A randomized algorithm recovers the exact Pauli decomposition of k-sparse n-qubit matrices in poly(n, k, log(1/δ)) time with high probability under sparse query access.
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A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead
14 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative 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.
Deterministic gradient-norm bounds in variational QML control DP-SGD clipping bias, so quantum models retain more accuracy than matched classical models under the same privacy budget.
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.
On Iris-scale hybrid quantum neural networks, ZNE, PEC, DDD, and LRE do not reliably outperform the unmitigated noisy baseline across five simulated noise channels.
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.
A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.
A genetic-algorithm framework searches 19 photonic hybrid-network design choices and reports 99.44% (Digits) and 98.78% (MNIST) validation accuracy in simulation.
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.
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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An efficient Pauli decomposition algorithm for structured matrices
A randomized algorithm recovers the exact Pauli decomposition of k-sparse n-qubit matrices in poly(n, k, log(1/δ)) time with high probability under sparse query access.
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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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Private training in quantum machine learning
Deterministic gradient-norm bounds in variational QML control DP-SGD clipping bias, so quantum models retain more accuracy than matched classical models under the same privacy budget.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
On Iris-scale hybrid quantum neural networks, ZNE, PEC, DDD, and LRE do not reliably outperform the unmitigated noisy baseline across five simulated noise channels.
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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
A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.
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Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
A genetic-algorithm framework searches 19 photonic hybrid-network design choices and reports 99.44% (Digits) and 98.78% (MNIST) validation accuracy in simulation.
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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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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.