Quantum imaginary time evolution steered by reinforcement learning

Understand

The quantum imaginary time evolution is a powerful algorithm for preparing the ground and thermal states on near-term quantum devices.

• However, algorithmic errors induced by Trotterization and local approximation severely hinder its performance.
• Here we propose a deep reinforcement learning-based method to steer the evolution and mitigate these errors.
• In our scheme, the well-trained agent can find the subtle evolution path where most algorithmic errors cancel out, enhancing the fidelity significantly.