Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers
By Saeefa Rubaiyet Nowmi · Paper · quant-ph
Building a quantum model involves a tradeoff: how complex the circuit should be, and how much training data it needs. Caro et al. show that models with fewer trainable gates need less training data to generalize well. Q-FLAIR shows that a quantum feature-map circuit can be grown
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