19 citations · 22 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…