Page Not Found
Page not found. Your pixels are in another canvas.
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Page not found. Your pixels are in another canvas.
About me
This is a page not in th emain menu
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml
and set future: false
.
Published:
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.
Published:
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.
Published:
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.
Published:
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.
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.
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.
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.
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.