10 papers
Evidential Quantum Vertical Federated Learning
Hao Luo, Zhiyuan Zhai, Qianli Zhou +3
Quantum federated learning (QFL) has recently emerged as a promising paradigm for privacy-preserving collaborative learning, yet most existing studies focus on horizontal federated…
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
Data encoding remains a fundamental bottleneck in quantum machine learning, where amplitude encoding of high-dimensional classical vectors into quantum states incurs exponential co…
TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we intro…
Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +3
Hybrid quantum-classical learning models increasingly integrate neural networks with variational quantum circuits (VQCs) to exploit complementary inductive biases. However, many ex…
Tensor Network Assisted Distributed Variational Quantum Algorithm for Large Scale Combinatorial Optimization Problem
Yuhan Huang, Siyuan Jin, Yichi Zhang +3
Although quantum computing holds promise for solving Combinatorial Optimization Problems (COPs), the limited qubit capacity of NISQ hardware makes large-scale instances intractable…
Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +3
We introduce CL-QAS, a continual quantum architecture search framework that mitigates the challenges of costly amplitude encoding and catastrophic forgetting in variational quantum…