QNAS applies multi-objective NAS with a SuperCircuit and NSGA-II to discover compact HQNN architectures that trade off accuracy against runtime and cutting overhead, achieving 97.16% on MNIST (8 qubits), 87.38% on Fashion-MNIST (5 qubits), and 100% on Iris (4 qubits).
A comparative analysis and noise robustness evaluation in quantum neural networks,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
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
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QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
QNAS applies multi-objective NAS with a SuperCircuit and NSGA-II to discover compact HQNN architectures that trade off accuracy against runtime and cutting overhead, achieving 97.16% on MNIST (8 qubits), 87.38% on Fashion-MNIST (5 qubits), and 100% on Iris (4 qubits).
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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.
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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.