arXiv Machine Learning
QSTAR: Quantum Selective Transfer with Adaptive Routing
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MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery
arXiv:2607. 10707v1 Announce Type: cross Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration.
arXiv Machine Learning
Jun 24
Quantum Adaptive Self-Attention for Quantum Transformer Models
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
arXiv AI
Jul 8
Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
arXiv Machine Learning
Jul 8
Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
arXiv Machine Learning
Sep 22
Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification
arXiv:2609.23476v1 Announce Type: cross Abstract: A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning t...
arXiv Machine Learning
Aug 7
How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
arXiv:2608. 05595v1 Announce Type: cross Abstract: Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work.