Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918486028058624 |
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| author | Giusto, Edoardo Iurlaro, Gabriele Montrucchio, Bartolomeo Scionti, Alberto Terzo, Olivier Vercellino, Chiara Vitali, Giacomo Viviani, Paolo |
| author_facet | Giusto, Edoardo Iurlaro, Gabriele Montrucchio, Bartolomeo Scionti, Alberto Terzo, Olivier Vercellino, Chiara Vitali, Giacomo Viviani, Paolo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04737 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 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 Quantum Physics 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. |
| title | Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2605.04737 |