Thesis title — TBD

Motivation, contributions and thesis outline — to be written.

Overview of the CMS experiment, the collision-to-discovery workflow, and the cost of Monte Carlo simulation that motivates faster methods.

Introduction to qubits and quantum gates, current hardware realizations, and classical simulation methods (statevector, density matrix, tensor networks) and their regimes of simulability.

Parametrized quantum circuits as machine learning models, including the parameter-shift rule for gradient estimation — to be expanded.

Introduces the Quantum Angle Generator (QAG): model architecture, angle encoding schemes, and training — to be expanded.

Comparison of classical generative models, quantum GANs and quantum circuit Born machines against the Quantum Angle Generator.

Symbolic Pauli propagation in the Heisenberg picture, used for gradient-enabled classical pre-training of parametrized quantum circuits ahead of deployment on hardware.

Summary of results, future work (including a high-energy-physics application of Pauli propagation), and concluding remarks.