Trainability ยท Algorithms
Barren-plateau gradient variance
Theorem statement
For a parametrized quantum circuit whose dynamical Lie algebra is the orthogonal direct sum $\mathfrak{g} = \bigoplus_j \mathfrak{g}_j$ of its ideals, the loss variance is the reductive Ragone sum $$\operatorname{Var}_\theta[\ell] = \sum_j \frac{P_{\mathfrak{g}_j}(\rho) P_{\mathfrak{g}_j}(O)}{\dim\mathfrak{g}_j},$$ where $P_{\mathfrak{g}_j}$ is the $\mathfrak{g}_j$-purity (derived from cross-ideal Casimir orthogonality); the simple-Lie-algebra case is its single-ideal corollary. When $\dim\mathfrak{g}$ grows exponentially in the qubit count, an exponential barren plateau follows (given the Haar second-moment / Schur projection the formalization carries as a named hypothesis).
Sources
- A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
Michael Ragone, Bojko N. Bakalov, Frederic Sauvage, Alexander F. Kemper, Carlos Ortiz Marrero, Martin Larocca, M. Cerezo, 2023
- Barren plateaus in variational quantum computing
Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J. Coles, et al., 2024
- Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven, 2018
Lean context
- Lean declaration
QuantumAlg.RagoneReductive.totalVariance_eq
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