Trainability · Algorithms
Simple-DLA barren plateau (g ≃ su(d))
Theorem statement
For a parametrized quantum circuit whose dynamical Lie algebra $\mathfrak{g}$ is simple, $\mathfrak{g} \simeq \mathfrak{su}(d)$ (dimension $d^2-1$, centerless), the loss variance reduces to a single term $$\operatorname{Var}_\theta[\ell] = \frac{P_{\mathfrak{g}}(\rho)\, P_{\mathfrak{g}}(O)}{d^2-1}.$$ In particular, for $d = 2^n$ the dimension $\dim\mathfrak{g} = 4^n-1$ grows exponentially in the qubit count $n$, so the loss exhibits an exponential barren plateau (under the Haar second-moment / Schur hypothesis carried as a named input).
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 quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven, 2018
Lean context
- Lean declaration
QuantumAlg.SimpleSU.main
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