One of the main challenges of quantum many‐body physics is the exponential growth in the
dimensionality of the Hilbert space with system size. This growth makes solving the …

We develop a variational approach to simulating the dynamics of open quantum many-body
systems using deep autoregressive neural networks. The parameters of a compressed …

Representation by neural networks, in particular by restricted Boltzmann machines (RBMs),
has provided a powerful computational tool to solve quantum many-body problems. An …

Finding optimal correction of errors in generic stabilizer codes is a computationally hard
problem, even for simple noise models. While this task can be simplified for codes with some …

We examine the usefulness of applying neural networks as a variational state ansatz for
many-body quantum systems in the context of quantum information-processing tasks. In the …

In order to solve the errors generated during the operation of quantum computers, quantum
error correction codes are the most effective candidates at the moment. The best choice of …

Quantum information transfer is an information processing technology with high speed and
high entanglement with the help of quantum mechanics principles. To solve the problem of …

An artificial neural network (ANN) with the restricted Boltzmann machine (RBM) architecture
was recently proposed as a versatile variational quantum many-body wave function. In this …

We discuss the relations between restricted Boltzmann machine (RBM) states and the matrix
product states (MPS) for the ground states of 1D translational invariant stabilizer codes. A …

Variational Monte Carlo with neural network quantum states has proven to be a promising
avenue for evaluating the ground-state energy of spin Hamiltonians. However, despite …