1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
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…
Random-Matrix-Induced Simplicity Bias in Over-parameterized Variational Quantum Circuits
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
Over-parameterization is commonly used to increase the expressivity of variational quantum circuits (VQCs), yet deeper and more highly parameterized circuits often exhibit poor tra…
VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
Jun Qi, Chao-Han Yang, Pin-Yu Chen +1
Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience. We propose VQC-MLPNet, a hybr…
Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen +1
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning b…
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen +3
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neu…