Title: Reverse Quantum Annealing Approach to Portfolio Optimization Problems,
Abstract: We investigate a hybrid quantum-classical solution method to the
mean-variance portfolio optimization problems. Starting from real financial
data statistics and following the principles of the Modern Portfolio Theory, we
generate parametrized samples of portfolio optimization problems that can be
related to quadratic binary optimization forms programmable in the analog
D-Wave Quantum Annealer 2000Q. The instances are also solvable by an
industry-established Genetic Algorithm approach, which we use as a classical
benchmark. We investigate several options to run the quantum computation
optimally, ultimately discovering that the best results in terms of expected
time-to-solution as a function of number of variables for the hardest instances
set are obtained by seeding the quantum annealer with a solution candidate
found by a greedy local search and then performing a reverse annealing
protocol. The optimized reverse annealing protocol is found to be more than 100
times faster than the corresponding forward quantum annealing on average.