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This research advances the mathematical framework for analyzing parameterized quantum circuits, specifically addressing the computation of dynamical Lie algebras in architectures relevant to current quantum machine learning applications. By extending algebraic results to include non-parametrized entangling gates, the work aims to improve the theoretical understanding and optimization capabilities of variational quantum algorithms in the NISQ regime.
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Dynamical Lie Algebras for Fixed-Gate Quantum Ans\"atze

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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.

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.

Quantum Physics (quant-ph); Quantum Algebra (math.QA)

arXiv:2610.13709 [quant-ph]

(or arXiv:2610.13709v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.13709 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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