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arXiv:2605.04737 · IEEE International Conference on Quantum Computing and Engineering (QCE)

Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification

Edoardo Giusto, Gabriele Iurlaro, Bartolomeo Montrucchio, Alberto Scionti, Olivier Terzo, Chiara Vercellino, Giacomo Vitali, Paolo Viviani

quant-phSubmitted May 6, 2026• Announced May 7, 2026• 1 citation
arXivHTMLPDF

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.

Journal reference
2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. p. 296-305
DOI
10.1109/QCE60285.2024.00043

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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.
Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classically, a high qubit connectivity is needed, which is not currently possible on superconducting devices. For this reason, neutral atom quantum computers can be used, since they allow to arrange the atoms with more freedom. Examples of neutral-atom-based QKE can be found in the literature, but they are focused on graph learning and use the analogue approach. In this paper, a general method based on the gate model is presented. After deriving 1-qubit and 2-qubit gates starting from laser pulses, a parameterized sequence for feature mapping on 3 qubits is realized. This sequence is then used to empirically compute the kernel matrix starting from a dataset, which is finally used to train the SVM. It is also shown that this process can be generalized up toN\mathrm{N}Nqubits taking advantage of the more flexible arrangement of atoms that this technology allows. The accuracy is shown to be high despite the small dataset and the low separation. This is the first paper that not only proposes an algorithm for explicitly deriving a universal set of gates but also presents a method of estimating quantum kernels on neutral atom devices for general problems using the gate model.
The rapid development of reliable quantum processing units opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the similarity between graph-structured data, based on the time evolution of a quantum system. By encoding the topology of the input graph in the Hamiltonian of the system, the evolution produces measurement samples that retain key features of the data. We study analytically the procedure and illustrate its versatility in providing links to standard classical approaches. We then show numerically that this scheme performs well compared to standard graph kernels on typical benchmark datasets. Finally, we study the possibility of a concrete implementation on a realistic neutral-atom quantum processor.
We extend the quantum-feature kernel framework, which relies on measurements of graph-dependent observables, along three directions. First, leveraging neutral-atom quantum processing units (QPUs), we introduce a scheme that incorporates attributed graphs by embedding edge features into atomic positions and node features into local detuning fields of a Rydberg Hamiltonian. We demonstrate both theoretically and empirically that local detuning enhances kernel expressiveness. Second, in addition to the existing quantum evolution kernel (QEK), which uses global observables, we propose the generalized-distance quantum-correlation (GDQC) kernel, based on local observables. While the two kernels show comparable performance, we show that GDQC can achieve higher expressiveness. Third, instead of restricting to observables at single time steps, we combine information from multiple stages of the quantum evolution via pooling operations. Using extensive simulations on two molecular benchmark datasets, MUTAG and PTC\_FM, we find: (a) QEK and GDQC perform competitively with leading classical algorithms; and (b) pooling further improves performance, enabling quantum-feature kernels to surpass classical baselines. These results show that node-feature embedding and kernel designs based on local observables advance quantum-enhanced graph machine learning on neutral-atom devices.
Neutral-atom quantum devices offer a natural and powerful platform for Quantum Reservoir Computing (QRC), a non-variational approach to quantum-enhanced machine learning that is attracting growing interest in the Noisy Intermediate-Scale Quantum (NISQ) era. In this work, we implement a QRC-based learning model tailored to Pasqal neutral-atom hardware, where classical data are encoded into time-dependent detuning waveforms and processed through programmable many-body dynamics. Feature embeddings are extracted from single- and two-qubit Pauli-ZZZ observables and used within a simple linear readout model. We systematically investigate both global and local detuning encoding strategies on a supervised image classification task, benchmarking performance against a purely classical linear baseline. Our results show that processing the data through the quantum reservoir yields a consistent $\sim$4\% improvement in accuracy, highlighting the system effectiveness as a nonlinear feature map. Both encoding strategies exhibit comparable performance, suggesting that QRC can leverage different control schemes while remaining compatible with near-term and emerging hardware capabilities. These findings provide concrete evidence that QRC can enhance classical machine learning pipelines by injecting non-classical correlations without the need for variational optimization, thereby avoiding issues such as barren plateaus and training instabilities. More broadly, this work establishes neutral-atom platforms as a promising and scalable setting for QRC, opening the way toward practical quantum-enhanced learning on near-term devices.
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.