  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 one 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.
