182 citations · 364 across the 29 of their papers we have counts for
29 papers
TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator
Meng Zhang, Dennis Yin, Nicholas Gangi +4
Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency…
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Tianlong Chen, Zhenyu Zhang, Hanrui Wang +6
Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving…
Qplacer: Frequency-Aware Component Placement for Superconducting Quantum Computers
Junyao Zhang, Hanrui Wang, Qi Ding +7
Noisy Intermediate-Scale Quantum (NISQ) computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing.…
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers
Hanrui Wang, Pengyu Liu, Kevin Shao +5
Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable li…
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Hanrui Wang, Yilian Liu, Pengyu Liu +10
Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can…
DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting
Hanrui Wang, Pengyu Liu, Yilian Liu +4
Quantum hardware suffers from high error rates and noise, which makes directly running applications on them ineffective. Quantum Error Correction (QEC) is a critical technique towa…
Q-Pilot: Field Programmable Qubit Array Compilation with Flying Ancillas
Hanrui Wang, Daniel Bochen Tan, Pengyu Liu +4
Neutral atom arrays have become a promising platform for quantum computing, especially the field programmable qubit array (FPQA) endowed with the unique capability of atom movement…
Atomique: A Quantum Compiler for Reconfigurable Neutral Atom Arrays
Hanrui Wang, Pengyu Liu, Daniel Bochen Tan +6
The neutral atom array has gained prominence in quantum computing for its scalability and operation fidelity. Previous works focus on fixed atom arrays (FAAs) that require extensiv…
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator
Hanqing Zhu, Jiaqi Gu, Hanrui Wang +7
The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficien…
M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +3
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency.…
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers
Zixuan Jiang, Jiaqi Gu, Hanqing Zhu +1
Transformers have achieved great success in machine learning applications. Normalization techniques, such as Layer Normalization (LayerNorm, LN) and Root Mean Square Normalization…
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…
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…
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…
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…
Delving into Effective Gradient Matching for Dataset Condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +1
As deep learning models and datasets rapidly scale up, network training is extremely time-consuming and resource-costly. Instead of training on the entire dataset, learning with a…
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
Wei Shi, Hanrui Wang, Jiaqi Gu +4
Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) var…
QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
Hanrui Wang, Zirui Li, Jiaqi Gu +3
Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (N…
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement
Hanqing Zhu, Jiaqi Gu, Chenghao Feng +4
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with nea…
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +5
Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achiev…
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
Jiaqi Gu, Hyoukjun Kwon, Dilin Wang +6
Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly des…
A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning
Chenghao Feng, Jiaqi Gu, Hanqing Zhu +4
The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous…
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +3
Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility…
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
Hanrui Wang, Jiaqi Gu, Yongshan Ding +4
Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQ…