Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction

• URL: http://arxiv.org/abs/2607.13737v1
• Date: Wed, 15 Jul 2026 11:54:02 GMT
• Title: Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction
• Abstract summary: We propose a topology-aligned inductive bias in which the model architecture mirrors the molecular bond graph.<n>This principle is instantiated in two architectures: a variational quantum circuit (Iso-QGNN) and a parameter-matched classical message-passing model (Iso-CGNN)<n>With 64 trainable parameters, the implementations achieve test AUCs of approximately 0.88 (quantum) and 0.91 (classical) on the gap task, and close to 0.78 (both) on the dipole task.
• Score: 0.04588028371034406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 model (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 0.88 (quantum) and 0.91 (classical) on the gap task, and close to 0.78 (both) on the dipole task. The models reach 90% of asymptotic performance within about 250 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.

Related papers

• Quantum Hamiltonian Evolution for Coherent Quantum Learning [0.0]
  We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamiltonian that encodes the loss function.
  arXiv  Detail & Related papers  (2026-09-03T10:41:23Z)
• Evaluating Quantum Kernel Methods for Track-Based Classification in High-Energy Physics [0.0]
  We present a design for large-scale quantum kernel classification using centroid-based CLAS12 drift-chamber features.<n>Each event is encoded into a six-qubit state via a fully entangled ZZFeatureMap, whose fidelities define a quantum kernel within a standard SVM framework.
  arXiv  Detail & Related papers  (2026-08-27T16:35:30Z)
• Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics [0.22940141855172033]
  This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime.<n>We present a hybrid pipeline that combines a quantum- Kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE)
  arXiv  Detail & Related papers  (2025-10-11T14:11:41Z)
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• A performance characterization of quantum generative models [35.974070202997176]
  We compare quantum circuits used for quantum generative modeling. We learn the underlying probability distribution of the data sets via two popular training methods. We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods.
  arXiv  Detail & Related papers  (2023-01-23T11:00:29Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Transfer Learning in Quantum Parametric Classifiers: An Information-Theoretic Generalization Analysis [42.275148861039895]
  A key step in quantum machine learning with classical inputs is the design of an embedding circuit mapping inputs to a quantum state. This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit.
  arXiv  Detail & Related papers  (2022-01-17T09:28:13Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Programming a quantum computer with quantum instructions [39.994876450026865]
  We use a density matrixiation protocol to execute quantum instructions on quantum data. A fixed sequence of classically-defined gates performs an operation that uniquely depends on an auxiliary quantum instruction state. The utilization of quantum instructions obviates the need for costly tomographic state reconstruction and recompilation.
  arXiv  Detail & Related papers  (2020-01-23T22:43:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.