Quantum Computing and Optimization: Case Study on Solar Cell Efficiency on NISQ System

Abstract

Quantum computing (QC) leverages principles of quantum mechanics to address computational problems that are challenging for classical approaches. In complex optimization tasks, particularly those involving high-dimensional design spaces, traditional methods such as density functional theory and classical machine learning often face scalability and computational limitations. Quantum optimization techniques offer a promising alternative to overcome these challenges. The objective of this work is to optimize the transport layer thicknesses of the solar cell. A comparative study of computational complexity is also conducted, taking into account some classical algorithms. The Quantum Approximate Optimization Algorithm (QAOA) is used for optimization. Some classical optimization algorithms are also used for compare complexities. The quantum optimization performed much better in terms of time and space complexity relative to the classical algorithms. These results demonstrate the high efficiency of QAOA for complex PV optimization problems and suggest that it may be an alternative to standard optimization methods. QAOA is a promising technique for optimizing solar cell design and could be applied to other computationally intensive optimization problems, including those encountered in various engineering applications.