ComputingNotes

Probability – Distributions

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Book Probability by Jim Pitman, Page 19 Events and Sets Set Theory & Venn Diagram Gallery Intersection: \(A \cap B\) A B “A AND B” — Elements in both. Union: \(A \cup B\) A B “A OR B” — Elements in either or both. Complement: \(A^c\) A U “NOT A” — Everything outside A. Subset: ... Read More

Paper Highlight – Quantum Inspired classical algorithm for recommendation systems

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E. Tang, “A quantum-inspired classical algorithm for recommendation systems,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Jun. 2019, pp. 217–228. doi: 10.1145/3313276.3316310.\ “Introduction | IBM Quantum Learning,” IBM Quantum Learning, 2021. https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/introduction (accessed Feb. 20, 2026). Paper Summary: Classical Analogue to Quantum Recommendation Systems Author: Ewin Tang Key Finding: The ... Read More

How to use LLMs with Matlab.

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https://github.com/matlab-deep-learning/llms-with-matlab/blob/main/examples/RetrievalAugmentedGenerationUsingOllamaAndMATLAB.md https://blogs.mathworks.com/deep-learning/2024/07/09/local-llms-with-matlab Connet to Ollama:https://github.com/matlab-deep-learning/llms-with-matlab/blob/main/doc/Ollama.md OR matlab terminal >> !ollama pull mistral Views: 29

IBM Course: Introduction to Quantum Machine Learning

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Resources: Introduction to Quantum Machine Learning A Comprehensive Course Overview The Classical-Quantum Intersection Quantum Machine Learning (QML) is a symbiotic cycle where quantum and classical systems push each other’s limits. The current focus remains on applying quantum algorithms to classical datasets to complement existing workflows where classical systems already excel. Feature Mapping & Kernels A ... Read More

Caculus – Exponential Functions

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Resource: The Exponential Function Suppose we have a quantity \(y\), whose rate of change over time is proportional to the amount present. We can describe this relationship using a differential equation: \[ \frac{dy}{dt} = ky \] If we define the initial state where \(y = y_0\) at time \(t = 0\), the solution to this ... Read More

DRL Toolbox Matlab

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“Reinforcement Learning Toolbox,” Mathworks.com, 2026. https://au.mathworks.com/products/reinforcement-learning.html?requestedDomain= (accessed Feb 23, 2026). Implementing Deep Reinforcement Learning Toolbox Defining Observation and Action Spaces The first stage of development involves establishing the operational boundaries. Data specifications must be defined for both observations (system states) and actions (available commands). rlNumericSpec([5 1]); % Define continuous state space rlFiniteSetSpec(1:4); % Define discrete action ... Read More

Deep Reinforcement Learning

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Resources: 🛒 The Supermarket Analogy [Plaat et al. Page 25] Imagine you have just moved to a new city, you are hungry, and you want to buy some groceries. There is an unrealistic catch: you have no map and no smartphone. After some random exploration, you find a supermarket. You carefully note the route in ... Read More

Time Complexity – Computer Science

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Complexity Hierarchy Logarithmic: $O(\log n)$ The “Sublinear” breakthrough. Allows processing of massive matrices by only sampling specific parts. Linear: $O(n)$ The “Old” Standard. Required reading the entire input, which is impossible for modern recommendation scales. Polynomial: $O(poly(k))$ The “Classical Analogue.” While slower than quantum, it remains fast enough to be practical. Exponential: $O(2^n)$ The “Quantum ... Read More

Systems of Linear Equations and Matrices

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References: Linear Equation: At its simplest, a linear equation is an algebraic equation that creates a straight line when plotted on a graph. Every variable in the equation is raised to the first power (meaning no exponents like $x^2$ or $y^3$), and there are no variables multiplied by each other. The Standard Forms Depending on ... Read More
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