2 citations · 2 across the 3 of their papers we have counts for
5 papers
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, a…
TQml Simulator: optimized simulation of quantum machine learning
Viacheslav Kuzmin, Basil Kyriacou, Tatjana Protasevich +3
Hardware-efficient circuits employed in Quantum Machine Learning are typically composed of alternating layers of uniformly applied gates. High-speed numerical simulators for such c…
Method for noise-induced regularization in quantum neural networks
Viacheslav Kuzmin, Wilfrid Somogyi, Ekaterina Pankovets +1
In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the gener…
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…