We implement two different methods for solving many-body quantum mechanical systems from scratch, a variational Monte Carlo (VMC) method and a restricted Boltzmann Machine (RBM). We explore a confined Bose-Einstein condensate within a harmonic oscillator potential trap. The VMC uses a single variational parameter in order to describe the wave function and estimate the expectation value for the ground state energy. The RBM uses several tuning parameters such as biases and weights associated with the network to describe the NQS. Both methods use Metropolis sampling technique and gradient descent has been applied as optimization scheme
After reviewing previously published techniques, a new algorithm is presented for optimising variabl...
In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs)...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...
Motivated by recent advances in the representation of ground state wavefunctions of quantum many-bod...
International audienceWe propose a neural-network variational quantum algorithm to simulate the time...
The projective quantum Monte Carlo (PQMC) algorithms are among the most powerful computational techn...
We investigate the use of variational wave functions that mimic stochastic recurrent neural networks...
This work presents a novel realization approach to quantum Boltzmann machines (QBMs). The preparatio...
We introduce the time-dependent variational Monte Carlo method for continuous-space Bose gases. Our ...
We construct a many-body Gaussian variational approach for the two-dimensional trapped Bose gas in t...
International audienceWe introduce the time-dependent variational Monte Carlo method for continuous-...
Neural networks have been recently proposed as variational wave functions for quantum many-body syst...
After reviewing previously published techniques, a new algorithm is presented for optimising variabl...
In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs)...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...
Motivated by recent advances in the representation of ground state wavefunctions of quantum many-bod...
International audienceWe propose a neural-network variational quantum algorithm to simulate the time...
The projective quantum Monte Carlo (PQMC) algorithms are among the most powerful computational techn...
We investigate the use of variational wave functions that mimic stochastic recurrent neural networks...
This work presents a novel realization approach to quantum Boltzmann machines (QBMs). The preparatio...
We introduce the time-dependent variational Monte Carlo method for continuous-space Bose gases. Our ...
We construct a many-body Gaussian variational approach for the two-dimensional trapped Bose gas in t...
International audienceWe introduce the time-dependent variational Monte Carlo method for continuous-...
Neural networks have been recently proposed as variational wave functions for quantum many-body syst...
After reviewing previously published techniques, a new algorithm is presented for optimising variabl...
In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs)...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...