4 citations · 4 across the 9 of their papers we have counts for
10 papers
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
Chi Zhang, Neel Vora, Yilun Xu +3
Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliabili…
Orbital AI Computing: Carbon Tradeoffs Across Satellite Scale
Nisha Sarwar, Lei Jiang, Fan Chen
Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems. However,…
LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites
Lei Jiang, Adrian Ildefonso, Daniel Loveless +1
Large language models (LLMs) impose rapidly growing energy demands, creating an emerging energy and carbon crisis driven by large-scale inference. Solar-powered, AI-enabled low Ear…
QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Zhenxiao Fu, Lei Jiang, Fan Chen
Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise. Addressing this limitation requires hardware-facing capabilitie…
CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices
Zhenxiao Fu, Chen Fan, Lei Jiang
LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/…
QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming
Zhenxiao Fu, Lei Jiang, Yilun Xu +2
Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices,…
CarbonScaling: Extending Neural Scaling Laws for Carbon Footprint in Large Language Models
Lei Jiang, Fan Chen
Large language models (LLMs) increasingly follow neural scaling laws that tie performance gains to rapidly expanding computational budgets, raising concerns about the sustainabilit…
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences
Zhenxiao Fu, Fan Chen, Shan Zhou +2
Throughout its lifecycle, a large language model (LLM) generates a substantially larger carbon footprint during inference than training. LLM inference requests vary in batch size,…
IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning
Fan Chen, Shahzeen Attari, Gayle Buck +1
To improve privacy and ensure quality-of-service (QoS), deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing, significantly i…
TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer
Cheng Chu, Zhenxiao Fu, Yilun Xu +4
Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, m…