MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
Computational advantage in hybrid quantum neural networks: Myth or reality?
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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.
Selecting a customized Hermitian observable enables training of QNNs up to 10 qubits under noise for global cost functions, outperforming Pauli observables, while PauliZ works best for local cost functions up to 10 qubits.
citing papers explorer
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MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
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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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HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
Selecting a customized Hermitian observable enables training of QNNs up to 10 qubits under noise for global cost functions, outperforming Pauli observables, while PauliZ works best for local cost functions up to 10 qubits.