2026-10-12 · arXiv Quantum Physics
arXiv:2610.13709v1 Announce Type: new
Abstract: Parameterized Quantum Circuits have emerged as a central framework for variational quantum algorithms in the NISQ regime, with major applications in quantum simulation, combinatorial optimization, and, more recently, quantum machine learning (QML). By developing a new approach for computing dynamical Lie algebras associated with quantum circuits combining parametrized gates and non-parametrized entangling gates, we aim to extend the applicability of recent algebraic results to architectures that are actually used in quantum machine learning.
原文链接：https://arxiv.org/abs/2610.13709
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