Quantum-Assisted Genetic Algorithm
This work addresses heuristic discrete optimization problems, offering a potential step toward practical applications for Noisy Intermediate-Scale Quantum (NISQ) devices, though it appears incremental as it builds on existing genetic and quantum annealing methods.
The authors tackled the problem of finding global optima in spin-glass optimization by introducing a Quantum-Assisted Genetic Algorithm (QAGA) that combines quantum fluctuations for mutations with classical crossovers. They demonstrated that QAGA effectively finds global optima on a D-Wave 2000Q processor, outperforming standard quantum annealing which struggles with this task.
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.