3 citations · 7 across the 12 of their papers we have counts for
13 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…
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…
Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation
Jun Qi, Chen-Yu Liu, Sabato Marco Siniscalchi +2
Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matric…
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…
Resource-Efficient Compilation of Distributed Quantum Circuits for Solving Large-Scale Wireless Communication Network Problems
Kuan-Cheng Chen, Felix Burt, Shang Yu +3
Optimizing routing in Wireless Sensor Networks (WSNs) is pivotal for minimizing energy consumption and extending network lifetime. This paper introduces a resourceefficient compila…
A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
Chen-Yu Liu, Chao-Han Huck Yang, Hsi-Sheng Goan +1
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits i…
QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train
Chen-Yu Liu, Chu-Hsuan Abraham Lin, Chao-Han Huck Yang +2
Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face cha…
Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in da…
Oracle Separation between Noisy Quantum Polynomial Time and the Polynomial Hierarchy
Nai-Hui Chia, Min-Hsiu Hsieh, Shih-Han Hung +1
This work investigates the oracle separation between the physically motivated complexity class of noisy quantum circuits, inspired by definitions such as those presented by Chen, C…
Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become cr…
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…