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Quantum computing in Financial Services, worry or be happy?


Posted on: April 18, 2019 by Frederik Kerling

In December 2014, the Commonwealth Bank of Australia (CBA) became one of the first commercial organizations, to invest significantly in Quantum Computing. This was long before the large American companies Microsoft, Intel, IBM, and Google, made the topic popular. In April 2017, they increased this amount with an additional 14$ million on top of the original 5$ million. In November 2017, Allianz and RBS joined the 45$ million investment group for quantum computing, with Fujitsu, CME group and Accenture. The Financial services sector is, of all market sectors, the most ‘surprising’ investor in quantum computing technology. Why is this, and what do they know?

Payment networks and Crypto products

One of the first things that spring to mind are payment networks. The world’s economy revolves around digital transaction. If the basis of availability, integrity and confidentiality is attacked via a quantum attack, this is obviously bad for business. Telecom companies like KPN are actively spreading the word that networking as we know it is under threat. And they are right, getting our public networks quantum safe, is a major non-trivial challenge that takes a lot of time. However, it is not a technical challenge, and a lot of the crucial financial data is run via low-latency networks. These are often privately owned, and fixing encryption in these networks is a much simpler task.

Then there are the cryptographic products, whose blockchains like trust technologies are notoriously quantum unsafe. They form a challenge, as with major blockchain examples, that updating the formalism almost always causes a hard-fork[1], which invalidates the single source of truth principle. But most banks don’t use public blockchain and have the trust and power to transition to quantum-safe trust technologies. So, although there are security worries for the Financial Services sector, it cannot be the leading cause of investment.

Practical use cases for Financial Services

In 2014, a short scour of the internet learned that CBA wanted to use a quantum computer to replace the work currently done by HPC via Monte Carlo simulations. Here lies a hint: classical binary computers are not great in handling statistics. There are significant combinatorial challenges where finding an optimum between different risks scales exponentially with the amount of assets under consideration. Right now, these calculations take several hours and are all approximations of possible optimal solutions to a problem, they are rarely the best.

Introducing quantum computers: quantum computers are by their natural properties, statistical machines. Let me rephrase that, they are statistics in their most pure form. This means that whatever you have been taught about statistics are just simple examples that we can humanly fathom. Just imagine trying to calculate the combinatorial possible outcomes of a single share price. There is quickly a near infinite amount of possibilities that could affect this, which we cannot even describe if we had all the atoms in the universe at our disposal. But a quantum computer can, in fact do this with only a small number of qubits. This means that the combinatorial challenge for a quantum computer scales linearly, rather than the present exponential scaling in classical computing, with the amount of assets under consideration and will be available much sooner than the encryption breaking capabilities.

This means you can for instance calculate the Value at Risk, and the Conditional Value at Risk much quicker and more effectively. This allows you to calculate the financial risks of complicated assets like T-bills or complicated derivatives portfolio accurately, whereas now this is, mathematically seen, just guesswork.

Quantum computers’ impact on the Financial Services sector

Almost all financial business is about evaluating the probabilities of positive outcomes, and eliminating negative outcomes, to achieve an acceptable risk with an agreeable profitable outcome. For over 60 years, the methods used to resolve this have been optimized to fit the available technological capabilities. Now, there are machines available that can do this radically differently and with a much better pedigree. These machines handle these specific computational challenges so favourably that the FS sector must invest to prepare its business for the new functionality. Those that do not, will quickly see their market shares dwindle as they lose competitiveness.

So, there are bright happy applications for the financial markets, that will make the sector more profitable, and less risky. It can even potentially stabilize the global economy as actual financial risks are becoming better predictable and more transparent. In our recent Thought Leadership publication Journey 2022 we describe how we get to these solutions for the Financial Services sector, and why they will be available sooner than you might think. I was hence perhaps wrong to say there is nothing to worry about, after all; can you afford to be late with the technology or not?

[1] A hard fork is a permanent divergence from a single source of truth, creating 2 distinctly different ledgers. https://www.investopedia.com/terms/h/hard-fork.asp

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About Frederik Kerling
Head of FinTech, Senior Quantum Expert and member of the Scientific Community
Frederik Kerling is head of FinTech within the Financial Services & Insurance industry at Atos, where he is part of the portfolio and partnerships team. Frederik started his career as theoretical physicist specializing in Quantum engineering, moved through 7 years of cybersecurity consulting, and now focusses on business development for the financial sector. He is internationally active within the quantum community, develops patents, and teaches a course bridging the gap between business and technology at the university of Amsterdam. In his spare time he can be found exercising, gaming, teaching, and discussing quantum fundamentals.

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