Speaker: Nobuyuki Yoshioka (RIKEN)

Date: July 6th, 16:00 -

Style: Webiner via Zoom

Title: Quantum many-body Simulation by Neural Networks

Abstract:

Neural networks have been successfully applied to many machine learning tasks due to the increasing computational resource and advancement of optimization techniques. Recently it has been shown that their high representability is valid not only for classical data such as images or voices, but also for quantum many-body wavefunctions [1]. The demension-free structure of the ansatz helps capture quantum entanglement, and hence is expected to be applied in a situation where one observes failure of tensor networks, which are one of the state-of-the-art simulation techniques in quantum many-body systems. In this presentation, we introduce the neural network as ansatz and review its properties and applications. In particular, we discuss the exact representations, entanglement structures, and optimization techniques for ground states.

Also, we discuss the application to solve open quantum many-body systems [2, 3].

[1] G. Carleo and M. Troyer, Science 355, 602 (2017).

[2] N. Yoshioka and R. Hamazaki, Phys. Rev. B 99, 214306 (2019).

[3] N. Yoshioka et al., in preparation.