Using maximally entangled training data exponentially flattens the loss landscape of highly expressive quantum models, limiting the loss improvement achievable in a fixed-size neighborhood.
Warm-starting and quantum comput- ing: A systematic mapping study.ACM Comput
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Loss Behavior in Supervised Learning with Entangled States
Using maximally entangled training data exponentially flattens the loss landscape of highly expressive quantum models, limiting the loss improvement achievable in a fixed-size neighborhood.