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This is course material for a course on linear algebra and mathematical prerequisites for quantum computing. It contains Jupyter notebooks and Python scripts that can be downloaded as part of the course.
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This is course material for a course on linear algebra and mathematical prerequisites for quantum computing. It contains Jupyter notebooks that can be downloaded as part of the course or opened in Binder as an online interactive notebook.
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Linear algebra, the language of matrices and vectors, is the fundamental language of quantum computing and quantum information. Approaching quantum computing through linear algebra is the approach taken in the most cited textbook on the subject: "Quantum Computation and Quantum Information" by Nielsen & Chuang. This approach is also mathematically the most approachable for the complete beginner. If you have very little background in mathematics, physics, and computer programming, this approach is for you!
Quantum computing is a way of processing information in a fundamentally different way from the way your laptop or smartphone processes data. The "classical" way of processing information is in terms of bits, binary values of zeros and ones. Quantum computing takes advantage of three different properties of quantum physics to process information.
Superposition is the fancy word used to describe how qubits, the quantum version of bits, can exist in a combination of both zero and one. Qubits can also be measured in different bases, allowing for things like adaptive measurement of states, and measurement based quantum computation. Since qubits can be in a state that is a mixture of both zero and one, this gives them computational properties that classical bits don't have.
Entanglement is the word used to describe how qubits can have states that are correlated with other qubit states. Qubits interact with each other and become entangled, meaning their states are now statistically correlated and information about one can sometimes reveal information about the other. Multiple qubits can be entangled at once and complicated behavior can emerge. One area of research that scientists have shown much interest in is multipartite entanglement. This is a complex form of entanglement that exhibits highly non-classical behavior. This can be used in quantum teleportation and quantum cryptography protocols. It is also the source of a famous experiment named after Einstein, Podolsky, and Rosen, as well as John Bell's famous experiments showing that local hidden variables theories are highly unlikely to be true.
Interference, like the interference we see in waves of light, sound, and water, is exhibited by quantum computers. Quantum computers can have constructive (additive) interference, or they can have destructive interference, where waves are out of sync and cancel. This interesteing and strange behavior is often the subject of debate when discussing wave-particle duality and the famous measurement problem of quantum physics. Interference can be used to enhance "good information" and to cancel "bad information" in computations. This allows us to set up computations in clever ways and use interference as an information processing technique to solve problems.
ASICs or Application Specific Integrated Circuits are specialized computer chips that are built for very specific kinds of computational tasks. A good example of this the GPU or Graphics Processing Unit, or Google's TPU (Tensor Processing Unit) which have enhanced machine learning drastically in recent years. Other examples might be the neuromorphic computing chips that Intel has designed. These were all designed to solve specific kinds of problems, such as enhancing machine learning tasks. Quantum computers can be thought of in a very similar way. There is a classical computer interface that delegates certain computational tasks to the quantum hardware. The quantum hardware then processes that computational task and then the classical computer receives the output data. There are many kinds of problems that quantum computers can solve. In fact, any computational task a classical computer can solve, a quantum computer can also solve. However, classical computers are still more efficient at certain things, so they will not be going anywhere. There are many algorithms and problems that can be solved exponentially faster on a quantum computer compared to all known classical methods.
Finding out where this computational advantage comes from is one of the central tasks of quantum computing research. There is a Quantum Algorithm Zoo, which lists many of the known algorithms that give speedup over classical methods. Much of the focus of The Singularity is to research these various applications and algorithms and implement them in various quantum computing languages. Some applications include using Shor's algorithm to break RSA public key cryptography and other algorithms that break elliptic curve cryptography. If you are interested in discovering the inner workings of quantum algorithms and trying to understand where this quantum supremacy or advantage comes from, check out these lectures.
If you are a Donald Knuth fan and love LaTeX too, check out the TikZ library quantikz. It is a LaTeX library for constructing quantum circuit diagrams. It was used for rendering many of the quantum circuit diagrams in the following lectures.
Below are lectures using Jupyter notebooks that fully develop all of the basic math and programming that is needed to start solving quantum computing problems. They start out with basic linear algebra, and explain the notion of quantum gates (similar to classical logical gates in classical computers). Click on the "launch binder" button to open the interactive version.
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AndrewChau /
As a believer of learning through examples, I have decided to put my own examples of Gremlin queries inside Jupyter Notebooks for people to actually try out. The course is roughly based on this book (http://kelvinlawrence.net/book/Gremlin-Graph-Guide.pdf) by krlawrence but adapted into Python for execution inside a Jupyter Notebook.
albertofernandezvillan /
This repository contains both a collection of Jupyter Notebooks as well as other resources (e.g. presentations, links, ...) that are going to be used during the "Second quarter university extension courses" that the University of Oviedo is going to teach (online).
chmartin /
This is a collection of Jupyter Notebooks for the Udemy Python for DS & ML Bootcamp
amirotd /
This repository is related to a university course named "Design and Synthesis of Analog Filters" and i've tried to Implement methods and algorithms of this course in jupyter notebook environment.
jlira5418 /
## Step 1 - Scraping Complete your initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter. * Create a Jupyter Notebook file called `mission_to_mars.ipynb` and use this to complete all of your scraping and analysis tasks. The following outlines what you need to scrape. ### NASA Mars News * Scrape the [Mars News Site](https://redplanetscience.com/) and collect the latest News Title and Paragraph Text. Assign the text to variables that you can reference later. ```python # Example: news_title = "NASA's Next Mars Mission to Investigate Interior of Red Planet" news_p = "Preparation of NASA's next spacecraft to Mars, InSight, has ramped up this summer, on course for launch next May from Vandenberg Air Force Base in central California -- the first interplanetary launch in history from America's West Coast." ``` ### JPL Mars Space Images - Featured Image * Visit the url for the Featured Space Image site [here](https://spaceimages-mars.com). * Use splinter to navigate the site and find the image url for the current Featured Mars Image and assign the url string to a variable called `featured_image_url`. * Make sure to find the image url to the full size `.jpg` image. * Make sure to save a complete url string for this image. ```python # Example: featured_image_url = 'https://spaceimages-mars.com/image/featured/mars2.jpg' ``` ### Mars Facts * Visit the Mars Facts webpage [here](https://galaxyfacts-mars.com) and use Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc. * Use Pandas to convert the data to a HTML table string. ### Mars Hemispheres * Visit the astrogeology site [here](https://marshemispheres.com/) to obtain high resolution images for each of Mar's hemispheres. * You will need to click each of the links to the hemispheres in order to find the image url to the full resolution image. * Save both the image url string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name. Use a Python dictionary to store the data using the keys `img_url` and `title`. * Append the dictionary with the image url string and the hemisphere title to a list. This list will contain one dictionary for each hemisphere. ```python # Example: hemisphere_image_urls = [ {"title": "Valles Marineris Hemisphere", "img_url": "..."}, {"title": "Cerberus Hemisphere", "img_url": "..."}, {"title": "Schiaparelli Hemisphere", "img_url": "..."}, {"title": "Syrtis Major Hemisphere", "img_url": "..."}, ] ``` - - - ## Step 2 - MongoDB and Flask Application Use MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above. * Start by converting your Jupyter notebook into a Python script called `scrape_mars.py` with a function called `scrape` that will execute all of your scraping code from above and return one Python dictionary containing all of the scraped data. * Next, create a route called `/scrape` that will import your `scrape_mars.py` script and call your `scrape` function. * Store the return value in Mongo as a Python dictionary. * Create a root route `/` that will query your Mongo database and pass the mars data into an HTML template to display the data. * Create a template HTML file called `index.html` that will take the mars data dictionary and display all of the data in the appropriate HTML elements. Use the following as a guide for what the final product should look like, but feel free to create your own design.  - - - ## Step 3 - Submission To submit your work to BootCampSpot, create a new GitHub repository and upload the following: 1. The Jupyter Notebook containing the scraping code used. 2. Screenshots of your final application. 3. Submit the link to your new repository to BootCampSpot. 4. Ensure your repository has regular commits and a thorough README.md file ## Hints * Use Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data. * Use Pymongo for CRUD applications for your database. For this homework, you can simply overwrite the existing document each time the `/scrape` url is visited and new data is obtained. * Use Bootstrap to structure your HTML template.
Fissayo /
This is my capstone project from the IBM Data Analyst course. In each analytics process, the data is stored in the Jupyter notebooks that are uploaded. Week 1 in the project is data collection; 2 is data wrangling, 3 is exploratory data analysis, 4 is Data visualisation, 5 is Building a dashboard and the last week is Presentation of findings.