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.

本研究基于现代投资组合理论原则，利用实际的金融数据统计生成参数化的投资组合优化问题样本，将问题转换成可由模拟模拟模拟模拟模拟的二次二进制优化问题，研究了多种运行量子计算的方式，最终发现采用 “贪心局部搜索 + 反向退火” 方案的时间效率最优，比正向量子退火平均快 100 倍以上。