ComputingNotes

What is Gradient? Loss Function? Model Parameters? Gradient Descent?

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Gradient Descent: Optimization algorithm; minimizes errors between predicted and actual results; updates parameters by moving against the gradient Loss Function: aim is to minimize this function, closer to zero; measures how bad the prediction is in comparison to the actual true value; various methods are used; one is Mean Squared Error (MSE) Gradient: Slope; direction ... Read More

Terms in Quantum Computing

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Quantum Circuit: Observables: In quantum physics, physical properties/quantity that can be measured. eg. system’s energy, alignment of spins (for a system of spins)http://docs.quantum.ibm.com/guides/specify-observables-paulihttps://en.wikipedia.org/wiki/Observablehttps://www.quantiki.org/wiki/observables-and-measurementshttps://jonathan-hui.medium.com/qc-observable-8a44d10c3f7a EigenValues and EigenVectors: https://www.youtube.com/watch?v=PFDu9oVAE-g&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_abLinear Algebra – https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Quantum State: Expectation Values: Expected value of the meaurement or result of an experiment; average of all possible results. https://www.youtube.com/watch?v=8VgBPEcZ_X0https://phys.libretexts.org/Bookshelves/Quantum_Mechanics/Essential_Graduate_Physics_-Quantum_Mechanics(Likharev)/04%3A_Bra-ket_Formalism/4.05%3A_Observables-_Expectation_Values_and_Uncertainties Measuring expectation Values: Unitary Gate: ... Read More

Quantum Circuit Learning

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Quantum Circuit Learning [1]: Hybrid Framework for machine learning with low-depth quantum circuit. QCL takes input data, iteratively tunes circuit parameters and gives desired output by utilizing gradient-based optimization of parameters similar to backpropagation method. Unitary Gate: Reversible transformations on quantum states; building blocks of quantum circuit; [1] K. Mitarai, M. Negoro, M. Kitagawa, and ... Read More

Quantum Key Distribution

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Quantum Key Distribution (QKD) and Quantum Cryptography (QC) Simulation:https://lab.quantumflytrap.com/lab/bb84?mode=waves https://en.wikipedia.org/wiki/BB84 Code:https://github.com/qiskit-community/qiskit-community-tutorials/blob/master/awards/teach_me_qiskit_2018/quantum_cryptography_qkd/Quantum_Cryptography2.ipynb What is Quantum Key Distribution? https://medium.com/quantum-untangled/quantum-key-distribution-and-bb84-protocol-6f03cc6263c5 https://crypto.stackexchange.com/questions/59066/understanding-quantum-key-distribution [1] S.-K. Liao et al., “Satellite-to-ground quantum key distribution,” Nature, vol. 549, no. 7670, pp. 43–47, Sep. 2017, doi: 10.1038/nature23655.

Quantum Cryptography

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Quantum Cryptography: How quantum cryptography works? https://www.nist.gov/cybersecurity/what-quantum-cryptography https://www.nsa.gov/Cybersecurity/Quantum-Key-Distribution-QKD-and-Quantum-Cryptography-QC Resources:

Gradient Descent

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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

Privacy in ML

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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

Differential Privacy

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Heads and Tails Analogue Laplace Distribution Apple, Google using DP. https://privacytools.seas.harvard.edu/differential-privacy https://www.labelia.org/en/blog/the-nuts-and-bolts-of-differential-privacy-part1https://towardsdatascience.com/a-differential-privacy-example-for-beginners-ef3c23f69401/ References:

Latex Tips

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$pdflatex = “latexdiff –graphics-markup=0 backup.tex main.tex > main-d.tex; pdflatex %O main-d”
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