An Explainable Fuzzy Logic-Based Approach to Quantum Backend Selection
Year: 2026
Type: conference-paper
Abstract: Quantum backend selection is emerging as a critical step toward achieving quantum computing utility. Indeed, the accuracy of quantum circuit execution strongly depends on the specific quantum machine used, each characterized by a distinct computational noise pattern. Currently, machine learning models for quantum backend selection are being proposed to address this challenge effectively. However, these approaches still lack two fundamental aspects: the integration of execution queue information, which impacts waiting time and scheduling efficiency, and the explainability of the selected backend, which is crucial to understand and trust the decision-making process. This paper introduces the first fuzzy-based machine learning approach for quantum backend selection capable of addressing both aspects. Experimental results show that the proposed method achieves competitive performance compared to state-of-the-art crisp classifiers, while selecting the backend by incorporating execution waiting times and exposing the circuit features underlying the selection decision.
Institution: University of Naples Federico II
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7172323837