Many software engineering problems require significant computational effort to be performed. From the automatic generation and optimization of test suites to the use of artificial intelligence techniques to assess and/or improve software code quality, these are all complex activities to perform in a reasonable amount of time.

Recently, Quantum Computing has emerged, a new technology that makes use of quantum mechanics principles to provide solutions to those problems that are too complex to be solved with traditional technologies. 

Over the last years, multiple solutions relying on the use of quantum computing are becoming available. Quantum optimization algorithms have been proposed with the aim of providing a more performing alternative to traditional optimization techniques. Similarly, the use of quantum machine learning is gaining researchers' and practitioners' interest with the availability of quantum alternatives to traditional ML algorithms (e.g., QSVM).

In such scenario, researchers are starting to apply quantum computing solutions to solve software engineering problems, among others, as well as to improve the performance of the currently available solutions.

Therefore, the adoption of quantum computing seems to be one of the most promising ways to solve software engineering-specific problems or improve the performance of existing solutions. This calls researchers and practitioners to make an effort in devising and evaluating new methodologies regarding the application of Quantum Programming for Software Engineering (QP4SE).