AIBullisharXiv – CS AI · 7h ago7/10
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
Researchers introduce QADR, a hybrid quantum-classical machine learning framework that significantly reduces memory requirements for training quantum circuits from exponential O(2^n) to O(n·2^(2d+1)) scaling. By decomposing large quantum circuits into localized sub-circuits, QADR demonstrates superior performance on high-dimensional tasks where conventional quantum machine learning approaches fail, suggesting practical quantum advantage for near-term quantum hardware.