GAT-QNN uses a two-stage genetic algorithm to train macroCircuits and select efficient microCircuits for hybrid quantum neural networks, reporting 22-23% accuracy gains on 4-class MNIST across backends.
An introduction to quantum machine learning,
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GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks
GAT-QNN uses a two-stage genetic algorithm to train macroCircuits and select efficient microCircuits for hybrid quantum neural networks, reporting 22-23% accuracy gains on 4-class MNIST across backends.