Furthermore, the RF algorithm has the major advantage of being interpretable and is suited for well defined and structured features like the laser processing parameters for the forward mapping of the problem at hand. The RF implementation within the Python ML module scikit-learn 1.2.2. was used...
We propose a quantum inverse iteration algorithm, which can be used to estimate ground state properties of a programmable quantum device. The method relies on the inverse power iteration technique, where the sequential application of the Hamiltonian inve
Also, for GNNs \psi is a mapping from vertices to feature-vectors. Many GNN implementations, including the ones used in experiments here, assume the graph to be undirected and ignore the edge-labelling in \epsilon Full size image 2.1 General working principle of GNNs Let G = (V,E,\sigma...
From the computational cost perspective, we also show a fair comparison using the same number of evaluations of the objective function (see the subfigure in Fig. 8 (right corner)). It is clear to note that the local gradient shows a fast convergence but it quickly traps into a local ...
What challenges are associated with inter-cell interference in cellular-connected Unmanned Aerial Vehicles (UAVs)? How does apprenticeship learning via Inverse Reinforcement Learning align with expert behavior? Keywords Apprenticeship learning Path planning ...
Examples include simulation of molecular hydrogen with the linear optical setup,9 superconducting circuits,10–13 and trapped ions.14 Finally, the variational simulation for larger molecules (LiH and BeH2) were reported recently.11 From the material science perspective, the use of cold atom quantum ...
In this paper, we briefly review the mathematics behind ZI, describe the principal operations and usage, and introduce some central points for the developer perspective. We report the results obtained in EEG and EIT inversion tests performed with real and syntetic data, respectively, and demonstrate...
This issue becomes clear by looking as an example at a linear mapping with 4- dimensional input and output, which can be described by a multiplication of the input with a transformation matrix containing the model weights. The solving of the inverse problem would correspond to inverting the ...
Similarly, we model the reward function as a parametric mapping \(R_{\varvec{\omega }}\), and we enforce the additional constraint of being a linear mapping defined in terms of a feature function \(\varvec{\phi }\). More formally, we define a space of linear reward functions as: $...
Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emerged