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Resources: The Concept: Quantum Feature Maps In classical machine learning, a “kernel trick” is often used to map data into a higher-dimensional space. Quantum computers provide a natural way to do this: Feature Map The process of encoding a classical input vector \( \vec{x} \) into a quantum state \( |\Phi(\vec{x})\rangle \) acts as a ... Read More

In linear algebra, the dot product (or inner product) is specifically used for vectors. It takes two vectors of the same dimension and results in a single scalar value (a number). 1. Geometric Definition Geometrically, the dot product measures the overlap or projection of one vector onto another. a · b = ||a|| ||b|| cos(θ) ... Read More

Book Calculus by Thomas/Finney, 9th Edition, Page 282 Understanding Initial Value Problems in Calculus When studying differential equations in calculus, we often encounter something called an Initial Value Problem (IVP). While differential equations describe entire families of functions, an initial value problem helps us find one specific solution that fits a given condition. What Is ... Read More

https://en.wikipedia.org/wiki/Graph_(discrete_mathematics) https://algs4.cs.princeton.edu/41graph https://au.mathworks.com/help/matlab/math/directed-and-undirected-graphs.html https://mathinsight.org/definition/undirected_graph https://medium.com/basecs/a-gentle-introduction-to-graph-theory-77969829ead8 https://www.jsums.edu/nmeghanathan/files/2015/05/CSC434-Fall2014-Module-1-Graph-Theory-Basics.pdf

https://www.ibm.com/quantum/blog/iqp-upgrade https://quantum.cloud.ibm.com/docs/en/migration-guides/classic-iqp-to-cloud-iqp New version of IBM Quantum Platformhttps://quantum.cloud.ibm.com/ https://quantum.cloud.ibm.com/docs/en/guides/cloud-setup Major Differences:https://quantum.cloud.ibm.com/docs/en/migration-guides/classic-iqp-to-cloud-iqp#major-differences

Iterative process: Goal of gradient descent: MINIMIZE the cost function (error between predicted and actual value) Requirements: a Learning Rate and the direction where to go. Terms: Types of Gradient Descents: https://www.ibm.com/think/topics/gradient-descent Linear Regression: Gradient Descent (One of the best source)https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descentMath behind gradient descent: https://sebastianraschka.com/faq/docs/gradient-optimization.html https://www.khanacademy.org/math/multivariable-calculus/applications-of-multivariable-derivatives/optimizing-multivariable-functions/a/what-is-gradient-descent https://www.geeksforgeeks.org/gradient-descent-algorithm-and-its-variants https://en.wikipedia.org/wiki/Gradient_descent

What is DP? What is privacy in first place? What do we refer to privacy in ML? What is the main difference between: PCA and PCA DP? Same with SGD vs SGD DP? Why do we need DP? What are the problems in Machine Learning in terms of privacy? How to quantify privacy? Loss of ... Read More