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This paper investigates the experimental performance of a discrete portfoliooptimization problem relevant to the financial services industry on thegate-model of quantum computing. We implement and evaluate a portfoliorebalancing use case on an idealized simulator of a gate-model quantumcomputer. The characteristics of this exemplar application include trading indiscrete lots, non-linear trading costs, and the investment constraint. Wedesign a novel problem encoding and hard constraint mixers for the QuantumAlternating Operator Ansatz, and compare to its predecessor the QuantumApproximate Optimization Algorithm. Experimental analysis demonstrates thepotential tractability of this application on Noisy Intermediate-Scale Quantum(NISQ) hardware, identifying portfolios within 5% of the optimal adjustedreturns and with the optimal risk for a small eight-stock portfolio.

Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz