We use tensor network techniques to obtain high-order perturbative diagrammatic
expansions for the quantum many-body problem at very high precision. The approach is …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …

Effective quantum computation relies upon making good use of the exponential information
capacity of a quantum machine. A large barrier to designing quantum algorithms for …

We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional
differential equations (DEs) in a quantum latent space. We suggest a strategy for building …

We developed a new method PROTES for black-box optimization, which is based on the
probabilistic sampling from a probability density function given in the low-parametric tensor …

We propose a technique for optimizing parameterized circuits in variational quantum
algorithms based on the probabilistic tensor sampling optimization. This method allows one …

Characterising intractable high-dimensional random variables is one of the fundamental
challenges in stochastic computation. The recent surge of transport maps offers a …

We propose a deep importance sampling method that is suitable for estimating rare event
probabilities in high-dimensional problems. We approximate the optimal importance …

We propose an approach for learning probability distributions as differentiable quantum
circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data …

We propose a hierarchical tensor-network approach for approximating high-dimensional
probability density via empirical distribution. This leverages randomized singular value …