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About me

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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Quantum entanglement is an essential resource in quantum information theory. What is the rate of distillation of entanglement from isotropic states using operations that are positive-partial-transpose-preserving? This project presents a detailed proof of a theorem from the paper *A semidefinite program for distillable entanglement* by Rains that shows a lower bound for this rate. The problem is equivalent to the rate of transmission through a quantum depolarizing channel assisted by PPTp codes.

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E91 protocol proposed by Ekert was one of the first practical applications of quantum entanglement. It achieves the task of quantum key distribution. In this short document I present a step by step proof of the protocol.

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In this project I present a detailed proof of a hypercontractive inequality using basic entropic quantities and their properties. The proof comes from the paper *Hypercontractivity Via the Entropy Method* by Blais and Tan.

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What is *Quantum Machine Learning*? We may use classical machine learning to solve problems in quantum physics or we may use quantum algorithms to develop new, fully quantum machine learning techniques. I chose a paper from both categories and summarized them in my project. The first paper is *A Neural Decoder for Topological Codes* by Giacomo Torlai, Roger G. Melko which proposes a stochastic neural network to create decoders for topological quantum error-correction codes. The second paper is *Quantum Perceptron Models* by Nathan Wiebe, Ashish Kapoor, Krysta M Svore which shows how to quantumly represent and train a perceptron.

Download my work __here__.

In classical information theory channel capacities are additive. Is it the case in quantum information theory as well? Can we transmit any information by combining quantum channels which alone have no capacity? This project summarizes a paper *Quantum Communication With Zero-Capacity Channels* by Graeme Smith and Jon Yard that answers these questions.

Download my work __here__.

Short description of portfolio item number 1

Short description of portfolio item number 2

Quantum entanglement is an essential resource in quantum information theory. What is the rate of distillation of entanglement from isotropic states using operations that are positive-partial-transpose-preserving? This project presents a detailed proof of a theorem from the paper *A semidefinite program for distillable entanglement* by Rains that shows a lower bound for this rate. The problem is equivalent to the rate of transmission through a quantum depolarizing channel assisted by PPTp codes.

Download my work __here__.

In this project I present a detailed proof of a hypercontractive inequality using basic entropic quantities and their properties. The proof comes from the paper *Hypercontractivity Via the Entropy Method* by Blais and Tan.

Download my work __here__.

What is *Quantum Machine Learning*? We may use classical machine learning to solve problems in quantum physics or we may use quantum algorithms to develop new, fully quantum machine learning techniques. I chose a paper from both categories and summarized them in my project. The first paper is *A Neural Decoder for Topological Codes* by Giacomo Torlai, Roger G. Melko which proposes a stochastic neural network to create decoders for topological quantum error-correction codes. The second paper is *Quantum Perceptron Models* by Nathan Wiebe, Ashish Kapoor, Krysta M Svore which shows how to quantumly represent and train a perceptron.

Download my work __here__.

In classical information theory channel capacities are additive. Is it the case in quantum information theory as well? Can we transmit any information by combining quantum channels which alone have no capacity? This project summarizes a paper *Quantum Communication With Zero-Capacity Channels* by Graeme Smith and Jon Yard that answers these questions.

Download my work __here__.

** Published:**

QAOA is a quantum algorithm for solving combinatorial optimization problems. Its sucessful execution depends on input parameters that need to be tailored to a specific problem that we are solving. Usually, this choice is challenging for a user and might require a trial and error approach. The problem can be mitigated by using a classical feedback loop that involves a classical optimizer that searches for good input parameters. I will present my implementation of such a hybrid QAOA algorithm in Q# and C# taht uses a gradient-free Cobyla optimizer and I will show how to solve a simple problem using it.

Linear Algebra for Engineers, *University of Waterloo, Department of Combinatorics & Optimization*, 2017

Organizing and proctoring weekly quizzes, grading.

Introduction to Optimization (Non-Specialist Level), *University of Waterloo, Department of Combinatorics & Optimization*, 2018

Holding office hours, grading and proctoring exams.

Portfolio Optimization Models, *University of Waterloo, Department of Combinatorics & Optimization*, 2018

Tutorials, holding office hours, grading and proctoring exams.

Introduction to Optimization, *University of Waterloo, Department of Combinatorics & Optimization*, 2019

Holding office hours, grading and proctoring exams.