Variational optimization of two-dimensional MERA with GPGPU

Our study focuses on the potential of Multi-scale Entanglement Renormalization Ansatz (MERA) to describe the ground state of quantum matter, particularly at quantum critical points. Most numerical calculations are limited to one-dimensional models because working with two-dimensional lattices is difficult due to the large space and time complexity. In this study, we utilize a near-optimal contraction tree optimization and slicing technique to distribute computation over multiple GPGPUs. This approach significantly enhances the speed of evaluating various two-dimensional MERA ansatz and provides promising insights into the potential of MERA for understanding quantum matter.

*This work was supported by JST COI-NEXT Program Grant No.JPMJPF2014, Grant-in-Aid for Transformative Research Areas "The Natural Laws of Extreme Universe—A New Paradigm for Spacetime and Matter from Quantum Information" (KAKENHI Grants No. JP21H05182 and No. JP21H05191) and KAKENHI Grants No. JP20K03766.

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Bulletin of the American Physical Society

2024 APS March Meeting

Monday–Friday, March 4–8, 2024; Minneapolis & Virtual

Session S58: Quantum Many-Body Systems and Methods III

Abstract: S58.00003 : Variational optimization of two-dimensional MERA with GPGPU*

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