Quantum Computing applied to finance is gaining momentum. This is thanks to the finance industry having a lot of hard problems to solve and Quantum Computing reaching a point of technological readiness where it’s able to start tackling these problems. Having said that, it’s still early days and it is not yet clear exactly how best to merge quantum theory and finance or what we could expect from a quantum-fintech future. In this article we catch up with mathematician David Orrell, author of Quantum Economics and Finance: An applied mathematics introduction, to get his take on a quantum economy.
David, you have been researching prediction and forecasting for quite some time now, and your earlier books (most notably: Apollo’s Arrow: The Science of Prediction and the Future of Everything) touched on the difficulties involved in modelling highly complex systems. Considering economics is perhaps one of the most complex systems we can think of, how do your ideas on forecasting in your recent book (Quantum Economic: The New science of money) compared to your earlier thoughts?
– When I wrote Apollo’s Arrow, what struck me about economics was that economists had invented a theory, the efficient market hypothesis, to explain why they couldn’t predict: all information was immediately priced in, so changes were due to random news, and no one could beat the markets. The quantum view is very different. The reason we can’t accurately predict markets has nothing to do with efficiency, it is because they are complex systems. In Quantum Economics I argue that the role of economists should be closer to that of doctors – instead of trying to predict the exact moment of a crisis, they should warn of dangers and suggest solutions. The problem with economics isn’t that economists failed to accurately predict for example the 2007/8 financial crisis, it is that their policies and models helped bring about the crisis in the first place, by creating a false sense of security.
So your saying complex problems like the dynamics of stock market prediction could be better explained through quantum mechanics, essentially a quantum interpretation of money. Now some would say these ideas are quite radical. What would you say to this, and where do you see your concepts fitting in with mainstream paradigm?
– I would say that it is quite radical, and it doesn’t fit in with the mainstream paradigm – but as I have long argued in books such as Economyths, economics needs radical change! Mainstream economics typically evolves by taking ideas from areas such as behavioural psychology and watering them down in such a way that core ideas can be left intact. For example, economists talk about “frictions” which slow adjustment to equilibrium, without ever quite letting go of the idea of rational utility-optimising equilibrium. The quantum approach is based on a different kind of logic that isn’t compatible with the idea of rational equilibrium, so it can’t be incorporated in the same way – instead it offers a genuine alternative.
It’s definitely an interesting notion, but what do you think are the main potential benefits the financial sector could gain from incorporating these concepts of a quantum interpretation of economics?
– The financial sector can benefit because the quantum approach accurately reflects the properties of money and gives a realistic model of the financial system. Mainstream economics downplays or ignores things like irrational behaviour or the dynamics of money, so most people in areas like quantitative finance don’t take it very seriously anyway. A secondary benefit is that the models are native to quantum computers.
That’s a good point, especially since quantum computing is already being investigated by several major financial institutions for applications such as optimization, risk analysis, market prediction etc. However, these approaches still treat the financial aspects on a classical setting. Do you think your concepts on the quantum economy would be integrated to such quantum computing techniques and if so, where do you think it would be best to apply them?
– One application is option pricing using the quantum walk, which is detailed in my book Quantum Economics and Finance: An Applied Mathematics Introduction. The Oxford University spinoff Quantum Dice contributed a chapter to the new second edition, describing a design for a photonic device to run the algorithm. Other areas discussed in the book include quantum agent-based modelling, which can include things like entanglement between agents, and modelling asset markets as quantum systems – another good book on this is Quantum Markets: Physical Theory of Market Microstructure, by Jack Sarkissian.
You raise an interesting point here, where use case algorithms shape the development and determine readiness of the underlying hardware. Now, quantum computing has made huge strides over the past few years, from technological breakthroughs that just keep coming, to the emergence of novel and innovative potential applications. Most predictions point to major adoption, especially in finance, within the next five years. What is your take on this?
– I agree that finance will be one of the first areas to benefit, but as you point out the focus so far has been on using quantum computers to run classical algorithms. I think the biggest problem in finance isn’t the speed of the computers, it is the nature of the algorithms. So the focus should be instead on quantum algorithms that are native to quantum devices. We don’t need to wait for full-blown quantum computers.
And of course, the algorithms we run on such quantum devices should capture the complexity of the systems we model, again bringing us back to a quantum interpretation of finance. So, one of the main points you bring across in Quantum Economics is that the mathematics underlying quantum mechanics is more universal than it is typically give credit for. Besides physics, quantum theory has already been applied to cognition and decision making. What other areas do you see it being valuable for or would applied to in the future?
– Quantum probability is a natural fit for many problems where we are trying to compute the propensity for a system to evolve in different ways. Quantum social science is a very broad area – check out the quantum social science bootcamp in July, run by Ohio University: https://u.osu.edu/quantumbootcamp/. Topics include quantum applications to cognition, game theory, international relations, humour, Buddhism, and even finance! Project Q in Sydney is doing an online symposium on 24-25 June with contributions from physicists, philosophers, artists, novelists, and so on: https://projectqsydney.com/q-symposia/quantum-meta-physics-heuristics-aesthetics-ethics/. There has long been a kind of taboo in scientific circles about applying quantum ideas to other areas, but after a century or so that is finally starting to lift.
These are indeed exciting times. In your opinion, considering the rise of machine learning, quantum computing and big data, what is the future of prediction/forecasting as a science and what are the current barriers hold it back?
– It is interesting that the models used in quantum cognition, while very simple, resemble the basic building blocks of the circuits used in quantum AI. And it will be fascinating to see what comes out of these machines when they are hooked up to big data and so on. At the same time, I think that predictions of the sort that we really care about – such as the future state of the planet – are difficult not because our computers aren’t elaborate enough, but because the systems can’t be reduced to mathematical models in the first place. So certain types of prediction may improve, but don’t expect perfect weather forecasts a month out!
If you found this interview interesting, and are looking to learn more about quantum theory applied to finance, we highly recommend reading David Orrell’s book: Quantum Economics and Finance: An Applied Mathematics Introduction
Written in clear and accessible language, this book covers the essential mathematics behind economic and finance topics such as quantum cognition, option pricing, and quantum game theory, and delves into the nuts and bolts of quantum mechanics, the principles of quantum economic modelling, and the basics of quantum computer logic. The book is aimed at anyone who wants to understand the quantum ideas working their way into economics and finance, without getting drowned in wave equations.