aiqbench analysis
This research introduces a symbolic Pauli propagation method to enable efficient gradient-based pre-training for quantum circuits without relying on costly on-chip training. By deriving analytic functions of circuit parameters and applying controlled truncations, the approach scales beyond current classical simulation limits while maintaining accuracy in tasks like solving spin models via VQE. This advancement addresses critical bottlenecks in Quantum Machine Learning by facilitating scalable optimization workflows prior to hardware deployment.
Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits
arXiv:2512.16674v4 Announce Type: replace 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 frequency 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 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 a spin model, showing that accurate results can be achieved with a scalable and computationally efficient procedure.
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 frequency 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 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 a spin model, showing that accurate results can be achieved with a scalable and computationally efficient procedure.
arXiv:2512.16674 [quant-ph]
(or arXiv:2512.16674v4 [quant-ph] for this version)
Full text under the publisher's open licence · 158 words
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