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

arXiv:2607.20168

Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage

Junchi Shen

q-fin.PRquant-phstat.MLSubmitted Jul 22, 2026• Announced Jul 23, 2026• 0 citations
arXivHTMLPDF

Abstract

Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main evaluation -- a point-in-time universe and 170 walk-forward windows (2012-2025) -- no quantum advantage exists: the fidelity kernel is indistinguishable from its RBF control ($Δ$IC $=+0.005$, $p=0.42$), and a $2\times2$ design crossing kernel type with training budget (a Nystrom extension to the full ~38,000-observation windows) shows quantum kernels matching, but never beating, equal-budget linear models; after family-wise correction no pairwise difference among eleven models is significant, with point estimates favoring penalized linear regressions throughout. We then document how the opposite conclusion arises: a 60-window evaluation on a universe screened with full-sample information makes the same quantum kernel appear dominant on stability criteria and significantly better than neural baselines. Interaction characteristics from the anomalies literature help nothing, quantum or classical; a widened bandwidth grid reveals an interior optimum rather than the near-classical endpoint a coarse grid suggests; and the geometric difference, while large throughout ($g \gg 1$), does not predict out-of-sample gains ($ρ=-0.20$). We propose protocol standards -- kernel-swap controls, budget-equalized comparisons, point-in-time universes, and multiplicity-robust inference -- for empirical claims of quantum advantage in finance.

Comments
16 pages, 2 figures, 5 tables. Code and data pipeline available on request

Related papers

Empirical Evaluation of Quantum Kernel Methods for Classification: A Comparative Study with Classical Kernels

Ronak Bhalgami

Quantum kernel methods are the most concrete near-term proposal for quantum machine learning on classical data, yet published benchmarks disagree on whether they outperform tuned classical kernels, and the disagreement cannot be resolved from the literature because the comparisons are not protocol-controlled. We argue that the obstacle is one of measurement, not theory: the usual setup fixes the quantum feature map's data scaling while grid-searching the classical kernel's hyperparameters, recomputes nothing per fold, and reports cost in simulator time. We therefore treat the comparison itself as the object of study and impose three controls. The bandwidth is tuned on the same grid budget as the classical $γ$, the kernel is recomputed inside every fold from fold-fitted preprocessing, and cost is counted in hardware circuit evaluations. On top of these we run a factorial design of 324 cells in which dimensionality, training size, class separability and data structure are explicit covariates, together with four standard datasets, a shots-versus-gate-noise ablation, and a cost sweep. The dominant effect we measure is not quantum: tuning the feature map's bandwidth is worth 0.120 accuracy on synthetic data and 0.339 on real data, exceeding every quantum-classical difference we observe. Tuned quantum kernels are then statistically indistinguishable from a tuned RBF on all four real datasets. The advantage on synthetic data is confined almost entirely to data generated by the feature map itself, and it shrinks with dimensionality (-0.0045 per qubit) while growing with sample size. Finite sampling costs an order of magnitude more accuracy than gate and readout noise, and a fidelity kernel needs $5\times10^{7}$ circuits at n = 10,000. Protocol variance is thus sufficient to explain the field's contradictory conclusions.

quant-pharXiv:2610.42029•Nov 2, 2026

When Do Quantum Kernels Help? An Interaction-Order Criterion and Null Results Across Three Biomedical Cohorts

Dhruv sachdeva, Rajeev Gupta

Quantum kernel methods are increasingly applied to clinical prediction, but reported advantages rarely come with ablations, confidence intervals or negative controls. We benchmark a hybrid quantum-classical stacking model under nested cross-validation on three public biomedical cohorts: CRyPTIC tuberculosis drug resistance (12,287 isolates, six drugs), Duke DCE-MRI radiogenomics (n = 922, five endpoints) and MIAS mammography (322 images). Against an all-classical ensemble, the quantum branches change AUC by between -0.0024 and +0.0048 on every tuberculosis and Duke endpoint. Of twenty-four paired comparisons none significantly favours the hybrid, and the one nominally significant difference (p = 0.012, the hybrid below logistic regression on mammography) does not survive correction for multiplicity. Two measurements explain the null: fidelity-kernel off-diagonals decay as 0.5037^n (R^2 = 0.99), and a 277-fold increase in simulated entanglement moves AUC by 0.0148. We recast the inductive-bias argument for quantum kernels as a screen that needs no quantum computation: a quantum feature map can help only if the target depends on high-order feature interactions. The screen measured all six tuberculosis drugs as first-order before any quantum model was run. On synthetic controls an entangling map overtakes tuned classical baselines only at maximal interaction order, and its margin grows with sample size, against the common small-data claim. We also document three evaluation failures in our own pipeline, including a feature-label leak (0.918 AUC where the rebuild gives 0.54-0.59) and stacking leakage worth up to 0.09 AUC. All features, predictions and code are released. Data: https://doi.org/10.5281/zenodo.23344459 . Code: https://doi.org/10.5281/zenodo.23344193 .

•Oct 23, 2026•0 citations

Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation

Siavash Kakavand, Christoph Strohmeyer, Michael Schlotter

Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise quantum-classical comparisons reach significance at $α= 0.05$; (ii) learning curve analysis over six training fractions, showing steeper quantum slopes on six of eight datasets that nonetheless fail to close the gap to the best classical baseline; (iii) hardware validation on IBM ibm_fez (Heron r2), demonstrating kernel fidelity $r \geq 0.976$ across six experiments; and (iv) seed sensitivity analysis confirming reproducibility (mean CV 1.4%). A Kruskal-Wallis factorial analysis reveals that dataset choice dominates performance variance ($\varepsilon^2 = 0.73$), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel, the radial basis function (RBF). Quantum kernel training (QKT) via kernel-target alignment yields the single competitive result -- balanced accuracy 0.968 on breast cancer -- but with ~2,000x computational overhead. Our findings provide actionable guidelines for quantum kernel research. The complete benchmark suite is publicly available to facilitate reproduction and extension.

quant-pharXiv:2604.18837•Apr 22, 2026•0 citations

Empirical Evaluation of Quantum Kernel Learning for Sector Rotation Prediction: Using Fama–French 10 Industry Portfolios

Tohru Nakajima

Quantum Machine Learning (QML) is gaining attention for extracting nonlinear struc-tures from high-dimensional data, yet its practical utility in financial forecasting remains insufficiently verified. This study empirically evaluates the effectiveness of quantum ker-nel learning for sector rotation prediction using daily returns from Fama–French 10 In-dustry Portfolios (2022–2024). We formulated a supervised classification task using a compact nine-feature set comprising mean return, volatility, win rate, excess return, MA5, MA20, max drawdown, 5-day return, and skewness. We compared classical Support Vector Classifiers (RBF and Linear kernels) against a fidelity quantum kernel (ZZFea-tureMap) evaluated on AerSimulator using 1-nearest neighbor classification. Results in-dicate that while the classical RBF kernel achieved the highest average accuracy across all sectors (52.1%), the quantum kernel (50.0%) outperformed the classical linear kernel (48.4%) and achieved the highest accuracy in the Non-Durables (55.0%) and Manufactur-ing (52.2%) sectors, demonstrating superior stability. These findings suggest that the effec-tiveness of quantum kernels in the near term is not universal but sector-dependent and conditional. Although quantum methods currently trail strong classical baselines on av-erage, future research should focus on advanced circuit design and hardware-aware evaluations to unlock their potential.

•Feb 10, 2026•0 citations

Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study

Chi-Sheng Chen, Xinyu Zhang, En-Jui Kuo +3 more

Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shifts, and limited generalization capacity. To address these challenges, we propose a Quantum Temporal Convolutional Neural Network (QTCNN) that combines a classical temporal encoder with parameter-efficient quantum convolution circuits for cross-sectional equity return prediction. The temporal encoder extracts multi-scale patterns from sequential technical indicators, while the quantum processing leverages superposition and entanglement to enhance feature representation and suppress overfitting. We conduct a comprehensive benchmarking study on the JPX Tokyo Stock Exchange dataset and evaluate predictions through long-short portfolio construction using out-of-sample Sharpe ratio as the primary performance metric. QTCNN achieves a Sharpe ratio of 0.538, outperforming the best classical baseline by approximately 72\%. These results highlight the practical potential of quantum-enhanced forecasting model, QTCNN, for robust decision-making in quantitative finance.

cs.LGCross-listedarXiv:2512.06630•Dec 9, 2025•0 citations

Quantum kernels under classical postprocessing

Mustafa Göksel Delikaya

Quantum kernel methods retain the standard supervised-learning interface while defining similarities through overlaps of quantum feature states. In practice, the resulting Gram matrices can develop strong global components that dominate the spectrum and make geometric comparisons to classical kernels difficult. This motivates separating global effects from task-relevant variation and testing whether classical postprocessing can reveal and reshape the remaining structure without changing the embedding circuit. Kernel centering (double-centering) is therefore used to remove the constant component while preserving positive semidefiniteness. In parallel, a controlled set of classical extensions is examined at three levels, namely elementwise transforms on datapoints before embedding, spectrum-level eigenvalue mappings, and entrywise kernel transformations, with trace normalization used for consistent comparison. Overall, the results show that the centering reshapes kernel spectra and geometry-based comparisons, often amplifying the separation from a classical RBF reference, even though model predictions are invariant. In contrast, circuit-independent classical extensions can affect predictive performance, narrowing the classification gap for fidelity kernels and improving regression performance, in some settings matching or exceeding the classical baseline. These findings position kernel postprocessing as a practical and low-cost way to analyze quantum kernel geometry and to improve kernel-based supervised learning pipelines (e.g., SVC/SVR) that use quantum kernels.

•Jan 1, 2026•0 citations
Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main evaluation -- a point-in-time universe and 170 walk-forward windows (2012-2025) -- no quantum advantage exists: the fidelity kernel is indistinguishable from its RBF control (ΔΔΔIC =+0.005=+0.005=+0.005, p=0.42p=0.42p=0.42), and a 2×22\times22×2 design crossing kernel type with training budget (a Nystrom extension to the full ~38,000-observation windows) shows quantum kernels matching, but never beating, equal-budget linear models; after family-wise correction no pairwise difference among eleven models is significant, with point estimates favoring penalized linear regressions throughout. We then document how the opposite conclusion arises: a 60-window evaluation on a universe screened with full-sample information makes the same quantum kernel appear dominant on stability criteria and significantly better than neural baselines. Interaction characteristics from the anomalies literature help nothing, quantum or classical; a widened bandwidth grid reveals an interior optimum rather than the near-classical endpoint a coarse grid suggests; and the geometric difference, while large throughout (g≫1g \gg 1g≫1), does not predict out-of-sample gains (ρ=−0.20ρ=-0.20ρ=−0.20). We propose protocol standards -- kernel-swap controls, budget-equalized comparisons, point-in-time universes, and multiplicity-robust inference -- for empirical claims of quantum advantage in finance.
Quantum kernel methods are the most concrete near-term proposal for quantum machine learning on classical data, yet published benchmarks disagree on whether they outperform tuned classical kernels, and the disagreement cannot be resolved from the literature because the comparisons are not protocol-controlled. We argue that the obstacle is one of measurement, not theory: the usual setup fixes the quantum feature map's data scaling while grid-searching the classical kernel's hyperparameters, recomputes nothing per fold, and reports cost in simulator time. We therefore treat the comparison itself as the object of study and impose three controls. The bandwidth is tuned on the same grid budget as the classical γγγ, the kernel is recomputed inside every fold from fold-fitted preprocessing, and cost is counted in hardware circuit evaluations. On top of these we run a factorial design of 324 cells in which dimensionality, training size, class separability and data structure are explicit covariates, together with four standard datasets, a shots-versus-gate-noise ablation, and a cost sweep. The dominant effect we measure is not quantum: tuning the feature map's bandwidth is worth 0.120 accuracy on synthetic data and 0.339 on real data, exceeding every quantum-classical difference we observe. Tuned quantum kernels are then statistically indistinguishable from a tuned RBF on all four real datasets. The advantage on synthetic data is confined almost entirely to data generated by the feature map itself, and it shrinks with dimensionality (-0.0045 per qubit) while growing with sample size. Finite sampling costs an order of magnitude more accuracy than gate and readout noise, and a fidelity kernel needs 5×1075\times10^{7}5×107 circuits at n = 10,000. Protocol variance is thus sufficient to explain the field's contradictory conclusions.
Quantum kernel methods are increasingly applied to clinical prediction, but reported advantages rarely come with ablations, confidence intervals or negative controls. We benchmark a hybrid quantum-classical stacking model under nested cross-validation on three public biomedical cohorts: CRyPTIC tuberculosis drug resistance (12,287 isolates, six drugs), Duke DCE-MRI radiogenomics (n = 922, five endpoints) and MIAS mammography (322 images). Against an all-classical ensemble, the quantum branches change AUC by between -0.0024 and +0.0048 on every tuberculosis and Duke endpoint. Of twenty-four paired comparisons none significantly favours the hybrid, and the one nominally significant difference (p = 0.012, the hybrid below logistic regression on mammography) does not survive correction for multiplicity. Two measurements explain the null: fidelity-kernel off-diagonals decay as 0.5037^n (R^2 = 0.99), and a 277-fold increase in simulated entanglement moves AUC by 0.0148. We recast the inductive-bias argument for quantum kernels as a screen that needs no quantum computation: a quantum feature map can help only if the target depends on high-order feature interactions. The screen measured all six tuberculosis drugs as first-order before any quantum model was run. On synthetic controls an entangling map overtakes tuned classical baselines only at maximal interaction order, and its margin grows with sample size, against the common small-data claim. We also document three evaluation failures in our own pipeline, including a feature-label leak (0.918 AUC where the rebuild gives 0.54-0.59) and stacking leakage worth up to 0.09 AUC. All features, predictions and code are released. Data: https://doi.org/10.5281/zenodo.23344459 . Code: https://doi.org/10.5281/zenodo.23344193 .
Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise quantum-classical comparisons reach significance at α=0.05α= 0.05α=0.05; (ii) learning curve analysis over six training fractions, showing steeper quantum slopes on six of eight datasets that nonetheless fail to close the gap to the best classical baseline; (iii) hardware validation on IBM ibm_fez (Heron r2), demonstrating kernel fidelity r≥0.976r \geq 0.976r≥0.976 across six experiments; and (iv) seed sensitivity analysis confirming reproducibility (mean CV 1.4%). A Kruskal-Wallis factorial analysis reveals that dataset choice dominates performance variance (ε2=0.73\varepsilon^2 = 0.73ε2=0.73), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel, the radial basis function (RBF). Quantum kernel training (QKT) via kernel-target alignment yields the single competitive result -- balanced accuracy 0.968 on breast cancer -- but with ~2,000x computational overhead. Our findings provide actionable guidelines for quantum kernel research. The complete benchmark suite is publicly available to facilitate reproduction and extension.
Quantum Machine Learning (QML) is gaining attention for extracting nonlinear struc-tures from high-dimensional data, yet its practical utility in financial forecasting remains insufficiently verified. This study empirically evaluates the effectiveness of quantum ker-nel learning for sector rotation prediction using daily returns from Fama–French 10 In-dustry Portfolios (2022–2024). We formulated a supervised classification task using a compact nine-feature set comprising mean return, volatility, win rate, excess return, MA5, MA20, max drawdown, 5-day return, and skewness. We compared classical Support Vector Classifiers (RBF and Linear kernels) against a fidelity quantum kernel (ZZFea-tureMap) evaluated on AerSimulator using 1-nearest neighbor classification. Results in-dicate that while the classical RBF kernel achieved the highest average accuracy across all sectors (52.1%), the quantum kernel (50.0%) outperformed the classical linear kernel (48.4%) and achieved the highest accuracy in the Non-Durables (55.0%) and Manufactur-ing (52.2%) sectors, demonstrating superior stability. These findings suggest that the effec-tiveness of quantum kernels in the near term is not universal but sector-dependent and conditional. Although quantum methods currently trail strong classical baselines on av-erage, future research should focus on advanced circuit design and hardware-aware evaluations to unlock their potential.
Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shifts, and limited generalization capacity. To address these challenges, we propose a Quantum Temporal Convolutional Neural Network (QTCNN) that combines a classical temporal encoder with parameter-efficient quantum convolution circuits for cross-sectional equity return prediction. The temporal encoder extracts multi-scale patterns from sequential technical indicators, while the quantum processing leverages superposition and entanglement to enhance feature representation and suppress overfitting. We conduct a comprehensive benchmarking study on the JPX Tokyo Stock Exchange dataset and evaluate predictions through long-short portfolio construction using out-of-sample Sharpe ratio as the primary performance metric. QTCNN achieves a Sharpe ratio of 0.538, outperforming the best classical baseline by approximately 72\%. These results highlight the practical potential of quantum-enhanced forecasting model, QTCNN, for robust decision-making in quantitative finance.
Quantum kernel methods retain the standard supervised-learning interface while defining similarities through overlaps of quantum feature states. In practice, the resulting Gram matrices can develop strong global components that dominate the spectrum and make geometric comparisons to classical kernels difficult. This motivates separating global effects from task-relevant variation and testing whether classical postprocessing can reveal and reshape the remaining structure without changing the embedding circuit. Kernel centering (double-centering) is therefore used to remove the constant component while preserving positive semidefiniteness. In parallel, a controlled set of classical extensions is examined at three levels, namely elementwise transforms on datapoints before embedding, spectrum-level eigenvalue mappings, and entrywise kernel transformations, with trace normalization used for consistent comparison. Overall, the results show that the centering reshapes kernel spectra and geometry-based comparisons, often amplifying the separation from a classical RBF reference, even though model predictions are invariant. In contrast, circuit-independent classical extensions can affect predictive performance, narrowing the classification gap for fidelity kernels and improving regression performance, in some settings matching or exceeding the classical baseline. These findings position kernel postprocessing as a practical and low-cost way to analyze quantum kernel geometry and to improve kernel-based supervised learning pipelines (e.g., SVC/SVR) that use quantum kernels.