Test automation plays a crucial role in modern software development, ensuring faster releases and higher software quality. However, optimizing parallel test execution remains a challenge due to resource constraints and scheduling inefficiencies. This research explores the potential of quantum computing, specifically quantum annealing using D-Wave systems, to enhance test execution efficiency. By formulating test suite scheduling as a combinatorial optimization problem, we leverage quantum annealing to achieve optimal test distribution across available resources. Our proposed approach significantly reduces execution time and improves resource utilization compared to classical optimization techniques. Experimental results demonstrate the effectiveness of quantum-enhanced scheduling, highlighting the potential of quantum computing in revolutionizing test automation.
Test automation plays a crucial role in modern software development, ensuring faster releases and higher software quality. However, optimizing parallel test execution remains a challenge due to resource constraints and scheduling inefficiencies. This research explores the potential of quantum computing, specifically quantum annealing using D-Wave systems, to enhance test execution efficiency. By formulating test suite scheduling as a combinatorial optimization problem, we leverage quantum annealing to achieve optimal test distribution across available resources. Our proposed approach significantly reduces execution time and improves resource utilization compared to classical optimization techniques. Experimental results demonstrate the effectiveness of quantum-enhanced scheduling, highlighting the potential of quantum computing in revolutionizing test automation.
This paper explores the application of quantum computing, specifically quantum annealing using D-Wave systems, to optimize parallel test execution in software test automation. The work formulates test scheduling as a Quadratic Unconstrained Binary Optimization (QUBO) problem, incorporating execution time, resource cost, and test dependencies. Experimental results demonstrate that quantum-enhanced scheduling significantly reduces total execution time and improves resource utilization compared to classical scheduling techniques such as Round-Robin and Genetic Algorithms. The study highlights the practical potential of quantum computing for improving CI/CD pipelines and large-scale automated testing.
Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.
This study introduces an innovative application of Parallel Quantum Annealing (PQA) to concurrently solve multiple NP-hard problems on a quantum annealer, leveraging unused qubits on D-Wave's Pegasus architecture to enhance efficiency and accuracy. While parallel execution of different problems on the same quantum annealing device has been previously explored, our application extends the current PQA method, which typically solves a single problem multiple times or different problems with minor embedded combinatorial optimization. This study primarily focuses on three NP-hard problems relevant to integrated circuit design: the Graph Coloring Problem (GCP), the Minimum Vertex Cover Problem (MVCP), and the Graph Partitioning Problem (GPP). After formulating these problems as Quadratic Unconstrained Binary Optimization (QUBO), they were embedded into the D-Wave quantum annealing machine, and the annealing process was run on the combined QUBO. The results were subsequently decoded into individual problems. Our results, quantified via the Time-To-Target (TTT) metric, showcased enhanced efficiency and accuracy in reaching target solutions, indicating a substantial improvement over traditional Quantum Annealing (QA) and Simulated Annealing (SA) methods. The research illustrates the potential of quantum computing for solving complex real-world problems more efficiently and emphasizes the importance of leveraging all available qubits in quantum annealers. The findings can significantly contribute to the development and optimization of quantum computing methods and have broad application implications, particularly in areas where NP-hard problems are prevalent.
Quantum computing offers the potential to address complex computational challenges, particularly in areas such as simulation and optimisation. Despite this promise, current gate-based quantum devices remain limited in scale and reliability, constraining their practical use. In this context, quantum annealing (QA) emerges as a viable and currently accessible alternative for solving optimisation problems, as it employs adiabatic quantum evolution to efficiently explore large and complex search spaces. This makes it a natural candidate for search-based software testing in the Cyber-Physical Systems (CPS) domain, which demands effective test cases due to their complexity and combination of continuous and discrete domains. This work explores the use of quantum annealing to enhance test case generation for CPS through a mutation-based approach. We employ quantum annealing to identify areas on test cases that can be improved and apply mutations to those areas. We encode test case mutation as a binary optimisation problem, and use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave’s quantum annealer to find the solution. As a main contribution, we offer insights into how quantum annealing can advance testing methodologies by empirically evaluating the correlation between problem size, hardware limitations, and the effectiveness of the results. Moreover, we compare the proposed method against state-of-the-art classical optimisation algorithms, targeting efficiency (time to generate test cases) and effectiveness (fault detection rates). Our results indicate that quantum annealing may enable faster test case generation (when problem decomposition, sampling and scheduling strategies are applied) while achieving comparable fault detection performance to state-of-the-art alternatives.
Quantum computing has emerged as a powerful tool to efficiently solve computational challenges, particularly in simulation and optimisation. However, hardware limitations prevent quantum computers from achieving the full theoretical potential. Among the quantum algorithms, quantum annealing is a prime candidate to solve optimisation problems. This makes it a natural candidate for search-based software testing in the Cyber-Physical Systems (CPS) domain, which demands effective test cases due to their safety-critical nature. This work explores the use of quantum annealing to enhance test case generation for CPS through a mutation-based approach. We encode test case mutation as a binary optimisation problem, and use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave's quantum annealer to find the solution. As a main contribution, we offer insights into how quantum annealing can advance software testing methodologies by empirically evaluating the correlation between problem size, hardware limitations, and the effectiveness of the results. Moreover, we compare the proposed method against state-of-the-art classical optimisation algorithms, targeting efficiency (time to generate test cases) and effectiveness (fault detection rates). Results indicate that quantum annealing enables faster test case generation while achieving comparable fault detection performance to state-of-the-art alternatives.
Job Shop Scheduling is a combinatorial optimization problem of particular importance for production environments where the goal is to complete a production task in the shortest possible time given limitations in the resources available. Due to its computational complexity it quickly becomes intractable for problems of interesting size. The emerging technology of Quantum Annealing provides an alternative computational architecture that promises improved scalability and solution quality. However, several limitations as well as open research questions exist in this relatively new and rapidly developing technology. This paper studies the application of quantum annealing to solve the job shop scheduling problem, describing each step required from the problem formulation to the fine-tuning of the quantum annealer and compares the solution quality with various classical solvers. Particular attention is devoted to aspects that are often overlooked, such as the computational cost of representing the problem in the formulation required by the quantum annealer, the relative qubits requirements and how to mitigate chain breaks. Furthermore, the impact of advanced tools such as reverse annealing is presented and its effectiveness discussed. The results indicate several challenges emerging at various stages of the experimental pipeline which bring forward important research questions and directions of improvement.