activity
20142023
most citedQuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

182 citations · 726 across the 43 of their papers we have counts for

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Showing 2022Show all

9 papers · 1 filter

cs.LG2022★ 1 cited

HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression

Jiaqi Gu, Ben Keller, Jean Kossaifi +3

Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting infe…

cs.LG2022★ 2 cited

An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design

Mingjie Liu, Haoyu Yang, Zongyi Li +7

Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulations of optical and chemical models that are co…

quant-ph2022★ 17 cited

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

Hanrui Wang, Pengyu Liu, Jinglei Cheng +10

Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been rel…

cs.ET2022★ 12 cited

NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

Jiaqi Gu, Zhengqi Gao, Chenghao Feng +4

Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation…

cs.AR2022★ 1 cited

TAG: Learning Circuit Spatial Embedding From Layouts

Keren Zhu, Hao Chen, Walker J. Turner +4

Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with…

cs.ET2022★ 1 cited

Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration

Hanqing Zhu, Keren Zhu, Jiaqi Gu +4

Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly…