1 citations · 3 across the 9 of their papers we have counts for
10 papers
Logical Resource Estimation for Quantum State Preparation with Compilation
Diyi Liu, Hanyu Wang, Shuchen Zhu +6
Quantum state preparation is a fundamental primitive in quantum algorithms for encoding classical data into quantum amplitudes. We compare the cost of preparing general -qubit s…
UFO: Unlocking Ultra-Efficient Quantized Private Inference with Protocol and Algorithm Co-Optimization
Wenxuan Zeng, Chao Yang, Tianshi Xu +4
Private convolutional neural network (CNN) inference based on secure two-party computation (2PC) suffers from high communication and latency overhead, especially from convolution l…
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…
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…
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…
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…