Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks

Area of Science:

• Quantum Physics
• Computational Physics
• Machine Learning

Background:

• Simulating nonequilibrium real-time evolution in isolated quantum matter is computationally challenging.
• Two-dimensional systems are a focus of experimental quantum simulation efforts.

Purpose of the Study:

• To present a versatile and efficient machine learning-inspired approach for quantum many-body simulations.
• To overcome limitations in simulating large systems and long-time dynamics.

Main Methods:

• Utilized an artificial neural network encoding of quantum many-body wave functions.
• Addressed key challenges in simulating time evolution for improved accuracy.

Main Results:

• Studied the dynamics of the two-dimensional transverse-field Ising model.
• Observed collapse and revival oscillations of ferromagnetic order.
• Achieved simulation timescales comparable to or exceeding state-of-the-art tensor network methods.

Conclusions:

• The developed machine learning approach offers a powerful tool for quantum simulations.
• This method enhances the accurate description of complex quantum dynamics in two dimensions.