Learning feynman diagrams with tensor trains
Y Núñez Fernández, M Jeannin, PT Dumitrescu… - Physical Review X, 2022 - APS
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 …
expansions for the quantum many-body problem at very high precision. The approach is …
Quantum state preparation using tensor networks
AA Melnikov, AA Termanova, SV Dolgov… - Quantum Science …, 2023 - iopscience.iop.org
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 …
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …
Efficient quantum circuits for accurate state preparation of smooth, differentiable functions
A Holmes, AY Matsuura - 2020 IEEE International Conference …, 2020 - ieeexplore.ieee.org
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 …
capacity of a quantum machine. A large barrier to designing quantum algorithms for …
Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations
AE Paine, VE Elfving, O Kyriienko - arXiv preprint arXiv:2308.01827, 2023 - arxiv.org
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 …
differential equations (DEs) in a quantum latent space. We suggest a strategy for building …
PROTES: probabilistic optimization with tensor sampling
A Batsheva, A Chertkov… - Advances in Neural …, 2023 - proceedings.neurips.cc
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 …
probabilistic sampling from a probability density function given in the low-parametric tensor …
Probabilistic tensor optimization of quantum circuits for the problem
GV Paradezhenko, AA Pervishko, D Yudin - Physical Review A, 2024 - APS
We propose a technique for optimizing parameterized circuits in variational quantum
algorithms based on the probabilistic tensor sampling optimization. This method allows one …
algorithms based on the probabilistic tensor sampling optimization. This method allows one …
[HTML][HTML] Deep composition of tensor-trains using squared inverse rosenblatt transports
Characterising intractable high-dimensional random variables is one of the fundamental
challenges in stochastic computation. The recent surge of transport maps offers a …
challenges in stochastic computation. The recent surge of transport maps offers a …
Deep importance sampling using tensor trains with application to a priori and a posteriori rare events
We propose a deep importance sampling method that is suitable for estimating rare event
probabilities in high-dimensional problems. We approximate the optimal importance …
probabilities in high-dimensional problems. We approximate the optimal importance …
Protocols for trainable and differentiable quantum generative modelling
O Kyriienko, AE Paine, VE Elfving - arXiv preprint arXiv:2202.08253, 2022 - arxiv.org
We propose an approach for learning probability distributions as differentiable quantum
circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data …
circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data …
Generative modeling via hierarchical tensor sketching
We propose a hierarchical tensor-network approach for approximating high-dimensional
probability density via empirical distribution. This leverages randomized singular value …
probability density via empirical distribution. This leverages randomized singular value …