Abstract: Accurately estimating the parameters of a gray-box linear time-invariant state-space representation is still a challenging problem especially if the number of unknowns exceeds ten. The ...

a notebook-first introduction to QSVT and QSP a reusable Python package for polynomial design, spectral transforms, and small PennyLane QSVT checks where the backend can synthesize the transform ...

LinearTransformation(clip clp, string "Input", string "Output", string "Interpolation", string "LUT_Path") string LUT_Path = path to the folder where the LUTs are ( e ...

The seventh graders in Maddie Hines’ project-based math class in North Portland are learning all about how to run sophisticated experiments where they come up with a hypothesis, test it out using the ...

In mathematics, ubiquitous objects called groups display nearly magical powers. Though they’re defined by just a few rules, groups help illuminate an astonishing range of mysteries. They can tell you ...

Linear algebra is essential for understanding core data science concepts like machine learning, neural networks, and data transformations. Different books cater to various needs. Some focus on ...

Just a few years ago, large language models (LLMs) were mostly used by researchers and data scientists in highly technical environments. Today, they’ve become part of the tools many people use every ...

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Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this work, we describe the development of a new algorithm for the computation of ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, and statistics. These provide the foundation for developing the needed ...

Abstract: Linear systems involved in engineering and scientific calculations can be more easily analyzed using similarity transformation. However, understanding the numerous abstract linear algebra ...