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.
Automated near-term quantum algorithm discovery for molecular ground states
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Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.
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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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Fine-Tuning Large Language Models for Quantum Reasoning
Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.