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.

Harnessing DEN models for quantum computing tasks on neutral atom QPUs / Vercellino, Chiara; Vitali, Giacomo; Viviani, Paolo; Scionti, Alberto; Terzo, Olivier; Montrucchio, Bartolomeo. - ELETTRONICO. - (2024), pp. 217-222. (Intervento presentato al convegno 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) tenutosi a Montreal (CA) nel September 15–20, 2024) [10.1109/QCE60285.2024.10281].

Harnessing DEN models for quantum computing tasks on neutral atom QPUs

Vercellino, Chiara;Vitali, Giacomo;Viviani, Paolo;Scionti, Alberto;Montrucchio, Bartolomeo
2024

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.
2024
979-8-3315-4137-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2992948