# Adapting Quantum Machine Learning for Energy Dissociation of Bonds

arXiv: https://arxiv.org/abs/2510.06563 · HTML: https://mlsanity.com/paper/2510.06563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems](https://mlsanity.com/paper/1910.13045/) (2019)
- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [Modeling Feature Maps for Quantum Machine Learning](https://mlsanity.com/paper/2501.08205/) (2025)
- [Modeling Quantum Machine Learning for Genomic Data Analysis](https://mlsanity.com/paper/2501.08193/) (2025)

## Built on by (most cited first, 2 of 2)

- [Resource-Efficient Variational Quantum Classifier](https://mlsanity.com/paper/2511.09204/) (2025)
- [Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection](https://mlsanity.com/paper/2511.21932/) (2025)

## Abstract (arXiv)

Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic properties (atomic numbers, hybridization), bond characteristics (bond order, type), and local environmental descriptors. Our quantum framework, implemented in Qiskit Aer on six qubits, employs ZZFeatureMap encodings with variational ansatz (RealAmplitudes) across multiple architectures Variational Quantum Regressors (VQR), Quantum Support Vector Regressors (QSVR), Quantum Neural Networks (QNN), Quantum Convolutional Neural Networks (QCNN), and Quantum Random Forests (QRF). These are rigorously benchmarked against strong classical baselines, including Support Vector Regression (SVR), Random Forests (RF), and Multi-Layer Perceptrons (MLP). Comprehensive evaluation spanning absolute and relative error metrics, threshold accuracies, and error distributions shows that top-performing quantum models (QCNN, QRF) match the predictive accuracy and robustness of classical ensembles and deep networks, particularly within the chemically prevalent mid-range BDE regime. These findings establish a transparent baseline for quantum-enhanced molecular property prediction and outline a practical foundation for advancing quantum computational chemistry toward near chemical accuracy.