45 citations · 75 across the 9 of their papers we have counts for
8 papers
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials
G. Laskaris, D. Morozov, D. Tarpanov +6
Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there…
Tensor networks for quantum computing
Aleksandr Berezutskii, Minzhao Liu, Atithi Acharya +25
In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications…
Tensor Quantum Programming
A. Termanova, Ar. Melnikov, E. Mamenchikov +6
Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications…
TQCompressor: improving tensor decomposition methods in neural networks via permutations
V. Abronin, A. Naumov, D. Mazur +7
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage…
Comparison between Tensor Networks and Variational Quantum Classifier
Georgios Laskaris, Artem A. Melnikov, Michael R. Perelshtein +3
The primary objective of this paper is to conduct a comparative analysis between two Machine Learning approaches: Tensor Networks (TN) and Variational Quantum Classifiers (VQC). Wh…
NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization
M. R. Perelshtein, A. I. Pakhomchik, Ar. A. Melnikov +7
Quantum algorithms are getting extremely popular due to their potential to significantly outperform classical algorithms. Yet, applying quantum algorithms to optimization problems…
Tetra-AML: Automatic Machine Learning via Tensor Networks
A. Naumov, Ar. Melnikov, V. Abronin +5
Neural networks have revolutionized many aspects of society but in the era of huge models with billions of parameters, optimizing and deploying them for commercial applications can…
Hybrid quantum ResNet for car classification and its hyperparameter optimization
Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin +6
Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of milli…