A unifying account of warm start guarantees for patches of quantum landscapes
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Abstract (arXiv)
Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantial gradients. Previous work has studied certain classes of parameterized quantum circuits and found example regions where gradients vanish at worst polynomially in system size. Here we present a general bound that unifies all these previous cases and that can tackle physically-motivated ans"atze that could not be analyzed previously. Concretely, we analytically prove a lower-bound on the variance of the loss that can be used to show that in a non-exponentially narrow region around a point with curvature the loss variance cannot decay exponentially fast. This result is complemented by numerics and an upper-bound that suggest that any loss function with a barren plateau will have exponentially vanishing gradients in any constant radius subregion. Our work thus suggests that while there are hopes to be able to warm-start variational quantum algorithms, any initialization strategy that cannot get increasingly close to the region of attraction with increasing problem size is likely inadequate.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2025.
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Citing papers per year: 2025: 4; 2026: 1.
5 papers.
- 2025Enhancing variational quantum algorithms by balancing training on classical and quantum hardwarecited by 3
- 2025Accelerating Inference for Multilayer Neural Networks with Quantum Computerscited by 1
- 2025Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithmscited by 0
- 2025Sample-efficient quantum error mitigation via classical learning surrogatescited by 0
- 2026Ravines in quantum cost landscapes: opportunities for improved VQA predictionscited by 0
- 2026Ravines in quantum cost landscapes: opportunities for improved VQA predictions1 Jul 2026
- 2025Sample-efficient quantum error mitigation via classical learning surrogates10 Nov 2025
- 2025Accelerating Inference for Multilayer Neural Networks with Quantum Computers8 Oct 2025
- 2025Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms16 Jun 2025
- 2025Enhancing variational quantum algorithms by balancing training on classical and quantum hardware20 Mar 2025