Development and application of new numerical techniques for correlated electron models

Yusuke Nomura, Masatoshi Imada

With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation.

R. D. Sedgewick

Shan-Wen Tsai

X. Zotos

In this article, the development of a novel numerical simulation method based on the Lanczos technique and the microcanonical statistical ensemble is discussed. It allows the evaluation of finite temperature dynamic correlations for quantum many-body systems on lattices larger than hitherto possible. To demonstrate the applicability of the method, a series of recently obtained results for one-dimensional spin and electronic models are reviewed.

Tôru Sakai

Shigetoshi Sota, Takanori Sugimoto, Takami Tohyama

この論文は国立情報学研究所の電子図書館事業により電子化されました。