Speaker
Description
Determining the Hamiltonian of a quantum system is essential for understanding its dynamics and validating its behavior. Hamiltonian learning provides a data-driven approach to reconstruct the generator of the dynamics from measurements on the evolved system, enabling the characterization and learning of quantum devices from experimental data. Among its applications, it is particularly important for benchmarking and validating quantum hardware, such as quantum computers and simulators. However, as these devices grow in size and complexity, this task becomes increasingly challenging. To address this, we propose a scalable and experimentally friendly Hamiltonian learning protocol for geometrically local Hamiltonians. It leverages the quantum Zeno effect as a reshaping tool to localize the system's dynamics and then applies quantum process tomography to learn the coefficients of local Hamiltonian terms acting on selected qubits. Unlike existing approaches, our method does not require complex state preparations and uses experimentally accessible, coherence-preserving operations. By decomposing a global Hamiltonian learning problem into independent local learning problems, the proposed framework also provides a natural starting point for learning-based approaches to the characterization and modeling of complex quantum systems. We derive theoretical performance guarantees and demonstrate the feasibility of our protocol through numerical simulations and an experimental implementation on IBM's superconducting quantum hardware, successfully reconstructing the coefficients of a 109-qubit Hamiltonian.
| Topic | Quantum state and process tomography with learning-based approaches |
|---|