17 citations · 28 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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)…
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…
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…