6 citations · 11 across the 7 of their papers we have counts for
7 papers
Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise
Jinyang Li, Samudra Dasgupta, Yuhong Song +3
Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical com…
Escaping Barren Plateau: Co-Exploration of Quantum Circuit Parameters and Architectures
Yipei Liu, Yuhong Song, Jinyang Li +4
Barren plateaus (BP), characterized by exponentially vanishing gradients that hinder the training of variational quantum circuits (VQC), present a pervasive and critical challenge…
QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers
Jinyang Li, Yuhong Song, Yipei Liu +4
With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computin…
Mera: Memory Reduction and Acceleration for Quantum Circuit Simulation via Redundancy Exploration
Yuhong Song, Edwin Hsing-Mean Sha, Longshan Xu +2
With the development of quantum computing, quantum processor demonstrates the potential supremacy in specific applications, such as Grovers database search and popular quantum neur…
BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge +4
Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more…
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
Panjie Qi, Edwin Hsing-Mean Sha, Qingfeng Zhuge +5
State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be accommodated on resource constrained embedded devices. Moreover, with the development of te…
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
Yuhong Song, Weiwen Jiang, Bingbing Li +6
A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…