Quantum Technology Initiative Journal Club

Europe/Zurich
513/R-070 - Openlab Space (CERN)

513/R-070 - Openlab Space

CERN

15
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Michele Grossi (CERN)
Description

Weekly Journal Club meetings organised in the framework of the CERN Quantum Technology Initiative (QTI) to present and discuss scientific papers in the field of quantum science and technology. The goal is to help researchers keep track of current findings and walk away with ideas for their own research. Some previous knowledge of quantum physics would be helpful, but is not required to follow the talks.

To propose a paper for discussion, contact: michele.grossi@cern.ch

Zoom Meeting ID
63779300431
Host
Michele Grossi
Alternative host
Matteo Robbiati
Passcode
55361000
Useful links
Join via phone
Zoom URL
    • 16:00 17:00
      CERN QTI Journal CLUB
      Convener: Dr Michele Grossi (CERN)
      • 16:00
        Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits 40m

        TITLE: Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits

        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.

        URL: https://arxiv.org/abs/2512.16674

        Speaker: Saverio Monaco