Portfolio Optimization: Applications in Quantum Computing
26 Pages Posted: 15 Jun 2013 Last revised: 12 Jul 2014
Date Written: February 9, 2014
Portfolio optimization in a quantum computing paradigm is explored. The D-Wave adiabatic quantum computation optimization system is used to determine an optimal portfolio of stocks using binary selection. The stock returns, variances and covariances are modeled in the graph-theoretic maximum independent set (MIS) and weighted maximum independent set (WMIS) structures. These structures are mapped into the Ising model representation of the underlying D-Wave optimizer. The results show different stock selections over a range of predetermined risk thresholds and underlying models. This implementation and following discussion provides a practitioner’s view of what might be accomplished in this framework. The particular models used in the implementations have restricted appeal but do link the financial engineering domain to the quantum computing optimization domain. Further research on model enhancements or different model structures needs to be undertaken to improve its usefulness in comparison to the current industrial domain.
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