5 papers
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…
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…
Forecasting steam mass flow in power plants using the parallel hybrid network
Andrii Kurkin, Jonas Hegemann, Mo Kordzanganeh +1
Efficient and sustainable power generation is a crucial concern in the energy sector. In particular, thermal power plants grapple with accurately predicting steam mass flow, which…
Information plane and compression-gnostic feedback in quantum machine learning
Nathan Haboury, Mo Kordzanganeh, Alexey Melnikov +1
The information plane (Tishby et al. arXiv:physics/0004057, Shwartz-Ziv et al. arXiv:1703.00810) has been proposed as an analytical tool for studying the learning dynamics of neura…