutral atom platform for graph classification tasks.

Findings

Quantum evolution kernel achieved slightly better classification accuracy than classical kernels.

The method is effective even when simulated on a noisy quantum platform.

Successfully applied to the PROTEINS dataset using AWS-accessible hardware.

Abstract

Neutral atom platforms are analogue quantum simulators that offer the possibility to map graphs onto a 2D qubit register using programmable Rubidium atoms arrays, whose valence electrons' energy state is used as qubits, using optical tweezers. This makes it possible to implement algorithms for solving graph combinatorial optimization and Quantum Machine Learning (QML) tasks, such as graph classification. However, the restrictions of real hardware, as well as the very low number of publicly available machines, make such implementation non-trivial. In this work, we manage to compute the Quantum Evolution Kernel (QEK) to extract the features from graphs of the PROTEINS dataset using the 256-qubits Aquila platform (available through AWS) and then we apply classical Machine Learning (ML) techniques for the final classification. The method is benchmarked against classical kernels, resulting in slightly better performance, proving the effectiveness of the method, even in the case of a noisy quantum simulator.