(HPC.Wire) IonQ, Inc. has announced an initiative in partnership with GE Research to explore the impact of quantum computing and IonQ’s quantum computers in the pivotal field of risk analysis. The initiative is expected to lay the groundwork for risk management across key sectors including finance, government and others.
“Globally, we’re grappling with incredibly complex systems that impact financial markets, supply chains, and daily business operations; the organizations that do not understand their exposure to risks within these complex systems are increasingly vulnerable,” said Peter Chapman, CEO and President of IonQ. “As we explore how quantum computing could help us calculate — and correct for — these risks, we’re proud to partner with GE, whose forward-thinking team sees that the rise of data availability pairs naturally with quantum computers to find new solutions to these management challenges.”
In the wake of COVID-19, risk management and resilience have become more important than ever. Recent findings show organizations across industries are in need of stronger risk analysis surrounding finance, cybersecurity, third-party relationships and more. With IonQ and GE Research’s new partnership, this analysis could be made possible by the use of copulas in quantum computing. Copulas have a flexible way of depicting relationships between variables; the models are well suited to measure information from multiple sets of random data inputs and distill them into a single variable. Because quantum hardware is uniquely suited to this type of analysis, the teams aim to explore breakthrough implications for risk management solutions.
“Quantum computing has the potential to accelerate disruptive innovation for many industries,” said Dave Vernooy, a Senior Executive and Digital Technologies Leader at GE Research. “A big focus for us is finding ways to make quantum real across our industries. We can do this by collaborating with leading quantum computing vendors such as IonQ to show how quantum-based approaches can help organizations better model risk and its impacts, and we’re excited to see how this work can be extended into classification, machine learning and network partitioning.”