Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization

Understand

Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resources and lack scalability.

• Here, we introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics.
• Our approach reconstructs an equivalent quantum circuit representation from measurement data of unknown Hamiltonian evolution without requiring additional assumptions and achieves polynomial sample and computational efficiency.
• Our results have broad applications in various directions.