8 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…
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-…
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…
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…