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Harsha R. Gaikwad

Publications and source records attributed to Harsha R. Gaikwad.

3 recordsLinked to original sources

Quantum Software Architecture Framework (QSAF): A Component-Based Framework for Designing Hybrid Quantum-Classical Systems

Quantum software development has largely focused on algorithms, with limited attention to software architecture. As computing moves toward hybrid quantum-classical systems, this gap limits scalability, reusability, and engineering rigor. This study introduces a component-based quantum software architecture framework (QSAF) for hybrid quantum-classical software systems, enabling developers to transition from circuit-level design to system-level reasoning. We identified 34 reusable quantum circuit primitives across seven functional categories and reinterpreted them as architectural components with explicit interfaces and design-relevant constraints. These components are further characterized using non-functional dimensions such as circuit depth, error sensitivity, and information flow, enabling a structured analysis of design trade-offs. The proposed QSAF framework establishes a multi-level abstraction hierarchy linking quantum gates, circuit primitives, algorithmic structures, and hybrid system architectures. Through this approach, common workflows, particularly hybrid quantum-classical workflows such as variational quantum algorithms, can be systematically decomposed, compared, and optimized. By making the architectural structure and trade-offs explicit, this study provides a foundation for quantum software engineering, supporting modular design, reuse, and informed architectural decision-making in quantum application development.

cs.SE↗

Comparative Analysis of GPGPU based ACO and PSO Algorithm for Employee Scheduling Problems

Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are simple, easy to implement, their robustness to control parameters, and their computational efficiency when compared with mathematical algorithms and other heuristic optimization techniques. The calculation in PSO and ACO is very simple. Compared with the other developing calculations, it occupies the bigger optimization ability and it can be completed easily. It is used to solve many NP-Hard problems. Employee Scheduling is a real-life NP-Hard problem faced by many organizations. Self-scheduling in all situations is not always practical and possible. Nurse Rostering is related to highly constrained resource allocation problem into slots in a legal shift Earlier the problem was solved using different heuristic algorithms. In this dissertation, we have proposed, GPGPU based parallelization of PSO and ACO to solve Employee scheduling problems. To parallelize both algorithms, a master-slave approach is used. The BCV 8.13.1 data set is used for experimentation purposes. Analysis of results is done based on mean, standard deviation, standard mean error. Keywords: Employee Scheduling, Parallelization, PSO, GPGPU.

cs.DC↗

Overview and Applications of GPGPU Based Parallel Ant Colony Optimization

Ant Colony Optimization algorithm is a magnificent heuristics technique based on the behavior of ants. Parallel computing is a means to achieve the desired results in commensurable execution time. Parallelization of Ant Colony Optimization is utilized to solve large and complex problems. This paper discusses a review of different parallelization approaches for Ant Colony Optimization and its various applications. Parallel Ant Colony Optimization has proved to be a successful approach for highly constrained problems such as routing, scheduling, timetabling, etc. Parallelization of Ant Colony Optimization reduces the execution time, increases the size of the problem, etc.

cs.DC↗