An Approximate Method for Solving the Quantum Many-Body Problem

K. W. Wong, P. C. W. Fung

Виктор Павлович Маслов

Hiroki Saito

Motivated by the recent successful application of artificial neural networks to quantum many-body problems [G. Carleo and M. Troyer, Science {\bf 355}, 602 (2017)], a method to calculate the ground state of the Bose-Hubbard model using a feedforward neural network is proposed. The results are in good agreement with those obtained by exact diagonalization and the Gutzwiller approximation. The method of neural-network quantum states is promising for solving quantum many-body problems of ultracold atoms in optical lattices.

Walter Greiner

An Min Wang, Feng Xu

We propose a way to solve BCS-type pairing model by to exactly solve its spin-analogy in the subspace. The advantages of our method are to avoid to directly deal with the approximate procedure and to transfer an exponentially complicated problem to a polynomial problem. Moreover, it builds the theoretical foundation of quantum simulation of pairing model and can be used to check the precision of quantum simulation. It is also helpful to understand the many-body quantum theory.

Walter Greiner