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.
Demonstrating quantum advantage in hybrid quantum neural networks for model capacity,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
Systematic exploration of hybrid quantum neural networks on a CKD dataset finds that compact architectures with encodings like IQP and Ring entanglement deliver the best accuracy-robustness-efficiency trade-off.
citing papers explorer
-
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.
-
Hybrid Quantum-Classical Neural Architecture Search
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
-
Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
Systematic exploration of hybrid quantum neural networks on a CKD dataset finds that compact architectures with encodings like IQP and Ring entanglement deliver the best accuracy-robustness-efficiency trade-off.