Preprint

PUBDB-2025-05683

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits

Monaco, S. (Corresponding author)DESY* ; Slim, J.DESY* ; Rehm, F. ; Krücker, D.DESY* ; Borras, K.DESY*

2026

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Please use a persistent id in citations: doi:10.3204/PUBDB-2025-05683

Report No.: arXiv:2512.16674

Abstract: Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods. By employing Pauli propagation, it is possible to derive a symbolic representation of observables as analytic functions of a circuit's parameters. Although the number of terms in such functional representations grows rapidly with circuit depth, suitable choices of ansatz and controlled truncations on Pauli weights and trigonometric degree components yield accurate yet tractable estimators of the target observables. With the right ansatz design, this approach can be extended to system sizes beyond the reach of classical statevector simulation, enabling scalable training for larger quantum systems. This also enables a form of classical pre-training through gradient-based optimization prior to deployment on quantum hardware. The proposed approach is demonstrated on the Variational Quantum Eigensolver for obtaining the ground state of the ANNNI spin model on 32 qubits, showing that accurate results can be achieved with a scalable and computationally efficient procedure.

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Contributing Institute(s):

1. CMS-Experiment (CMS)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. ENGAGE - Enabling the Next-Generation of Computational Physicists and Engineers (101034267) (101034267)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2026

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