Harnessing DEN models for quantum computing tasks on neutral atom QPUs

TL;DR

This work demonstrates effective embedding of complex graphs into neutral atom quantum computers using Distance Encoder Networks, enabling applications in quantum machine learning and combinatorial problems.

Contribution

The paper introduces a method combining DEN with machine-specific adjustments to embed large graphs into neutral atom QPUs, achieving high success rates.

Findings

Embedded up to 76% of protein graphs on Aquila QPU.

Successfully embedded all antenna subgraphs on Orion Alpha QPU.

Applied hybrid quantum-classical algorithms to solve graph coloring problems.

Abstract

We present our work on effectively representing unit-disk graphs on the registers of neutral atom quantum machines. Specifically, we aimed to embed graphs corresponding to proteins and cellular antenna networks into unit-disk graphs, ensuring compatibility with the registers of two real QPUs: Orion Alpha by PASQAL and Aquila by QuEra. To address machine-specific constraints, we made adjustments and integrated Distance Encoder Networks (DEN) from our previous work. Despite these challenges, we successfully embedded up to 76% of protein-representing graphs for a quantum machine learning classification task on the Aquila QPU, and all subgraphs derived from 90 antenna geographical positions in Turin, Italy, on the Orion Alpha QPU. In the latter case, the graphs represented instances of the graph coloring problem, which we tackled using the hybrid quantum-classical algorithm BBQ-mIS. These promising results underscore the effectiveness and versatility of our embedding approach for representing unit-disk graphs on neutral atom quantum computers across diverse applications.