Quantifying the advantages of applying quantum approximate algorithms to portfolio optimisation
This quantum algorithm uses QAOA to solve discrete global minimum variance portfolio problems (DGMVP) by encoding asset weights into binary variables. The method involves designing specific initial states, cost operators, and mixing operators for the circuit. Results show that warm-started initial states improve solution quality, though achieving significant quantum advantage requires drastically reducing noise.