Search Results for author: Hanrui Wang

Found 20 papers, 10 papers with code

SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture

no code implementations23 Nov 2022 Han Zheng, Christopher Kang, Gokul Subramanian Ravi, Hanrui Wang, Kanav Setia, Frederic T. Chong, Junyu Liu

We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$.


QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

1 code implementation30 Oct 2022 Hanrui Wang, Pengyu Liu, Jinglei Cheng, Zhiding Liang, Jiaqi Gu, Zirui Li, Yongshan Ding, Weiwen Jiang, Yiyu Shi, Xuehai Qian, David Z. Pan, Frederic T. Chong, Song Han

Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.

NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms

no code implementations2 Aug 2022 Zhiding Liang, Jinglei Cheng, Hang Ren, Hanrui Wang, Fei Hua, Zhixin Song, Yongshan Ding, Fred Chong, Song Han, Yiyu Shi, Xuehai Qian

In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters.

Visual Question Answering (VQA)

RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL

no code implementations13 Jul 2022 Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Pan, Song Han, Nan Sun

To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.

Bayesian Optimization

Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications

no code implementations25 Apr 2022 Han Cai, Ji Lin, Yujun Lin, Zhijian Liu, Haotian Tang, Hanrui Wang, Ligeng Zhu, Song Han

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition.

Model Compression Neural Architecture Search +3

QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning

1 code implementation26 Feb 2022 Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, Song Han

Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy.

Image Classification

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

1 code implementation11 Jan 2022 Hanrui Wang, Shuo Wang, Zhe Jin, Yandan Wang, Cunjian Chen, Massimo Tistarell

This technique applies to both white-box and gray-box attacks against authentication systems that determine genuine or imposter users using the dissimilarity score.

Adversarial Attack Face Recognition

QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization

2 code implementations21 Oct 2021 Hanrui Wang, Jiaqi Gu, Yongshan Ding, Zirui Li, Frederic T. Chong, David Z. Pan, Song Han

Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware.

Denoising Quantization

Towards Efficient On-Chip Training of Quantum Neural Networks

no code implementations29 Sep 2021 Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, Song Han

The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks.

Image Classification

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

2 code implementations22 Jul 2021 Hanrui Wang, Yongshan Ding, Jiaqi Gu, Zirui Li, Yujun Lin, David Z. Pan, Frederic T. Chong, Song Han

Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines.

SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning

no code implementations17 Dec 2020 Hanrui Wang, Zhekai Zhang, Song Han

Inspired by the high redundancy of human languages, we propose the novel cascade token pruning to prune away unimportant tokens in the sentence.


HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

4 code implementations ACL 2020 Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, Song Han

To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search.

Machine Translation Neural Architecture Search +1

MicroNet for Efficient Language Modeling

1 code implementation16 May 2020 Zhongxia Yan, Hanrui Wang, Demi Guo, Song Han

In this paper, we provide the winning solution to the NeurIPS 2019 MicroNet Challenge in the language modeling track.

Knowledge Distillation Language Modelling +3

SpArch: Efficient Architecture for Sparse Matrix Multiplication

no code implementations20 Feb 2020 Zhekai Zhang, Hanrui Wang, Song Han, William J. Dally

We then propose a condensed matrix representation that reduces the number of partial matrices by three orders of magnitude and thus reduces DRAM access by 5. 4x.

Hardware Architecture Distributed, Parallel, and Cluster Computing

Learning to Design Circuits

no code implementations5 Dec 2018 Hanrui Wang, Jiacheng Yang, Hae-Seung Lee, Song Han

We propose Learning to Design Circuits (L2DC) to leverage reinforcement learning that learns to efficiently generate new circuits data and to optimize circuits.

Bayesian Optimization

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

12 code implementations ECCV 2018 Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.

Model Compression Neural Architecture Search

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