ASE 2025
Sun 16 - Thu 20 November 2025 Seoul, South Korea

This program is tentative and subject to change.

Mon 17 Nov 2025 11:00 - 11:12 at Grand Hall 4 - Efficiency & Fairness 1

Quantum computers in the Noisy Intermediate- Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing approaches either assume prior knowledge of noise models or rely on historical circuit data, limiting their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling principled estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving statistical guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed shot and learning based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.

This program is tentative and subject to change.

Mon 17 Nov

Displayed time zone: Seoul change

11:00 - 12:30
Efficiency & Fairness 1Research Papers at Grand Hall 4
11:00
12m
Talk
AutoFid: Adaptive and Noise-Aware Fidelity Measurement for Quantum Programs via Circuit Graph Analysis
Research Papers
Tingting Li Zhejiang University, Ziming Zhao Zhejiang University, Jianwei Yin Zhejiang University
11:12
12m
Talk
HybridSIMD: A Super C++ SIMD Library with Integrated Auto-tuning Capabilities
Research Papers
Haolin Pan Institute of Software, Chinese Academy of Sciences;School of Intelligent Science and Technology, HIAS, UCAS, Hangzhou;University of Chinese Academy of Sciences, Xulin Zhou Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Mingjie Xing Institute of Software, Chinese Academy of Sciences, Yanjun Wu Institute of Software, Chinese Academy of Sciences
11:25
12m
Talk
PEACE: Towards Efficient Project-Level Performance Optimization via Hybrid Code Editing
Research Papers
Xiaoxue Ren Zhejiang University, Jun Wan Zhejiang University, Yun Peng The Chinese University of Hong Kong, Zhongxin Liu Zhejiang University, Ming Liang Ant Group, Dajun Chen Ant Group, Wei Jiang Ant Group, Yong Li Ant Group
11:38
12m
Talk
CoTune: Co-evolutionary Configuration Tuning
Research Papers
Gangda Xiong University of Electronic Science and Technology of China, Tao Chen University of Birmingham
Pre-print
11:51
12m
Talk
It's Not Easy Being Green: On the Energy Efficiency of Programming Languages
Research Papers
Nicolas van Kempen University of Massachusetts Amherst, USA, Hyuk-Je Kwon University of Massachusetts Amherst, Dung Nguyen University of Massachusetts Amherst, Emery D. Berger University of Massachusetts Amherst and Amazon Web Services
12:04
12m
Talk
When Faster Isn't Greener: The Hidden Costs of LLM-Based Code Optimization
Research Papers
Tristan Coignion Université de Lille - Inria, Clément Quinton Université de Lille, Romain Rouvoy University Lille 1 and INRIA
12:17
12m
Talk
United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
Research Papers
Minghua He Peking University, Chiming Duan Peking University, Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Siyu Yu The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Lingzhe Zhang Peking University, China, Weijie Hong Peking university, Jing Han ZTE Corporation, Yifan Wu Peking University, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University