Case Study

Optimizing portfolios with quantum computing

To find the perfect combination of risk and return faster and more effectively, Raiffeisen Bank International wants to rely on new technologies in the future. Together with Reply, it has paved the way for portfolio optimization with quantum computers.

#QUBO
#QuantumComputing
#PortfolioOptimization
#FinancialServices

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The challenge

Maximum return, minimum risk

Raiffeisen Bank International (RBI) serves around 18.6 million customers across Europe. In order to enable these customers to benefit as much as possible from their investments, RBI continuously optimizes all portfolios. Shares, bonds and other investments should deliver the highest possible return and at the same time suffer as little fluctuation in value as possible. Finding the perfect combination of risk and return requires complex calculations that cannot be scaled endlessly. RBI therefore wants to prepare itself for future challenges.

The solution

Quantum technology in real business

Together with Reply, RBI has explored the potential of quantum computing for portfolio optimization in more detail. To do so, the experts used quantum computing to calculate the particularly complex components of a portfolio optimization that had already been carried out using a conventional computer. This was an exemplary but real use case in order to ensure the future practical usability of the technology.

The quantum algorithm respected the specific conditions and achieved similarly good results as the classical computer. At the same time, it offers a major advantage: It enables simple scaling for extensive calculations in the future.

The technology behind it

Specialized systems with power

For this project, the experts relied on systems from the company D-Wave. While universal quantum computers today still have a very low number of qubits and therefore limited computing capacities, the specialized systems from D-Wave can already perform quite extensive calculations for defined applications.

The only requirement: the right format. In order to be able to use the systems, the portfolio optimization had to be formulated as a so-called QUBO problem. The abbreviation QUBO stands for Quadratic Unconstrained Binary Optimization. Reply's experts supported RBI in converting the portfolio optimization into this form and preparing it in such a way that the calculations required as little computing capacity as possible.

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The result

Profound knowledge for more extensive analyses

Using a small portfolio, RBI and Reply were able to show that quantum computers can already quickly deliver almost optimal optimization results. In the future, quantum computers will be even more powerful and enable ever more comprehensive analyses. This means that RBI will soon be able to include additional asset classes in its calculations and analyze significantly more variations with the help of quantum technology. A clear plus in terms of quality, from which RBI's customers will benefit.

Reply has also succeeded in sharing its innovative spirit with RBI and strengthening its enthusiasm for new technologies. Thanks to the project with Reply, RBI now has extensive knowledge and practical experience in dealing with the new technology. It is therefore ideally equipped for the banking of tomorrow, in which quantum computing will play an increasingly important role.

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RBI regards Austria, where it operates as a leading corporate and investment bank, and Central and Eastern Europe (CEE) as its home market. 12 markets in the region are covered by subsidiary banks, and the RBI Group also includes numerous other financial services companies, for example in the areas of leasing, asset management and M&A. Around 45,000 employees serve 18.6 million customers in around 1,500 branches, the vast majority of which are in the CEE region. The RBI share is listed on the Vienna Stock Exchange. The regional Raiffeisen banks hold around 61,2 percent of RBI, the remainder is in free float. Within the Raiffeisen Banking Group, RBI is the central institution of the regional Raiffeisen banks and other affiliated credit institutions.

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Machine Learning Reply offers customized end-to-end Data Science solutions, covering the entire project life cycle – from initial strategy consulting, data architecture and infrastructure topics, to processing data with quality assurance using Machine Learning algorithms. Machine Learning Reply has extensive expertise in the field of Data Science in all key industries of German HDAX companies. Machine Learning Reply empowers clients to successfully introduce new data-driven business models and to optimize existing processes and products – with a focus on distributed open source and cloud technologies. With the Machine Learning Incubator, the company offers a program to train the next generation of decision-makers, data scientists and engineers.