Quantum computing has the promise of outperforming every classical computer to an extraordinary degree for certain tasks. It stands to change the world by solving problems that are intractable with today's technology, hence it will likely disrupt every industry in near future. The goal of this handson session is to provide a technical introduction to quantum computing for software developers and technology architects. We will focus on gate-model quantum computer architecture and Qiskit development kit to solve a simple problem. We will implement the Grover's search algorithm together, and run it on an IBM quantum backend. Through the exercises you will see the nuts and bolts of quantum computing and why it can make a big difference.
These exercise are a collection of python scripts as IPython notebook files. A pre-configured online environment (Jupyter Notebook) is prepared to run the exercises. The link to the online Jupyter Notebook in Binder can be found at the bottom of the page.
If you want to run the exercises on your own computer, you will need a working Python environment with required Python modules are numpy, pandas and qiskit. These can be installed using pip. An easy option to setup a new Python environment with all the tools (including Jupyter Notebook) is to download and install Anaconda (https://www.anaconda.com/download/). You can install Qiskit using Anaconda package manager or using 'pip install qiskit'. Moreover, you may signup to IBM Q Experince (https://quantumexperience.ng.bluemix.net/qx/signup) and receive you API tocken (https://quantumexperience.ng.bluemix.net/qx/account/advanced). The API tocken is necessary to configure Qiskit to connecet to IBM Q backend and run your code on an actual quantum computer.
PowerPoint slides of the workshop are in this file: Quantum_Computing_Handson_2019.pdf
This repository contains Jupyter Notebook files to start expremineting with gate-model quantum computers. The excersises are based on qiskit SDK, and are only tested on IBM Q quantum backends.
Run search_classic.ipynb to review the complexity of an unstructured search problem.
Run hello_qiskit.ipynb to start with Qiskit programming.
The Grover's algorithm brings quadratic speedup to the problem of unstructured search.
Run grover_1.ipynb to experminet with Grover's algorithm. The task is to complete the code by calling two functions black_box_check(circuit, key, q) and reflection_about_average(circuit, q).
Run grover_2.ipynb to experminet with details of Grover's algorithm. The task is to implement the fucntion reflection_about_average(circuit, q).
You can find the executable Jupyter Notebook files from the follwing link: 