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Reverse quantum annealing approach to portfolio optimization problems

Davide Venturelli, Alexei Kondratyev

2019enquantumoptimizationportfolioannealinggenetic algorithms

Abstract

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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 2000QTM. 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.

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Cite This Work

@article{fa45d8b5-b831-49d2-b215-57f0b6487205,
  title={Reverse quantum annealing approach to portfolio optimization problems},
  author={Davide Venturelli and Alexei Kondratyev},
  year={2019},
  language={en}
}
TY  - JOUR
TI  - Reverse quantum annealing approach to portfolio optimization problems
AU  - Davide Venturelli
AU  - Alexei Kondratyev
PY  - 2019
LA  - en
ER  -

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