Bonn 2025 – wissenschaftliches Programm
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QI: Fachverband Quanteninformation
QI 29: Quantum Information: Concepts and Methods I
QI 29.2: Vortrag
Donnerstag, 13. März 2025, 11:30–11:45, HS IV
Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation — •Daniel Alcalde Puente1,2 and Matteo Rizzi1,2 — 1Forschungszentrum Jülich, Institute of Quantum Control, Peter Grünberg Institut (PGI-8), 52425 Jülich, Germany — 2Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany
This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ansätze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation.
Keywords: measurements; Learning; feedback; Varaitional Quantum Circuits