260 citations · 582 across the 29 of their papers we have counts for
6 papers · 1 filter
How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya
Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling…
Coordinated Scheduling for MoE LLM Serving
Yifan Sun, Zhexiang Zhang, Jiantong Jiang +5
Serving Mixture-of-Experts (MoE) large language models (LLMs) is challenging because dynamic request workloads interact with sparse expert routing, creating both data-parallel (DP)…
Dynamic Menu-Based Pricing for Electric Vehicle Charging with Vehicle-to-Grid Integration
Mozhdeh Hematiboroujeni, Pierre Le Bodic, Adel N. Toosi +1
The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging ca…
Multi-Layer Scheduling for MoE-Based LLM Reasoning
Yifan Sun, Gholamreza Haffari, Minxian Xu +2
Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, but serving them efficiently at scale remains a critical challenge due to their substant…
LLM-Driven Intent-Based Privacy-Aware Orchestration Across the Cloud-Edge Continuum
Zijie Su, Muhammed Tawfiqul Islam, Mohammad Goudarzi +1
With the rapid advancement of large language models (LLMs), efficiently serving LLM inference under limited GPU resources has become a critical challenge. Recently, an increasing n…
DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting
Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya
Circuit cutting decomposes a large quantum circuit into smaller subcircuits executed independently; expectation values are recovered by classically combining subcircuit outcomes. P…