Neutral atom platforms are analogue quantum simulators that offer the possibility to map graphs onto a 2D qubit register using programmable Rubidium atoms arrays, whosevalence 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.

Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification / Giusto, Edoardo; Iurlaro, Gabriele; Montrucchio, Bartolomeo; Scionti, Alberto; Terzo, Olivier; Vercellino, Chiara; Vitali, Giacomo; Viviani, Paolo. - ELETTRONICO. - (In corso di stampa). (Intervento presentato al convegno 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) tenutosi a Montreal (CA) nel September 15–20, 2024).

Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification

Giusto, Edoardo;Iurlaro, Gabriele;Montrucchio, Bartolomeo;Scionti, Alberto;Vercellino, Chiara;Vitali, Giacomo;Viviani, Paolo
In corso di stampa

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, whosevalence 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.
In corso di stampa
File in questo prodotto:
File Dimensione Formato  
output.pdf

non disponibili

Descrizione: Published paper
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 765.01 kB
Formato Adobe PDF
765.01 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2992950