Deep reinforcement learning for quantum Hamiltonian engineering

Understand

Engineering desired Hamiltonian in quantum many-body systems is essential for applications such as quantum simulation, computation and sensing.

• Conventional quantum Hamiltonian engineering sequences are designed using human intuition based on perturbation theory, which may not describe the optimal solution and is unable to accommodate complex experimental imperfections.
• Here we numerically search for Hamiltonian engineering sequences using deep reinforcement learning (DRL) techniques and experimentally demonstrate that they outperform celebrated sequences on a solid-state nuclear magnetic resonance quantum simulator.
• As an example, we aim at decoupling strongly-interacting spin-1/2 systems.