TensorHyper-VQC improves VQC scalability and robustness.
problem Scalability and noise sensitivity in VQC.
method Tensor-train-guided hypernetwork framework.
result TensorHyper-VQC achieves superior performance and robust noise tolerance.
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
TensorHyper-VQC improves VQC scalability and robustness.
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
UKM framework optimizes VQCs, showing QCL performance is bounded.
Quantum machine learning solves high-dimensional PDEs with lower variance and improved accuracy.
Quantum self-attention boosts automated market maker performance in crypto trading.
This study compares feature importance and explainability in quantum vs classical ML models.