Computational Results for a Quantum Computing Application in Real-Life Finance

This article presents the computational results obtained from a real-life implementation of quantum computing application in investment analytics, which algorithm was described by a previously published paper [5]. The new results reported in this article include: 1) speed-up tests based on idealized problems; 2) speed-ups computed on data from a professional platform, accounting for imperfections on very large financial datasets in finance; and 3) speed-ups from using similar data to solve analogous problems such as implied graphs to explain factor cuts. Furthermore, the article addresses the implementation challenges and computational results, in terms of both speed-ups and error rates, along with any other alternative hardware solutions (if available) such as GPUs for certain users who prefer classical computer solutions. That way, users can compare and contrast the state of the art today and in the foreseeable future. Overall, this article provides insights into the potential of quantum computing to revolutionize the field of finance and highlights the progress made in leveraging artificial intelligence technology for solving real-world problems.