The development of quantum software systems, which use quantum bits (Qubits) and quantum gates (Qgates), presents unique challenges. A study has explored the use of agile software development approaches in this field, identifying 19 factors that could hinder project success. The researchers used a Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to create an Agile Quantum Software Project Success Prediction Model (AQSSPM). The model significantly improved project success rates and reduced costs. The study also highlighted the need for careful coordination between classical and quantum systems in Quantum Software Engineering (QSE).
Quantum Software Development and Agile Approaches
Quantum software systems, which utilize quantum bits (Qubits) and quantum gates (Qgates), represent a new realm in software engineering. These systems can solve complex problems more efficiently than their classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinder the adoption of traditional agile approaches in quantum software projects and develop an Agile Quantum Software Project Success Prediction Model (AQSSPM).
Methodology and Results
The study identified 19 causes of challenging factors impacting 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.
Quantum Computing and Software Engineering
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.
Quantum Software Engineering Challenges
The field of Quantum Software Engineering (QSE) is still evolving. It primarily relies on hybrid quantum and classical processes, tools, and methods necessitating careful coordination between classical and quantum systems. The challenges fall into four main categories: Knowledge and awareness, Sustainable scaling, Quantum aware tools and technologies, and Standards and specifications.
Agile Methods in 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. This paper aims to extend the previous study by exploring more in-depth the causes behind these challenges and providing effective best practices to mitigate them.
Genetic Algorithm in Agile Quantum Software Projects
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.
Quantum Computing and Technological Advancements
In the rapidly evolving world of technological advancements, QC has emerged as a pivotal innovation. Based on the principles of quantum mechanics such as quantum superposition and entanglement, QC platforms offer a new computational paradigm. Unlike traditional binary digits which strictly adhere to a 0 or 1 state, QC operates based on Qubits that can simultaneously exist in a superposition of both states, represented as 0 and 1. This shift from classical bits to Qubits signifies a profound transformation in the landscape of computing, pushing the boundaries of what is achievable with computational processes.
A novel genetic algorithm model for predicting the success of quantum software development projects has been proposed in a recent article titled “Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum Software Development Project”. The article, published on January 16, 2024, is authored by Arif Ali Khan, Muhammad Azeem Akbar, Valtteri Lahtinen, and Marko Paavola. The source of the article is arXiv, a repository of electronic preprints approved for publication after moderation, hosted by Cornell University.
