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Showing posts from July, 2012

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Quantum Mechanics and Quantum Computation

About the Course Quantum computation is a remarkable subject, and is based on one of the great computational discoveries that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course will introduce qubits (or quantum bits) and quantum gates, the basic building blocks of quantum computers. It will cover the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, and Shor's iconic quantum algorithm for factoring integers efficiently. The course will also explore the prospects for quantum algorithms for NP-complete problems and basic quantum cryptography.

The course will not assume any prior background in quantum mechanics. Instead, it will use the language of qubits and quantum gates to introduce …

About the Course Quantum computation is a remarkable subject, and is based on one of the great computational discoveries that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course will introduce qubits (or quantum bits) and quantum gates, the basic building blocks of quantum computers. It will cover the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, and Shor's iconic quantum algorithm for factoring integers efficiently. The course will also explore the prospects for quantum algorithms for NP-complete problems and basic quantum cryptography.

The course will not assume any prior background in quantum mechanics. Instead, it will use the language of qubits and quantum gates to introduce …

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I've created a GitHub repository for some fun simulations and other code to illustrate ideas and applications. I believe it's helpful for many people to use simulations to better understand what's going on when learning statistics. Why are things done the way they are? Well, let's simulate the random process and find out!

These are currently in Python but I'll be adding R versions. I'll be adding simulations that illustrate particular ideas in probability and statistics, or that are just fun. Some of the most interesting and useful, I believe, will be related to hypothesis testing.

The initial repository has a basic Monty Hall simulation and two roulette simulations that sample from either a uniform or exponential distribution. I tend to use NumPy quite a bit.

sim-udacity on GitHub

These are currently in Python but I'll be adding R versions. I'll be adding simulations that illustrate particular ideas in probability and statistics, or that are just fun. Some of the most interesting and useful, I believe, will be related to hypothesis testing.

The initial repository has a basic Monty Hall simulation and two roulette simulations that sample from either a uniform or exponential distribution. I tend to use NumPy quite a bit.

sim-udacity on GitHub

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