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Quantum evolution kernel for molecular machine learning on programmable neutral-atom arrays

Saiyam Sakhuja, Victor Drouin-Touchette, Viki Kumar Prasad

Published Oct 27, 2026• 0 citations
Publisher

Abstract

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 ($C^{ij}_6$) 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 $R^2$ 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.

DOI
10.26434/chemrxiv.15011111/v1

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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 (C6ijC^{ij}_6C6ij​) 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 R2R^2R2 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.
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.
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.
Predicting molecular properties is a cornerstone of drug discovery, materials science, and quantum chemistry, where accuracy and speed are critical. Classical machine learning mod- els offer rapid predictions but often fail to capture the intricate quantum interactions governing molecular behavior. Our team investigated quantum kernel methods to predict properties of the QM9 dataset, a widely recognized benchmark containing134,000\mathbf{1 3 4, 0 0 0}134,000organic molecules with diverse chemical structures. By encoding molecular features into quantum circuits, we mapped classical data into highdimensional Hilbert spaces to enhance prediction accuracy. Our pipeline encompasses data preprocessing, quantum circuit design, model training with support vector regression, and evaluation of ten key molecular properties. The quantum kernel approach delivered promising results, achieving mean absolute errors of 0.13 Debye for dipole moment and 0.08 eV for HOMOLUMO gap, even when executed on noisy quantum devices. This work provides a comprehensive, reproducible pipeline, benchmarks quantum and classical models, and discusses practical challenges, contributing significantly to the field of quantumenhanced computational chemistry.
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.
Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability. This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] with Low-Rank Adaptation (LoRA) fine-tuning [4]. Across 38 systematically varied subsets of the QM9 benchmark [5,6], MQPA achieves a mean absolute error (MAE) of 0.03 kcal/mol, outperforming both deep learning and quantum kernel baselines by 41% and surpassing the chemical accuracy threshold of 0.1 kcal/mol [7–9]. Extensive cross-platform benchmarking—including IBM Quantum, Qiskit Aer, and NVIDIA H100 clusters—demonstrates strong reproducibility, with inter-backend MAE variance below 3%. Positioned as a scalable and hardware-aware solution, MQPA offers practical advantages for quantum-enhanced molecular property prediction across applications in quantum chemistry, drug discovery, materials design, and environmental modeling.
Recent advances in quantum machine learning (QML) offer new opportunities for chemical property prediction, yet their practical performance relative to established classical models remains poorly characterized on real-world datasets. Building on our previous benchmark of classical and deep learning models for collision cross-section (CCS) predictions, we present an empirical comparison of quantum kernel regression (QKR) and a variational quantum regressor (VQR) against classical machine learning (CML) techniques, including Random Forest (RF), support vector regression (SVR), Lasso, and a Voting Regressor (VR) ensemble. Using an aquatic dissolved organic matter (DOM) dataset with five molecular descriptors and five chemical classes, we systematically investigate QKR feature map repetitions and classical readout kernels, as well as VQR circuit depths and optimizer choices, to identify performance-optimal QML configurations. Regression performance is evaluated using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) across training-set sizes up to 600 molecules per class. Overall, RF and Lasso deliver the most robust CCS predictions, achieving R2 ≥ 0.9 for lignins, carbohydrates, lipids, and proteins. Meanwhile, the optimized QKR architecture exhibits substantial gains as data increase and achieves competitive performance on chemically homogeneous subsets such as carbohydrates and lipids. In contrast, the VQR ansatz remains consistently weaker than both QKR and the classical baselines across all classes and data sizes. The results highlight the strong influence of dataset size, feature representation, and molecular heterogeneity on QML performance while also identifying conditions under which quantum methods may become competitive in future studies.