Quantum Hierarchical Risk Parity by Maxwell Rounds at QuantCon NYC 2017

At QuantCon NYC 2017 Maxwell presented the methodologies and results behind the algorithm that has been developed by 1QBit, named Quantum Hierarchical Risk Parity, or QHRP. This is an extension of the work done by Marcos Lopez de Prado on Hierarchical Risk Parity in his paper "Building Diversified Portfolios that Outperform Out-of-Sample."
QHRP tackles the problem of minimizing the risk of a portfolio of assets using a quantum-inspired approach. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to demonstrate strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data.
The quantum-ready approach to portfolio optimization is based on an optimization problem that can be solved using a quantum annealer. The algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. The results of real market data used to benchmark this approach against other common portfolio optimization methods will be shared in this presentation.
View the White Paper: bit.ly/2k5xTxW.
View the slides: www.slideshare...
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[@mikiallen7733]

are you only sampling by time ? does quantum solver take in to account the possibility of different samplings