Quantum algorithms achieve polylogarithmic complexity for Betti number estimation and homology testing via block-encoded Laplacians and cohomological projections, claiming exponential speedups under sparsity assumptions.
Quantum embeddings for machine learning
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 9roles
background 1polarities
background 1representative citing papers
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
Proves mean-field limit and propagation of chaos for gradient-flow trained mixtures of experts with explicit rate depending only on expert count, applied to quantum neural networks.
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.
A dual GNN predictor evaluates hardware-aware quantum circuit graphs to select task-specific kernels that balance expressivity and NISQ constraints, outperforming baselines on classification benchmarks.
A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
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.
citing papers explorer
-
New aspects of quantum topological data analysis: Betti number estimation, and testing and tracking of homology and cohomology classes
Quantum algorithms achieve polylogarithmic complexity for Betti number estimation and homology testing via block-encoded Laplacians and cohomological projections, claiming exponential speedups under sparsity assumptions.
-
SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
-
Mean-field limit from general mixtures of experts to quantum neural networks
Proves mean-field limit and propagation of chaos for gradient-flow trained mixtures of experts with explicit rate depending only on expert count, applied to quantum neural networks.
-
Hybrid Quantum-Classical Algorithm for Hamiltonian Simulation
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
-
Efficient classical computation of the neural tangent kernel of quantum neural networks
An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.
-
Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks
A dual GNN predictor evaluates hardware-aware quantum circuit graphs to select task-specific kernels that balance expressivity and NISQ constraints, outperforming baselines on classification benchmarks.
-
A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
-
A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
-
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.