45 citations · 189 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Microwave photon detection at parametric criticality
Kirill Petrovnin, Jiaming Wang, Michael Perelshtein +2
The detection of microwave fields at single-photon power levels is a much sought-after technology, with practical applications in nanoelectronics and quantum information science. H…
Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method
Egor Kornev, Sergey Dolgov, Karan Pinto +3
The solution of computational fluid dynamics problems is one of the most computationally hard tasks, especially in the case of complex geometries and turbulent flow regimes. We pro…
Optimization of chemical mixers design via tensor trains and quantum computing
Nikita Belokonev, Artem Melnikov, Maninadh Podapaka +3
Chemical component design is a computationally challenging procedure that often entails iterative numerical modeling and authentic experimental testing. We demonstrate a novel opti…
Protein-protein docking using a tensor train black-box optimization method
Dmitry Morozov, Artem Melnikov, Vishal Shete +1
Black-box optimization methods play an important role in many fields of computational simulation. In particular, such methods are often used in the design and modelling of biologic…