Quantum neural networks to simulate many body quantum systems

Speaker: 

Bartłomiej Gardas, Uniwersytet Jagielloński

Date: 

05/04/2019 - 13:00

We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.

Author: Zbigniew Puchała | Post date: 29.03.2019 | Last modified: 09.05.2019

Historia zmian

Data aktualizacji: 09/05/2019 - 22:06; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)

We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.
Data aktualizacji: 02/04/2019 - 11:21; autor zmian: ()

We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.
Data aktualizacji: 29/03/2019 - 14:24; autor zmian: Zbigniew Puchała (zbyszek@iitis.pl)

We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.
Data aktualizacji: 29/03/2019 - 14:24; autor zmian: Zbigniew Puchała (zbyszek@iitis.pl)