Quantum imaginary time evolution steered by reinforcement learning

• URL: http://arxiv.org/abs/2105.08696v3
• Date: Tue, 15 Mar 2022 11:08:00 GMT
• Title: Quantum imaginary time evolution steered by reinforcement learning
• Abstract summary: We propose a deep reinforcement learning-based method to steer the quantum imaginary time evolution. In our scheme, the well-trained agent can find the subtle evolution path where most algorithmic errors cancel out. The philosophy of our method, eliminating errors with errors, sheds light on error reduction on near-term quantum devices.
• Score: 0.47831562043724657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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