All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
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16 Pith papers cite this work, alongside 1,653 external citations. Polarity classification is still indexing.
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QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
Berry Phase Rate and other geometric observables from learned spectral embeddings of equity-index returns detect financial regime shifts with competitive out-of-sample performance and lower false-alarm rates than supervised baselines.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
Quantum circuits implement Hamming-distance-like genomic classifiers via active and symmetric inner products on IBM quantum processors with fixed qubit requirements for arbitrary training samples.
Hybrid quantum-classical model with quantum feature encoding and clustering outperforms classical neural networks for LPBF melt pool prediction.
For noisy near-term quantum devices, the paper recommends shallow angle encoding over amplitude encoding once two-qubit error rates exceed roughly 10^-3.
Hybrid XGBoost plus data-reuploading quantum model shows modest F1 gain and lowest false-alarm rate in proxy-free evaluation on temporally partitioned TLM:UAV data, framed as incremental NISQ-era benefit.
QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.
IA-QCNN applies quantum principles via ring-topology convolution and importance weighting to achieve claimed high-accuracy MGMT methylation prediction from MRI with fewer parameters and noise robustness than classical models.
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.
The paper introduces 'Platonic Projection Structures,' a reformulation of standard PSD operator theory applied to representation learning, with experiments that verify definitions rather than test predictions.
citing papers explorer
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New perspectives on quantum kernels through the lens of entangled tensor kernels
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
-
QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
-
Geometric Observables for Financial Regime Detection
Berry Phase Rate and other geometric observables from learned spectral embeddings of equity-index returns detect financial regime shifts with competitive out-of-sample performance and lower false-alarm rates than supervised baselines.
-
Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
-
Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Implementation of a Hamming-Distance-Like Genomic Quantum Classifier Using Inner Products on IBMQX4 and IBMQX16
Quantum circuits implement Hamming-distance-like genomic classifiers via active and symmetric inner products on IBM quantum processors with fixed qubit requirements for arbitrary training samples.
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A Hybrid Quantum-Classical Approach for Melt Pool Prediction in Laser Powder Bed Fusion
Hybrid quantum-classical model with quantum feature encoding and clustering outperforms classical neural networks for LPBF melt pool prediction.
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Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
For noisy near-term quantum devices, the paper recommends shallow angle encoding over amplitude encoding once two-qubit error rates exceed roughly 10^-3.
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Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
Hybrid XGBoost plus data-reuploading quantum model shows modest F1 gain and lowest false-alarm rate in proxy-free evaluation on temporally partitioned TLM:UAV data, framed as incremental NISQ-era benefit.
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QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction
QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.
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A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
IA-QCNN applies quantum principles via ring-topology convolution and importance weighting to achieve claimed high-accuracy MGMT methylation prediction from MRI with fewer parameters and noise robustness than classical models.
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Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.
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Platonic Projection Structures: Operator-Induced Observability in Representation Learning
The paper introduces 'Platonic Projection Structures,' a reformulation of standard PSD operator theory applied to representation learning, with experiments that verify definitions rather than test predictions.