Q-PhotoNAS applies genetic algorithm search to jointly optimize classical preprocessing, phase encoding, and photonic circuit structure for hybrid quantum-classical models, reporting 99.44% and 98.78% accuracy on Digits and MNIST with projected photonic QPU inference times.
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A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead
10 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 10representative citing papers
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).
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
Proposes multi-component bridge states outside cat code space for syndrome extraction in teleportation-based cat code QEC when nonlinear interactions are limiting.
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 models using structured entanglement keep high accuracy on MNIST, OrganAMNIST and CIFAR-10 while lowering adversarial attack success rates and raising the computational cost of generating attacks.
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.
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.
A correlation-guided hybrid quantum-classical model using QAOA achieves 84.6% accuracy on crime pattern classification with reduced trainable parameters compared to classical machine learning baselines.
citing papers explorer
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Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
Q-PhotoNAS applies genetic algorithm search to jointly optimize classical preprocessing, phase encoding, and photonic circuit structure for hybrid quantum-classical models, reporting 99.44% and 98.78% accuracy on Digits and MNIST with projected photonic QPU inference times.
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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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A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
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Use of Faulty States in Cat-Code Error Correction
Proposes multi-component bridge states outside cat code space for syndrome extraction in teleportation-based cat code QEC when nonlinear interactions are limiting.
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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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QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Hybrid quantum-classical models using structured entanglement keep high accuracy on MNIST, OrganAMNIST and CIFAR-10 while lowering adversarial attack success rates and raising the computational cost of generating attacks.
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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.
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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.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.
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Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics
A correlation-guided hybrid quantum-classical model using QAOA achieves 84.6% accuracy on crime pattern classification with reduced trainable parameters compared to classical machine learning baselines.