CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
Weisfeiler-lehman graph kernels
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QuIC provides a training-free quantum graph embedding proven permutation-invariant and injective for labeled graphs under an irrational-angle condition in the ideal case, with empirical separation shown on noisy hardware for hard graph families including CFI instances.
Correlation graphs built from windowed multi-channel sEMG plus a lightweight GNN yield 99% real-time accuracy on five hand gestures from eight subjects, exceeding three prior baselines.
citing papers explorer
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CTQWformer: A CTQW-based Transformer for Graph Classification
CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
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QuIC: A Training-Free Quantum Graph Embedding from Ideal Analysis to Practical Hardware Evaluation
QuIC provides a training-free quantum graph embedding proven permutation-invariant and injective for labeled graphs under an irrational-angle condition in the ideal case, with empirical separation shown on noisy hardware for hard graph families including CFI instances.
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A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
Correlation graphs built from windowed multi-channel sEMG plus a lightweight GNN yield 99% real-time accuracy on five hand gestures from eight subjects, exceeding three prior baselines.