Work

QSPLIT: A GUI-based testing framework for quantum split learning

Year: 2026

Type: software-paper

Abstract: The implementation of Quantum Neural Networks (QNN) is challenging because it requires specialized knowledge of quantum mechanics, posing a significant obstacle for developers and limiting broader adoption. In this paper, we present QSPLIT, a GUI-based testing framework for efficient testing of QNN architectures within quantum split learning environments. QSPLIT allows developers to provide a subset of QNN components as target code for validation, while automatically generating the remaining components as dummy code to construct a complete architecture. The framework executes parallel split learning across multiple target–dummy code combinations, providing GUI-based tools for real-time log tracking, result comparison, and code export. By integrating these functionalities, QSPLIT streamlines the development and validation of QNN models. Its effectiveness was demonstrated through experiments on MedNIST, a medical imaging dataset.

Source: SoftwareX

DOI: 10.1016/j.softx.2026.102621

Authors: Jae Hyun Cho, Min Jeon, Yae Jin Kwon, Kon-Woo Kwon, Youn Kyu Lee

Institutions: Chung-Ang University, Hongik University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funders: Chung-Ang University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea