- IonQ and Fidelity Center for Applied Technology scientists describe a new design as a critical first step in the application of quantum computing to Monte Carlo methods.
- The scientists involved in the project said the technique is scalable and has been demonstrated on IonQ hardware for up to 20 qubits.
- Critical Quote: “In finance, accuracy and speed can mean the difference between profit or loss. We believe this technique can provide financial institutions a tool they need to integrate quantum into their workflow and explore novel ways to inform portfolio engineering, retirement planning, and risk management in even the most complex of scenarios.” — Peter Chapman, CEO, IonQ
PRESS RELEASE — IonQ (NYSE: IONQ), an industry leader in quantum computing, in collaboration with the Fidelity Center for Applied Technology (FCAT), today announced an efficient and reliable design as a critical first step in the application of quantum computing to Monte Carlo methods. The first-of-its-kind state preparation technique is scalable and has been demonstrated on IonQ hardware for up to 20 qubits. The achievement may not only benefit financial institutions in tasks like portfolio management, but also other industries including science and engineering, where Monte Carlo algorithms are used.
A research paper outlining the new technique is available here for viewing.
State preparation is a necessary component of many quantum algorithms and is fundamental in expediting Monte Carlo methods, which use randomness to simulate outcomes of complex problems. Financial institutions use Monte Carlo algorithms to understand the relationship between an outcome and multiple variables in complex systems, but their precision is frequently limited by the length of time needed to run the same algorithm repeatedly with different values of the variables. IonQ and FCAT believe that when run on large and accurate quantum computers, this state preparation technique will help these institutions achieve faster results.
“The Fidelity Center for Applied Technology team were early believers in the power of quantum to reshape the field of finance, and we are pleased to announce the development of a first-of-its-kind state preparation technique with them,” said Peter Chapman, CEO, IonQ. “In finance, accuracy and speed can mean the difference between profit or loss. We believe this technique can provide financial institutions a tool they need to integrate quantum into their workflow and explore novel ways to inform portfolio engineering, retirement planning, and risk management in even the most complex of scenarios.”
Today’s announcement is an extension of IonQ’s project with the FCAT® team, during which the two groups issued a paper describing how certain generative quantum machine learning algorithms may provide an advantage over their classical counterparts. Additional information about IonQ’s work with FCAT will be presented at the American Physical Society (APS) Annual Meeting 2023, taking place March 5-10, 2023, in Las Vegas, Nevada.
“The Monte Carlo protocol is an integral component of financial planning, as it helps us understand how several correlated variables interact with each other when one element is changed,” said Adam Schouela, Head of Emerging Technology, Fidelity Center for Applied Technology. “However, current state preparation techniques are either theoretical or have some type of deficiency when scaling. Today, we’re proud to announce alongside IonQ a state preparation algorithm that we believe is scalable and executable on NISQ hardware.”
Today’s announcement follows recent news that IonQ has acquired quantum software compiler Entangled Networks, with the goal of building large-scale quantum computers by enabling computation across multiple distributed quantum processors. Additionally, in 2022 IonQ entered into a multi-million-dollar contract with the U.S. Air Force Research Lab, as well as partnerships with companies like Dell Technologies, Hyundai Motors, Airbus, and GE Research, among others. For more information, visit investors.ionq.com/news.