Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

No Quantum Advantage Without Structure: A Controlled Benchmark of Quantum Machine Learning on Classical Data

Sadia Afrin Purba, M A Al Mamun, Iyad Obeid A, Joseph Picone

Published Aug 7, 2026• 0 citations
Publisher
DOI
10.13140/rg.2.2.30344.38407

Related papers

Quantum Machine Learning: Bridging Classical and Quantum Frontiers

Sardar M. N. Islam, Prem Kinger, Neda Fatima +1 more

Innovations in Sustainable Technologies and Computing•Jan 1, 2024•4 citations

Benchmarking Quantum Machine Learning Models Against Classical Deep Learning

Elena Silva, Amelia Horvath

Quantum machine learning (QML) benchmarks frequently compare quantum models against weak classical baselines -- the same methodological gap identified for quantum optimisation in the QCCB study (Popescu and Petrov, 2024). This paper presents the QML-DL Benchmark (QMLDB), a rigorous comparative evaluation of six QML model families against nine state-of-the-art classical deep learning baselines (MLP, CNN, LSTM, ResNet, Transformer, TabNet, XGBoost, LightGBM, CatBoost) across ten benchmark datasets spanning six task domains. QMLDB controls for model capacity, training budget, and data size. QML achieves top performance on three of ten datasets -- all three with strong quantum-process proximity. On the remaining seven, classical deep learning dominates, with Transformer and ResNet consistently outperforming all QML models by 4.8-18.4pp. QMLDB establishes that QML advantage is narrower than commonly claimed, confined to datasets with genuine quantum structure, and proposes a quantum-process proximity score (QPPS) to predict whether a new dataset will benefit from QML before hardware experiments.

Quantum Frontiers Journal•Mar 28, 2025•0 citations

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data

Sydney Leither, Thomas Lubinski, Michael Kubal +1 more

Machine learning is being increasingly used for the detection, diagnosis, and treatment of cancer. However, models often struggle with biological data due to high dimensionality, limited sample diversity, and complex feature interactions. Recent works have investigated the potential for quantum machine learning models to exhibit improved performance over classical models on this kind of complex data, but have often lacked rigorous empirical evaluation of quantum advantage. In this work, we develop a methodology for fair benchmarking of quantum and classical machine learning models, based on the Red Cedar quantum machine learning and resource estimation framework and AutoML-optimized classical neural networks. We assess the potential for quantum advantage in machine learning across tabular, omics, and spatial oncological datasets drawn from the existing quantum machine learning literature, with a range of preprocessing methods, and find no evidence of quantum advantage. Our results suggest that the field should prioritize analyzing higher-dimensional, more biologically realistic datasets to make meaningful progress toward practical quantum advantage in oncological classification problems.

quant-pharXiv:2608.11373•Aug 13, 2026•0 citations

Quantum Machine Learning: Principles and Applications

Yanjun Ji

•Jan 1, 2026•0 citations

Benchmarking Classical and Quantum Machine Learning for Potency Prediction across Ten Therapeutically Relevant Targets

Shiv Garg, Sara Garg

Quantum machine learning (QML) is widely proposed as a successor to classical models in cheminformatics, but rigorous benchmarks under realistic contemporary data constraints remain scarce. We compared two quantum classifiers (quantum support vector machine, variational quantum classifier) against gradient boosting, random forest, and neural network baselines across ten therapeutically diverse protein targets (45-18,286 compounds), using Bemis-Murcko scaffold-stratified cross-validation. Across 48 valid target-threshold tasks, no model class globally dominated (best-quantum versus best-classical Wilcoxon p = 0.189). Classical ensembles excelled on data-rich, structurally coherent targets (e.g., BACE1), while quantum kernels held a marginal edge on sparse, structurally bimodal targets (e.g., Mtb DprE1). A potential predictor of quantum advantage was the per-target best-quantum/best-classical model Cohen's κ gap (Spearman ρ = +0.685, p = 0.029), suggesting that decision calibration, not ranking, drives QML's edge. We propose the κ gap as a preliminary low-cost screening criterion for prioritizing QML in early stage discovery campaigns.

ACS Medicinal Chemistry Letters•Jul 7, 2026•0 citations

Advances in Quantum Machine Learning

A. S. Wilson

•Sep 1, 2021•0 citations
Quantum machine learning (QML) benchmarks frequently compare quantum models against weak classical baselines -- the same methodological gap identified for quantum optimisation in the QCCB study (Popescu and Petrov, 2024). This paper presents the QML-DL Benchmark (QMLDB), a rigorous comparative evaluation of six QML model families against nine state-of-the-art classical deep learning baselines (MLP, CNN, LSTM, ResNet, Transformer, TabNet, XGBoost, LightGBM, CatBoost) across ten benchmark datasets spanning six task domains. QMLDB controls for model capacity, training budget, and data size. QML achieves top performance on three of ten datasets -- all three with strong quantum-process proximity. On the remaining seven, classical deep learning dominates, with Transformer and ResNet consistently outperforming all QML models by 4.8-18.4pp. QMLDB establishes that QML advantage is narrower than commonly claimed, confined to datasets with genuine quantum structure, and proposes a quantum-process proximity score (QPPS) to predict whether a new dataset will benefit from QML before hardware experiments.
Machine learning is being increasingly used for the detection, diagnosis, and treatment of cancer. However, models often struggle with biological data due to high dimensionality, limited sample diversity, and complex feature interactions. Recent works have investigated the potential for quantum machine learning models to exhibit improved performance over classical models on this kind of complex data, but have often lacked rigorous empirical evaluation of quantum advantage. In this work, we develop a methodology for fair benchmarking of quantum and classical machine learning models, based on the Red Cedar quantum machine learning and resource estimation framework and AutoML-optimized classical neural networks. We assess the potential for quantum advantage in machine learning across tabular, omics, and spatial oncological datasets drawn from the existing quantum machine learning literature, with a range of preprocessing methods, and find no evidence of quantum advantage. Our results suggest that the field should prioritize analyzing higher-dimensional, more biologically realistic datasets to make meaningful progress toward practical quantum advantage in oncological classification problems.
Quantum machine learning (QML) is widely proposed as a successor to classical models in cheminformatics, but rigorous benchmarks under realistic contemporary data constraints remain scarce. We compared two quantum classifiers (quantum support vector machine, variational quantum classifier) against gradient boosting, random forest, and neural network baselines across ten therapeutically diverse protein targets (45-18,286 compounds), using Bemis-Murcko scaffold-stratified cross-validation. Across 48 valid target-threshold tasks, no model class globally dominated (best-quantum versus best-classical Wilcoxon p = 0.189). Classical ensembles excelled on data-rich, structurally coherent targets (e.g., BACE1), while quantum kernels held a marginal edge on sparse, structurally bimodal targets (e.g., Mtb DprE1). A potential predictor of quantum advantage was the per-target best-quantum/best-classical model Cohen's κ gap (Spearman ρ = +0.685, p = 0.029), suggesting that decision calibration, not ranking, drives QML's edge. We propose the κ gap as a preliminary low-cost screening criterion for prioritizing QML in early stage discovery campaigns.