AutoFid: Adaptive and Noise-Aware Fidelity Measurement for Quantum Programs via Circuit Graph Analysis
This program is tentative and subject to change.
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 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 12mTalk | AutoFid: Adaptive and Noise-Aware Fidelity Measurement for Quantum Programs via Circuit Graph Analysis Research Papers | ||
11:12 12mTalk | 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 12mTalk | 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 12mTalk | 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 12mTalk | 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 12mTalk | 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 12mTalk | 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 | ||