Trainability Beyond Linearity in Variational Quantum Objectives
Barren-plateau results have established exponential gradient suppression as a widely cited obstacle to the scalability of variational quantum algorithms. When and whether these results extend to a given objective has been addressed through loss-specific arguments, but a general structural characterization has remained open. We show that the objective itself admits a fixed-observable representation if and only if the loss is affine in the measured statistics, thereby identifying the exact boundary of the standard concentration-based proof template. Existing transfer results for non-affine losses achieve this reduction under additional assumptions; our characterization implies that such a r...
| Authors | Gordon Ma, Xiufan Li |
|---|---|
| Primary category | quant-ph |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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