19 citations · 37 across the 9 of their papers we have counts for
9 papers
Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai
Margarita Veshchezerova, Egor Barashov, Evgenii Gusev +4
Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present a…
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…
Qubit-efficient quantum local search for combinatorial optimization
M. Podobrii, V. Kuzmin, V. Voloshinov +2
An essential component of many sophisticated metaheuristics for solving combinatorial optimization problems is some variation of a local search routine that iteratively searches fo…
Tensor networks based quantum optimization algorithm
V. Akshay, Ar. Melnikov, A. Termanova +1
In optimization, one of the well-known classical algorithms is power iterations. Simply stated, the algorithm recovers the dominant eigenvector of some diagonalizable matrix. Since…
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…
A Hybrid Quantum-Classical Approach to the Electric Mobility Problem
Margarita Veshchezerova, Mikhail Somov, David Bertsche +4
We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. The original formulation is a Mixed Integer Linear Program wit…
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…