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Moment-matching loss may mislead researchers into believing their quantum models generalize well, when in fact they don't.
Quantum graph neural networks can now achieve scalability and expressivity guarantees, paving the way for practical applications in complex relational data tasks.
Forward gradient estimators can train quantum neural networks orders of magnitude more efficiently than conventional methods, revolutionizing gradient estimation in PQCs.