Work

QEMOS: a scalable quantum error mitigation method to overcome qubit sensitivity

Year: 2025

Type: article

Abstract: Compared with traditional computers, quantum computers can provide exponential acceleration for certain critical fields. However, the coupling of quantum systems with the environment, along with the intrinsic characteristics of quantum systems, has collectively introduced quantum noise, which has emerged as a significant impediment to the development of quantum computing. Quantum error mitigation (QEM) has been proposed as an alternative solution in the noisy intermediate-scale quantum era. In recent years, with the rise of artificial intelligence, machine learning-based QEM technology has received attention from the industry. However, the latest machine learning-based QEM techniques have limitations, especially their inability to mitigate errors in the quantum circuits whose number of qubits exceeds the number of qubits in the training set, and their tendency to amplify noise when constructing feature sets. This paper proposes QEMOS, a novel random forest-based machine learning model that utilizes a new feature dataset incorporating quantum computer backend properties, with feature dimensionality reduction enabling decoupling from the number of qubits. The model is trained and tested using six different simulators from Qiskit and a real quantum computer tianyan-176. It is worth noting that this model overcomes the limitation of sensitivity to the number of qubits, which was the main problem of previous methods. When trained on 5–9 qubit circuits, the model achieves a probability of correct mitigation of 86.38% on 2–13 qubit circuits, though this efficacy is observed primarily for circuits exhibiting high-probability outputs and decreases as all output probabilities approach zero. Compared to the baseline, the model demonstrates a 31.74% error reduction on test sets with more qubits than the training set. On real quantum computer, testing shows an average error reduction of 67.5%.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae16fc