  Genetic algorithms, which mimic evolutionary processes to solve optimization
problems, can be enhanced by using powerful semi-local search algorithms as
mutation operators. Here, we introduce reverse quantum annealing, a class of
quantum evolutions that can be used for performing families of quasi-local or
quasi-nonlocal search starting from a classical state, as novel sources of
mutations. Reverse annealing enables the development of genetic algorithms that
use quantum fluctuation for mutations and classical mechanisms for the
crossovers -- we refer to these as Quantum-Assisted Genetic Algorithms (QAGAs).
We describe a QAGA and present experimental results using a D-Wave 2000Q
quantum annealing processor. On a set of spin-glass inputs, standard (forward)
quantum annealing finds good solutions very quickly but struggles to find
global optima. In contrast, our QAGA proves effective at finding global optima
for these inputs. This successful interplay of non-local classical and quantum
fluctuations could provide a promising step toward practical applications of
Noisy Intermediate-Scale Quantum (NISQ) devices for heuristic discrete
optimization.
