A research-oriented Python package that implements a variational quantum algorithm for solving nonlinear PDEs using a forward Euler time-stepping scheme as proposed in Lubasch et al., “Variational ...
Calculation: A representation of a network of electromagnetic waveguides (left) being used to solve Dirichlet boundary value problems. The coloured diagrams at right represent the normalized ...
This project implements a numerical solver for quantum partial differential equations (PDEs) using tensor network methods. The key theoretical components are: Tensor networks provide an efficient ...
Recently, neural networks have been extensively employed to solve partial differential equations (PDEs) in physical system modeling. While major studies focus on learning system evolution on ...
TEL AVIV, Israel, Sept. 16, 2025 (GLOBE NEWSWIRE) -- LightSolver, inventors of a new laser-based computing paradigm, today announced it has achieved a major technological breakthrough: the ability to ...
Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical ...
Abstract: In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existing methods ...
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