In preparation for the challenge, you should make yourself familiar with the foundational content necessary to understand and complete the four exercises and associated questions.
Most quantum computing algorithms require an understanding of linear algebra to formulate the problem. However, you need not be an expert on the topic to complete the IBM Quantum Spring Challenge 2022. Here are the resource that can help you learn or brush-up on the foundations of linear algebra and matrices.
Qiskit, and thus the challenge, is implemented in Python. If you’re not familiar with Python, you can learn how the basics from the Qiskit Textbook chapter on Python and Jupyter Notebooks. Jupyter notebooks are an interactive way to program and are the most common method for communicating Qiskit work. All exercises for the challenge are provided as Jupyter Notebooks on the IBM Quantum platform. To access the platform, you should create an account using the links provided in the section below.
In this tutorial, we introduce the Variational Quantum Eigensolver (VQE), motivate its use, explain the necessary theory, and demonstrate its implementation in finding the ground state energy of molecules.
General Information
Anderson Localization
Many-body localization
Make sure you have registered on the IBM Quantum platform as it hosts the challenge notebooks and exercises. You can register an account here. You will be using the IBM Quantum Lab, which hosts Jupyter Notebooks for you. If you are not familiar with Jupyter or the IBM Quantum Lab, you can read the Quantum Lab Guide for more information on how it is structured and what features are available.