108 citations · 539 across the 15 of their papers we have counts for
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quant-ph2023★ 15 cited
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
Léo Monbroussou, Eliott Z. Mamon, Jonas Landman +3
Quantum machine learning (QML) has become a promising area for real world applications of quantum computers, but near-term methods and their scalability are still important researc…
quant-ph2023
Quantum Fourier Networks for Solving Parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1
Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-…