Developing a quantum computing algorithm that outperforms its classical counterpart is widely viewed as a major milestone for the field. We achieve this milestone by offering an explicit, efficiently implementable algorithm that solves a fundamental problem in investing. One would want faster algorithms for better models at the same scale before worrying about learning the entire wealth distribution. We propose efficient quantum algorithms for both of these key subproblems.For example, quantum computers can efficiently reverse-engineer private shares to attain an accurate estimate of price-sensitive inside information. In recent years, the role of artificial intelligence has grown considerably in the operational decision-making process and, in particular, in the financial services industry. This paper takes advantage of the progress in quantum computing, addressing the problem of wealth distribution prediction in a big data set, a key problem in the deployment of trading strategies. Through a high-performance cloud computing architecture, we assess the impact of quantum computing technologies in comparison with the classical approach through several prediction models and analytical methodologies in both machine and deep learning.