Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with $97.76 \%$ accuracy, $97.92 \%$ precision, $97.76 \%$ recall, and an F 1 -score of $97.77 \%$ , outperforming classical baselines by up to $4 \%$ . These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with $97.76 \%$ accuracy, $97.92 \%$ precision, $97.76 \%$ recall, and an F 1 -score of $97.77 \%$, outperforming classical baselines by up to $4 \%$. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.

IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST)Oct 11, 20250 citations

Quantum Deep Learning Neural Networks

Abu Kamruzzaman, Yousef Salamah Alhwaiti, Avery M. Leider +1 more

Lecture notes in networks and systemsFeb 1, 201926 citations

Code Comprehension using Classical and Quantum CNN

Mobeen W. Alhalabi

Software developers often spend more time reading and understanding existing source code than writing new code. As a result, improving code comprehensibility can significantly enhance software development and maintenance by reducing the cognitive effort required to grasp program functionality. Code comments play a critical role in this process, helping developers navigate and maintain software more efficiently. However, numerous software projects suffer from missing, outdated, or inconsistent comments, forcing developers to infer functionality directly from the code. The design of programming languages also impacts code comprehension; as previous research suggests; different programming paradigms influence the ease with which developers understand code. This insight has driven researchers to explore machine learning (ML) and deep learning (DL) techniques for a variety of software engineering tasks, including testing, vulnerability detection, and source code analysis. Given the rapid growth in this research area, it has become increasingly difficult for the community to keep track of existing approaches and environments. Recently, ML and DL techniques have been widely adopted for tasks such as source code representation, quality analysis, and automated testing. In this paper, we introduce a novel quantum deep learning method that demonstrates superior performance over classical approaches in the comprehension of source code. Our approach highlights the potential of quantum computing to enhance machine learning techniques applied in the software engineering domain.Software developers often spend more time reading and understanding existing source code than writing new code. As a result, improving code comprehensibility can significantly enhance software development and maintenance by reducing the cognitive effort required to grasp program functionality. Code comments play a critical role in this process, helping developers navigate and maintain software more efficiently. However, numerous software projects suffer from missing, outdated, or inconsistent comments, forcing developers to infer functionality directly from the code. The design of programming languages also impacts code comprehension; as previous research suggests; different programming paradigms influence the ease with which developers understand code. This insight has driven researchers to explore machine learning (ML) and deep learning (DL) techniques for a variety of software engineering tasks, including testing, vulnerability detection, and source code analysis. Given the rapid growth in this research area, it has become increasingly difficult for the community to keep track of existing approaches and environments. Recently, ML and DL techniques have been widely adopted for tasks such as source code representation, quality analysis, and automated testing. In this paper, we introduce a novel quantum deep learning method that demonstrates superior performance over classical approaches in the comprehension of source code. Our approach highlights the potential of quantum computing to enhance machine learning techniques applied in the software engineering domain.

Journal of Information Systems Engineering & ManagementMay 19, 20250 citations

A survey on quantum deep learning

Huaiguang Wu, Jiahui Zhang, Lijie Wang +3 more

The Journal of SupercomputingMar 3, 202513 citations

Quantum Kernel Encoding Meets Deep Learning for Secure Software-Defined Networks.

Abubakar Danasabe

Jan 1, 20250 citations

Quantum Computing for Machine Learning

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

Springer eBooksJan 1, 20224 citations

97.76 \%

accuracy,

97.92 \%

precision,

97.76 \%

recall, and an F 1 -score of

97.77 \%

, outperforming classical baselines by up to

4 \%

. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.