Agile-Quantum Model Predicts Quantum Software Project Success, Reduces Costs

The Agile-Quantum Software Project Success Prediction Model (AQSSPM) is a new genetic algorithm model developed to predict the success of quantum software development projects. The model was created after identifying 19 potential challenges to agile-quantum project success.

Using the Genetic Algorithm with Naive Bayes Classifier and Logistic Regression, the AQSSPM improved project success probability from around 54% to nearly 100% and reduced costs. Quantum Computing presents unique challenges for software development, requiring specific processes and methods. The AQSSPM aims to provide effective practices to mitigate these challenges and predict the costs and efforts required for scaling agile methods in quantum software development.

What is the Agile-Quantum Software Project Success Prediction Model (AQSSPM)?

The Agile-Quantum Software Project Success Prediction Model (AQSSPM) is a novel genetic algorithm model developed by a team of researchers including Arif Ali Khan, Muhammad Azeem Akbar, Valtteri Lahtinen, Marko Paavola, Mahmood Niazi, Mohammed Naif Alatawi, and Shoayee Dlaim Alotaibi. The model is designed to predict the success of quantum software development projects. Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored.

The AQSSPM was developed after identifying 19 causes of challenging factors that could potentially impact agile-quantum project success. A survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17% to 99.68% with cost reductions from 0.463 to 0.403. Similarly, GA with LR increased success rates from 55.52% to 98.99% and costs decreased from 0.496 to 0.409 after 100 iterations. Both methods showed a strong positive correlation in causes ranking with no significant difference between them.

How Does Quantum Computing Impact Software Development?

Quantum Computing (QC) has emerged as a significant area of interest for industrial practitioners and academic researchers worldwide. Its potential to bring transformative changes across various industries is undeniable. This potential is evident in the investments and efforts of technology leaders such as IBM, Google, and Microsoft, who are actively working to harness QC for solving complex computational challenges. However, as QC machines operate on the principles of quantum mechanics, the development of software applications for them presents unique challenges.

The development process for QC applications closely mirrors traditional software development, underscoring the importance of a well-structured engineering process to address the complexities of QC. Therefore, refined Software Engineering (SE) methodologies are necessary to fully realize the benefits of QC. A key component in operationalizing QC systems is quantum software. This software, requiring comprehensive stack support and innovative tools and techniques, demands specific processes and methods tailored to create software systems based on quantum mechanics principles.

What are the Challenges in Quantum Software Development?

The field of Quantum Software Engineering (QSE) is still evolving. Currently, it primarily relies on hybrid quantum and classical processes, tools, and methods necessitating careful coordination between classical and quantum systems. In this regard, traditional classical iterative and agile methodologies for developing quantum software could significantly benefit QSE activities. However, more evidence is needed to directly connect agile methods to quantum software development.

Drawing from previous research, many view existing traditional agile methods as well-suited for quantum software development. They also identified potential challenges perceived to hinder scaling traditional agile methods in the quantum domain. These challenges fall into four main categories: Knowledge and awareness, Sustainable scaling, Quantum-aware tools and technologies, and Standards and specifications.

How Can the AQSSPM Help Mitigate These Challenges?

The AQSSPM aims to extend the previous study by exploring more in-depth the causes behind these challenges and providing effective best practices to mitigate them. Based on the study results, a model to predict the costs and efforts required for scaling agile methods in the quantum software development domain was introduced. This model utilizes a nature-inspired optimization algorithm, the Genetic Algorithm (GA), to evaluate the likelihood of success in terms of cost.

Within the GA, a measure called the fitness function was designed, aiming for the highest success rate of agile quantum software projects while considering costs. This optimization model was tested using two prediction models, the Naive Bayes Classifier (NBC) and Logistic Regression (LR). The results showed significant improvements in project success probability and cost reductions, demonstrating the potential of the AQSSPM in predicting the success of quantum software development projects.