The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze
arXiv:2309.07902 · doi:10.1038/s41467-024-49910-w
Abstract
Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their dynamical Lie algebra (DLA), a setting that we term Lie algebra Supported Ansatz (LASA). A large variety of commonly used ansätze such as the Hamiltonian Variational Ansatz, Quantum Alternating Operator Ansatz, and many equivariant quantum neural networks are LASAs. In particular, our theory provides, for the first time, the ability to compute the variance of the gradient of the cost function of the quantum compound ansatz. We rigorously prove that, for LASA, the variance of the gradient of the cost function, for a 2-design of the dynamical Lie group, scales inversely with the dimension of the DLA, which agrees with existing numerical observations. In addition, to motivate the applicability of our results for 2-designs to practical settings, we show that rapid mixing occurs for LASAs with polynomial DLA. Lastly, we include potential extensions for handling cases when the observable lies outside of the DLA and the implications of our results.
References in corpus (15)
- Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With Traps
- Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
- Theory of overparametrization in quantum neural networks
- Group-Invariant Quantum Machine Learning
- Equivalence of quantum barren plateaus to cost concentration and narrow gorges
- A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
- Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
- Barren plateaus in quantum tensor network optimization
- Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
- Constrained Optimization via Quantum Zeno Dynamics
- Quantum Deep Hedging
- Quantum Image Segmentation Based on Grayscale Morphology
- Efficient classical algorithms for simulating symmetric quantum systems
- Critical Points in Quantum Generative Models
- Expressivity of Variational Quantum Machine Learning on the Boolean Cube
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