Hybrid Quantum Investment Optimization with Minimal Holding Period

Understand

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.