QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline

Tianyi Bao, Ruizhe Zhong, Xinyu Ye, Yehui Tang, Junchi Yan
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:2953-2967, 2025.

Abstract

Quantum Error Mitigation (QEM) has emerged as a pivotal technique for enhancing the reliability of noisy quantum devices in the Noisy Intermediate-Scale Quantum (NISQ) era. Recently, machine learning (ML)-based QEM approaches have demonstrated strong generalization capabilities without sampling overheads compared to conventional methods. However, evaluating these techniques is often hindered by a lack of standardized datasets and inconsistent experimental settings across different studies. In this work, we present QEM-Bench, a comprehensive benchmark suite of twenty-two datasets covering diverse circuit types and noise profiles, which provides a unified platform for comparing and advancing ML-based QEM methods. We further propose a refined ML-based QEM pipeline QEMFormer, which leverages a feature encoder that preserves local, global, and topological information, along with a two-branch model that captures short-range and long-range dependencies within the circuit. Empirical evaluations on QEM-Bench illustrate the superior performance of QEMFormer over existing baselines, underscoring the potential of integrated ML-QEM strategies.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-bao25c, title = {{QEM}-Bench: Benchmarking Learning-based Quantum Error Mitigation and {QEMF}ormer as a Multi-ranged Context Learning Baseline}, author = {Bao, Tianyi and Zhong, Ruizhe and Ye, Xinyu and Tang, Yehui and Yan, Junchi}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {2953--2967}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/bao25c/bao25c.pdf}, url = {https://proceedings.mlr.press/v267/bao25c.html}, abstract = {Quantum Error Mitigation (QEM) has emerged as a pivotal technique for enhancing the reliability of noisy quantum devices in the Noisy Intermediate-Scale Quantum (NISQ) era. Recently, machine learning (ML)-based QEM approaches have demonstrated strong generalization capabilities without sampling overheads compared to conventional methods. However, evaluating these techniques is often hindered by a lack of standardized datasets and inconsistent experimental settings across different studies. In this work, we present QEM-Bench, a comprehensive benchmark suite of twenty-two datasets covering diverse circuit types and noise profiles, which provides a unified platform for comparing and advancing ML-based QEM methods. We further propose a refined ML-based QEM pipeline QEMFormer, which leverages a feature encoder that preserves local, global, and topological information, along with a two-branch model that captures short-range and long-range dependencies within the circuit. Empirical evaluations on QEM-Bench illustrate the superior performance of QEMFormer over existing baselines, underscoring the potential of integrated ML-QEM strategies.} }
Endnote
%0 Conference Paper %T QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline %A Tianyi Bao %A Ruizhe Zhong %A Xinyu Ye %A Yehui Tang %A Junchi Yan %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-bao25c %I PMLR %P 2953--2967 %U https://proceedings.mlr.press/v267/bao25c.html %V 267 %X Quantum Error Mitigation (QEM) has emerged as a pivotal technique for enhancing the reliability of noisy quantum devices in the Noisy Intermediate-Scale Quantum (NISQ) era. Recently, machine learning (ML)-based QEM approaches have demonstrated strong generalization capabilities without sampling overheads compared to conventional methods. However, evaluating these techniques is often hindered by a lack of standardized datasets and inconsistent experimental settings across different studies. In this work, we present QEM-Bench, a comprehensive benchmark suite of twenty-two datasets covering diverse circuit types and noise profiles, which provides a unified platform for comparing and advancing ML-based QEM methods. We further propose a refined ML-based QEM pipeline QEMFormer, which leverages a feature encoder that preserves local, global, and topological information, along with a two-branch model that captures short-range and long-range dependencies within the circuit. Empirical evaluations on QEM-Bench illustrate the superior performance of QEMFormer over existing baselines, underscoring the potential of integrated ML-QEM strategies.
APA
Bao, T., Zhong, R., Ye, X., Tang, Y. & Yan, J.. (2025). QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:2953-2967 Available from https://proceedings.mlr.press/v267/bao25c.html.

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