Mathematicians have worked for many years to develop mathematical tools for modeling joint probability distributions with multiple variables. These techniques can be particularly valuable in areas such as finance where it is quite helpful to understand the correlations between three and four stock indexes to help predict future performance. One of the tools that mathematicians use is called a copula which was originally introduced in 1959. Recently, quantum researchers have found a way to express copulas as maximally entangled quantum states on a quantum computer. Together IonQ and GE Research modeled stock prices using this technique with up to four index variables on a hybrid computing confirmation that included both IonQ’s Aria quantum processor and a classical computer. Their results achieved comparable, and in some cases better, outcomes than classical copula modeling. The quantum approach should have an easier time scaling up to support more variables than the classical approach. So we would expect greater quantum advantages as the problems get larger. And this technique of risk modeling is by no means limited to the finance area. Other areas where this could potentially be used include product design, factory operations, supply chain management, and many others. For more information about this research you can view a news release from IonQ here, a blog article with additional detail here, and a technical paper with even more technical details here.

June 23, 2022