MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
InThe Twelfth International Con- ference on Learning Representations
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QuantumQA dataset and verification-aware RL with adaptive reward fusion enable an 8B LLM to achieve performance competitive with proprietary models on quantum mechanics tasks.
citing papers explorer
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Discovering Data Encoding Strategies for Quantum-Classical Neural Networks Using Monte Carlo Tree Search
MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
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QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning
QuantumQA dataset and verification-aware RL with adaptive reward fusion enable an 8B LLM to achieve performance competitive with proprietary models on quantum mechanics tasks.