Abstract
In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.
Highlights
In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period
For a detailed description of applications of quantum computing in finance, see Ref.[23]
The most paradigmatic optimization problem in finance is that of portfolio optimization, both in its static and dynamic versions
Summary
In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. For a detailed description of applications of quantum computing in finance, see Ref.[23]. Among these applications, one of the most prominent is quantum optimization. There are many important optimization problems in finance which can be solved more efficiently using quantum computing. According to Modern Portfolio Theory, the optimal investment at a defined level of risk is the one which maximizes profit[30].
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