Quantum imaginary time evolution steered by reinforcement learning

Quantum imaginary time evolution - a common technique in theoretical studies to prepare ground states of quantum systems - comes with the uneasy requirement to implement non-unitary time evolution in the lab, and while recent solution has been proposed it carries leftover errors. The present work implements reinforcement learning to mitigate such errors in a physics-informed way, demonstrating the efficiency of AI-enhanced algorithms on a quantum computer. 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. We verified the method's validity with the transverse-field Ising model and the Sherrington-Kirkpatrick model. Numerical calculations and experiments on a nuclear magnetic resonance quantum computer illustrate the efficacy. The philosophy of our method, eliminating errors with errors, sheds light on error reduction on near-term quantum devices.

Automated summary

Quantum imaginary time evolution is a common technique used in theoretical studies to prepare ground states of quantum systems. This study proposes a deep reinforcement learning-based method to mitigate algorithmic errors in the quantum imaginary time evolution. The well-trained agent can find an evolution path where most algorithmic errors cancel out, significantly enhancing the fidelity. Numerical calculations and experiments on a nuclear magnetic resonance quantum computer demonstrate the efficacy of the method. The philosophy of eliminating errors with errors sheds light on error reduction on near-term quantum devices.