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.
Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance.Science China Information Sciences, 68(8):1–11, 2025
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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.