CaixaBank, the leading financial group in Spain, and D-Wave announced the business results for two significant financial quantum hybrid computing applications for investment portfolio optimization and investment hedging calculation.
The quantum hybrid applications have significantly decreased compute time to solve complex financial problems, improving investment portfolio optimization, increasing a bond portfolio internal rate of return (IRR), and minimizing the capital needed for hedging operations, as a result of their collaboration.
CaixaBank’s Life insurance and Pensions company, VidaCaixa, leveraged D-Wave’s Leap™ quantum cloud service and quantum hybrid solvers – which combine the strengths of classical and quantum computing – to build a quantum computing application within their investment portfolio selection and allocation, and within their portfolio hedging efforts. With this project, CaixaBank Group becomes the first known financial services company in the world to apply quantum computing in investment hedging in the insurance sector. The group is evaluating putting the application into regular production not only in VidaCaixa but in other areas in the organization, over the coming months.
The CaixaBank Group team utilized D-Wave’s quantum hybrid solver services to code a faster algorithm, which markedly reduces the computing time necessary to reach an optimal solution to improve the investment portfolio hedging. What normally took the bank several hours of compute time was reduced to just minutes via quantum computing technology – an up to 90% decrease in compute time over the traditional solution. This reduction of compute time facilitates increased modeling complexity, allowing for a more dynamic model that is better adapted to real-time markets; optimizes the invested capital while maintaining constant risk levels; and improves the hedging decision-making process.
When it comes to investment portfolio selection and allocation, the algorithm rapidly generates portfolios that can be optimized against a higher variety of constraints in a reduced timeframe. The result was a successful application which optimizes IRR by 10% in a chosen portfolio of bonds.
