45 citations · 68 across the 9 of their papers we have counts for
9 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…
Soft-Quantum Algorithms
Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy +1
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parame…
Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
Mateusz Papierz, Asel Sagingalieva, Alix Benoit +3
Data-driven surrogates can replace expensive multiphysics solvers for parametric PDEs, yet building compact, accurate neural operators for three-dimensional problems remains challe…
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy +1
Efficient data loading remains a bottleneck for near-term quantum machine learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-…
Superposed parameterised quantum circuits
Viktoria Patapovich, Maniraman Periyasamy, Mo Kordzanganeh +1
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across…
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…
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…
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…