Podcast: Venturelli and Kondratyev on quantum annealing
Authors show how quantum theory could aid portfolio construction
In this Quantcast episode, Davide Venturelli and Alexei Kondratyev discuss the results of their ground-breaking research on quantum computing. Venturelli is quantum computing science lead at the Universities Space Research Association (USRA) and a research scientist at NASA. Kondratyev is managing director and head of data analytics, electronic market solutions at Standard Chartered Bank in London.
Together, they collaborated on a research project supported by USRA on the application of quantum computing in finance, and in particular in portfolio construction. Their findings are now published in Beyond Markowitz with quantum annealing.
Quantum computing is, as Venturelli puts it, “the attempt of humanity to use physics to do mathematics”. Quantum physics is exploited as a resource for executing algorithms rather than a problem to deal with. Thanks to physicists’ growing ability to manage its properties, ideas of new applications are numerous, and finance is only the latest field to test the potential speed-up in calculation using this technology. In other fields of research, such as chemistry, quantum computing has already proved to be superior when the number of variables is high.
Its analog version, known as quantum annealing, is now tested for solving problems in areas such as logistics, transportation, scheduling, sampling for machine learning and simulation of materials and systems’ behaviour. Conveniently, it is well-suited to solve optimisation problems. For this reason, Markowitz’s portfolio optimisation model – one of the best-known frameworks in finance – seemed a natural choice for Venturelli and Kondratyev’s experiment.
The authors explain how the research was conducted, what advantages and caveats come with it, the status of the technology and the expectations of its future development.
Index:
0:00 What is quantum computing?
3:22 What is quantum annealing?
6:21 Quantum annealing application to portfolio construction
15:23 Applying it to Markovitz’s optimisation problem
18:05 Obstacles to the implementation of quantum annealers
22:10 Time horizon for the wider implementation of quantum computing
27:10 Experiments vs application to financial markets
30:20 Alternative applications of quantum annealers
36:30 What does a bank need in order to apply quantum computing?
41:45 What’s next for Venturelli and Kondratyev’s collaboration?
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