Neutral-atom quantum processors can embed molecular graphs natively through the geometry of atom arrays, which makes them a natural platform for graph-based quantum kernels. Existing quantum evolution kernels, however, encode limited chemical information and have been applied to molecular classification but not to regression. Here we introduce ChemQEK, a quantum evolution kernel framework for molecular property prediction on analog neutral-atom quantum processors. ChemQEK encodes atom-specific descriptors through local addressability and atom-pair London dispersion coefficients (
C6ij) into the parameters of the Rydberg Hamiltonian, and it combines global, local and pairwise observables of the evolved state within a single kernel. We further introduce kernel pooling strategies that combine information across evolution times and addressability schemes. Using noiseless emulation, we evaluate ChemQEK with kernel-based regression models on the ESOL, FreeSolv, Jean-Claude Bradley Open Melting Point, and QM9 datasets. Pooling over both evolution times and addressability schemes outperforms pooling over either alone and yields test
R2 values of 0.90, 0.84, and 0.69 for ESOL, FreeSolv, and melting point, respectively. On QM9, ChemQEK outperforms the quantum evolution kernel and generalized-distance quantum-correlation frameworks, each of which relies on a single observable type, for all eight properties considered. The QM9 experiments also reveal property dependent trade-offs between predictive performance and the number of quantum feature configurations, with up to $\sim$79\% fewer measurements retaining competitive performance for several properties. These results highlight the importance of chemically informed encoding, complementary observables, and multidimensional kernel pooling for quantum kernel-based molecular property prediction.
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.
Gaussian boson samplers (GBSs) have initially been proposed as a near-term demonstration of classically intractable quantum computation. We show here that they have a potential practical application: Samples from these devices can be used to construct a feature vector that embeds a graph in Euclidean space, where similarity measures between graphs---so-called graph kernels---can be naturally defined. This is crucial for machine learning with graph-structured data, and we show that the GBS-induced kernel performs remarkably well in classification benchmark tasks. We provide a theoretical motivation for this success, linking the extracted features to the number of
r matchings in subgraphs. Our results contribute to a new way of thinking about kernels as a quantum hardware-efficient feature mapping, and lead to a promising application for near-term quantum computing.
Quantum kernel methods are a promising branch of quantum machine learning, but their per- formance across data types and encoding choices remains incompletely understood. To address this gap, we developed an algorithmic framework for variational quantum kernels utilizing resource- efficient ansätze for complex classification tasks and introduced a parameter scaling technique to accelerate convergence. We conducted a comprehensive benchmark of this framework on eight chal- lenging, real-world and high-dimensional datasets covering tabular, image, time series, and graph data. Our results show that the proposed quantum kernel demonstrated competitive classification accuracy compared to standard classical kernels in classical simulation, such as the radial basis function (RBF) kernel. This work demonstrates that properly designed quantum kernels can func- tion as versatile, high-performance tools, laying a foundation for quantum-enhanced applications in machine learning. Further research is needed to fully assess the practical performance of quantum methods.
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.