From the course: Quantum Computing Fundamentals

Qiskit and GitHub Codespaces

From the course: Quantum Computing Fundamentals

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Qiskit and GitHub Codespaces

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- Quantum computing is still a fairly young field and there's not a single standard architecture for how quantum computers should be designed and built. There are lots of companies developing quantum computers today, such as D-Wave, Rigetti, IonQ, and many more and they all take slightly different approaches to building their systems. Instead of latching onto a single vendor, we designed this course to teach the foundational concepts of quantum programming that should be applicable across the diverse landscape of quantum computers. - That said, to show you concrete examples of these concepts implemented with actual code we had to select a programming environment to use. We'll be writing our quantum programs throughout this course using QISkit which is an open source software development framework for quantum computing developed by IBM's research division. Its name originally stood for Quantum Information Science Kit. We chose to use QISkit for this course because it's simple to use and includes several visualization tools to help us learn. Coding QISkit is written using the Python programming language as an interface to represent the quantum operations in your program. That Python code then gets translated and optimized into quantum instructions which you can run using a simulator on your own computer or send it to the cloud to run on IBM's real quantum hardware. For this course, we'll write our QISkit programs using Jupyter Notebooks and Visual Studio Code running within the GitHub Codespaces environment. - We recommend that you use Codespaces to follow along so you'll be working from the same environment as us. You can launch your own Codespaces environment by going to the GitHub page for this course, clicking on the green code button and then select create Codespace on main from the dropdown. It may take a minute or so for the Codespace environment to spin up and then you should see a visual studio code window like this. Within the SRC directory on the left, you'll find folders with the code examples shown throughout this course with the beginning and end states of the code at the start and end of each video. We've configured this Codespace to include all of the necessary dependencies, and you can find a list of the Python packages that are installed in the file requirements.txt. - If you find yourself struggling with the Jupyter Notebook interface, we recommend the LinkedIn learning course introducing Jupyter for more information. - Similarly, if you want to learn more about Python code, there are lots of other LinkedIn learning courses on that topic.

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