Quantum Computing and Optimization: Case Study on Solar Cell Efficiency on NISQ System
Pratik De Sarkar, K. K. Ghosh, Tanmay Sinha RoyIntroduction:
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.
Methods:
The Quantum Approximate Optimization Algorithm (QAOA) is used for optimization. Some classical optimization algorithms are also used for compare complexities.
Results:
The quantum optimization performed much better in terms of time and space complexity relative to the classical algorithms.
Discussion:
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.
Conclusion:
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.