Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections
quant-ph
Submitted: 2026-09-03
Updated: 2026-09-03
Terminology
Sources
- Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
- Opportunities and limitations of explaining quantum machine learning
- Subtleties in the trainability of quantum machine learning models
- Barren Plateaus in Variational Quantum Computing
- The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ans\"atze
- Lie-algebraic classical simulations for quantum computing
- Classification of dynamical Lie algebras for translation-invariant 2-local spin systems in one dimension
- A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
- Showcasing a Barren Plateau Theory Beyond the Dynamical Lie Algebra
- All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
- One-loop formulas for $H\rightarrow Z \nu_l\bar{\nu}_l$ for $l = e,\mu, \tau$ in 't Hooft-Veltman gauge
- Quantum machine learning beyond kernel methods
- Gradients and frequency profiles of quantum re-uploading models
- Constrained and Vanishing Expressivity of Quantum Fourier Models
- Concentration of Data Encoding in Parameterized Quantum Circuits
- Direct Gradient Computation for Barren Plateaus in Parameterized Quantum Circuits
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