3 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Potential of quantum scientific machine learning applied to weather modelling
Ben Jaderberg, Antonio A. Gentile, Atiyo Ghosh +5
In this work we explore how quantum scientific machine learning can be used to tackle the challenge of weather modelling. Using parameterised quantum circuits as machine learning m…
Graph Algorithms with Neutral Atom Quantum Processors
Constantin Dalyac, Lucas Leclerc, Louis Vignoli +9
Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithm…
Geometric quantum machine learning of BQP protocols and latent graph classifiers
Chukwudubem Umeano, Vincent E. Elfving, Oleksandr Kyriienko
Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used f…
Qadence: a differentiable interface for digital-analog programs
Dominik Seitz, Niklas Heim, João P. Moutinho +9
Digital-analog quantum computing (DAQC) is an alternative paradigm for universal quantum computation combining digital single-qubit gates with global analog operations acting on a…
Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations
Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko
We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional differential equations (DEs) in a quantum latent space. We suggest a strategy for building q…
Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Cases
Niraj Kumar, Evan Philip, Vincent E. Elfving
In many computational problems in engineering and science, function or model differentiation is essential, but also integration is needed. An important class of computational probl…