Publications (28)
HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning
Cheng Liu, Cheng Chu, Dawen Xu +5
Hardware faults on the regular 2-D computing array of a typical deep learning accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches ty…
BVQC: A Backdoor-style Watermarking Scheme for Variational Quantum Circuits
Cheng Chu, Lei Jiang, Fan Chen
Variational Quantum Circuits (VQCs) have emerged as a powerful quantum computing paradigm, demonstrating a scaling advantage for problems intractable for classical computation. As…
Energy-Efficient Accelerator Design for Deformable Convolution Networks
Dawen Xu, Cheng Chu, Cheng Liu +4
Deformable convolution networks (DCNs) proposed to address the image recognition with geometric or photometric variations typically involve deformable convolution that convolves on…
A Characterization of
Cheng Chu
We characterize the set of all measurable functions on $\RR^n$ possessing an majorant, denoted as $\cM_{A_1}(\RR^n)$, by certain Banach function spaces. We prove that a funct…
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines
Zhenxiao Fu, Min Yang, Cheng Chu +3
Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serv…
Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Kexin Chu, Zecheng Lin, Dawei Xiang +7
Global KV-cache sharing is an effective optimization for accelerating large language model (LLM) inference, yet it introduces an API-visible timing side channel that lets adversari…
CryptoQFL: Quantum Federated Learning on Encrypted Data
Cheng Chu, Lei Jiang, Fan Chen
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applica…
IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices
Cheng Chu, Grant Skipper, Martin Swany +1
In this work, we propose IQGAN, a quantum Generative Adversarial Network (GAN) framework for multiqubit image synthesis that can be efficiently implemented on Noisy Intermediate Sc…
QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates
Cheng Chu, Nai-Hui Chia, Lei Jiang +1
Despite potential quantum supremacy, state-of-the-art quantum neural networks (QNNs) suffer from low inference accuracy. First, the current Noisy Intermediate-Scale Quantum (NISQ)…
Asymptotic Bohr Radius for the Polynomials in One Complex Variable
Cheng Chu
We consider the Bohr radius for the class of complex polynomials in one variable of degree at most . It was conjectured by R. Fournier in 2008 that $R_n={1\over 3}+{Ï^2\o…
Product of truncated Hankel and truncated Toeplitz operators
Cheng Chu
A truncated Toeplitz operator is the compression of a classical Toeplitz operator on the Hardy space to a model space. A truncated Hankel operator is the compression of a Hankel op…
Normal Truncated Toeplitz Operators
Cheng Chu
The characterization of normal truncated Toepltiz operators is first given by Chalendar and Timotin. We give an elementary proof of their result without using the algebraic propert…
Reducing Subspaces of de Branges-Rovnyak Spaces
Cheng Chu
For , the closed unit ball of , the de Branges-Rovnyak spaces is a Hilbert space contractively contained in the Hardy space that i…
Hilbert Spaces Contractively Contained in Weighted Bergman Spaces on the Unit Disk
Cheng Chu
Sub-Bergman Hilbert spaces are analogues of de Branges-Rovnyak spaces in the Bergman space setting. They are reproducing kernel Hilbert spaces contractively contained in the Bergma…
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks
Cheng Chu, Fan Chen, Philip Richerme +1
Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Q…
Which de Branges-Rovnyak spaces have complete Nevanlinna-Pick property?
Cheng Chu
We characterize the de Branges-Rovnyak spaces with complete Nevanlinna-Pick property. Our method relies on the general theory of reproducing kernel Hilbert spaces.
Density of Polynomials in Sub-Bergman Hilbert Spaces
Cheng Chu
The sub-Bergman Hilbert spaces are analogues of de BrangesRovnyak spaces in the Bergman space setting. We prove that the polynomials are dense in sub-Bergman Hilbert spaces. This a…
Bounded Composition Operators and Multipliers of Some Reproducing Kernel Hilbert Spaces on the Bidisk
Cheng Chu
We study the boundedness of composition operators on the bidisk using reproducing kernels. We show that a composition operator is bounded on the Hardy space of the bidisk if some a…
SoK: Adversarial Robustness of the Variational Quantum Eigensolver via Red-Teaming
Ahmed Azaz Humdoon, Cheng Chu, Lei Jiang +2
The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum ch…
LSTM-QGAN: Scalable NISQ Generative Adversarial Network
Cheng Chu, Aishwarya Hastak, Fan Chen
Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction,…
A Gleason-Kahane-Żelazko theorem for reproducing kernel Hilbert spaces
Cheng Chu, Michael Hartz, Javad Mashreghi +1
We establish the following Hilbert-space analogue of the Gleason-Kahane-Å»elazko theorem. If is a reproducing kernel Hilbert space with a normalized complete Pick ker…
Compact Product of Hankel and Toeplitz Operators
Cheng Chu
In this paper, we study the product of a Hankel operator and a Toeplitz operator on the Hardy space. We give necessary and sufficient conditions of when such a product is…
Hardware Robustness of Sample-Based Quantum Diagonalization
Ahatesham Bhuiyan, Cheng Chu, Qian Lou +1
Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Althou…
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
Cheng Chu, Lei Jiang, Martin Swany +1
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the va…
CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu +4
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum ch…
TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer
Cheng Chu, Zhenxiao Fu, Yilun Xu +4
Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, m…
OFHE: An Electro-Optical Accelerator for Discretized TFHE
Mengxin Zheng, Cheng Chu, Qian Lou +4
This paper presents \textit{OFHE}, an electro-optical accelerator designed to process Discretized TFHE (DTFHE) operations, which encrypt multi-bit messages and support homomorphic…
A Note on the Spectral Area of Toeplitz Operators
Cheng Chu, Dmitry Khavinson
In this note, we show that for hyponormal Toeplitz operators, there exists a lower bound for the area of the spectrum. This extends the known estimate for the spectral area of Toep…