# Machine Learning for Practical Quantum Error Mitigation

arXiv: https://arxiv.org/abs/2309.17368 · HTML: https://mlsanity.com/paper/2309.17368/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations](https://mlsanity.com/paper/1911.07979/) (2019)
- [Provably efficient machine learning for quantum many-body problems](https://mlsanity.com/paper/2106.12627/) (2021)
- [Neural Error Mitigation of Near-Term Quantum Simulations](https://mlsanity.com/paper/2105.08086/) (2021)

## Built on by (most cited first, 20 of 20)

- [Artificial intelligence for representing and characterizing quantum systems](https://mlsanity.com/paper/2509.04923/) (2025)
- [Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows](https://mlsanity.com/paper/2402.17911/) (2024)
- [Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models](https://mlsanity.com/paper/2311.01727/) (2023)
- [A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers](https://mlsanity.com/paper/2404.12892/) (2024)
- [Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation](https://mlsanity.com/paper/2405.08100/) (2024)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [Neural Architecture Search for Quantum Autoencoders](https://mlsanity.com/paper/2511.19246/) (2025)
- [Nonstabilizerness Estimation using Graph Neural Networks](https://mlsanity.com/paper/2511.23224/) (2025)
- [Scalable Quantum Error Mitigation with Physically Informed Graph Neural Networks](https://mlsanity.com/paper/2604.16815/) (2026)
- [APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study](https://mlsanity.com/paper/2609.18219/) (2026)
- [Quantum time dynamics mediated by the Yang-Baxter equation and artificial neural networks](https://mlsanity.com/paper/2401.17116/) (2024)
- [Deep Learning for Low-Latency, Quantum-Ready RF Sensing](https://mlsanity.com/paper/2404.17962/) (2024)
- [Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers](https://mlsanity.com/paper/2502.02625/) (2025)
- [Noise tolerance via reinforcement: Learning a reinforced quantum dynamics](https://mlsanity.com/paper/2506.12418/) (2025)
- [A Study on Stabilizer R'enyi Entropy Estimation using Machine Learning](https://mlsanity.com/paper/2509.16799/) (2025)
- [Sample-efficient quantum error mitigation via classical learning surrogates](https://mlsanity.com/paper/2511.07092/) (2025)
- [Scalable Quantum Error Mitigation with Neighbor-Informed Learning](https://mlsanity.com/paper/2512.12578/) (2025)
- [Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning](https://mlsanity.com/paper/2609.20523/) (2026)
- [Weakly Supervised Quantum Error Mitigation](https://mlsanity.com/paper/2609.25555/) (2026)
- [Simulation-Based Quantum System Inference with Neural Posterior Estimation](https://mlsanity.com/paper/2609.34995/) (2026)

## Abstract (arXiv)

Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle. The key to overcoming errors on near-term devices has emerged through the field of quantum error mitigation, enabling improved accuracy at the cost of additional run time. Here, through experiments on state-of-the-art quantum computers using up to 100 qubits, we demonstrate that without sacrificing accuracy machine learning for quantum error mitigation (ML-QEM) drastically reduces the cost of mitigation. We benchmark ML-QEM using a variety of machine learning models -- linear regression, random forests, multi-layer perceptrons, and graph neural networks -- on diverse classes of quantum circuits, over increasingly complex device-noise profiles, under interpolation and extrapolation, and in both numerics and experiments. These tests employ the popular digital zero-noise extrapolation method as an added reference. Finally, we propose a path toward scalable mitigation by using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency. Our results show that classical machine learning can extend the reach and practicality of quantum error mitigation by reducing its overheads and highlight its broader potential for practical quantum computations.