aiqbench analysis
This research introduces topology-aligned architectures where model structures mirror molecular bond graphs, significantly improving parameter efficiency for quantum chemistry tasks like HOMO-LUMO gap prediction. By benchmarking both variational quantum circuits and classical message-passing networks on the QM9 dataset, the study demonstrates that this inductive bias allows models to reach high performance with minimal trainable parameters while maintaining gradient stability. These findings offer a practical pathway for deploying resource-constrained machine learning solutions within low-data regimes typical of molecular property prediction.
Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction
arXiv:2607.13737v2 Announce Type: replace Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology-aligned inductive bias in which the model architecture mirrors the molecular bond graph: atoms map to a fixed register of computational units, and bonds determine which pairs interact through shared learnable parameters. This principle is instantiated in two architectures: a variational quantum circuit (Iso-QGNN) and a parameter-matched classical message-passing network (Iso-CGNN). The models are benchmarked on HOMO-LUMO and dipole moment binary classification tasks over the QM9 benchmark. With 64 trainable parameters, the implementations achieve test AUCs of approximately 0.89 (quantum) and 0.92 (classical) on the gap task, and close to 0.78 (both) on the dipole task. The models reach 90% of asymptotic performance within about 300 training molecules and gradient norms remain stable throughout training. These results indicate that the topology-aligned inductive bias is the active ingredient driving parameter efficiency at QM9 scale, with implications for matched-baseline benchmarking in quantum machine learning.
Abstract:For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology-aligned inductive bias in which the model architecture mirrors the molecular bond graph: atoms map to a fixed register of computational units, and bonds determine which pairs interact through shared learnable parameters. This principle is instantiated in two architectures: a variational quantum circuit (Iso-QGNN) and a parameter-matched classical message-passing network (Iso-CGNN). The models are benchmarked on HOMO-LUMO and dipole moment binary classification tasks over the QM9 benchmark. With 64 trainable parameters, the implementations achieve test AUCs of approximately 0.89 (quantum) and 0.92 (classical) on the gap task, and close to 0.78 (both) on the dipole task. The models reach 90% of asymptotic performance within about 300 training molecules and gradient norms remain stable throughout training. These results indicate that the topology-aligned inductive bias is the active ingredient driving parameter efficiency at QM9 scale, with implications for matched-baseline benchmarking in quantum machine learning.
Machine Learning (cs.LG)
arXiv:2607.13737 [cs.LG]
(or arXiv:2607.13737v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.13737 Focus to learn more arXiv-issued DOI via DataCite
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