Participants will learn the basics of how to formulate and solve quantum chemistry, combinatorial optimization, and quantum machine learning problems using Qiskit. The presentations will assume little to no prior knowledge of quantum computation. In particular, the examples will be provided of how problems can be solved using the VQE (Variational Quantum Eigensover), QAOA (Quantum Approximate Optimization Algorithm), and QML (Quantum Machine Learning) algorithms. The tutorial will be interactive and will use Jupyter notebooks. We will provide step-by-step instructions on how to solve the problems from the ground up with as little hidden complexity as possible. These easily customizable code snippets will allow attendees to skip straight to adapting it to their problems and apply it to their own research.

The recommended way to run the hands-on Jupyter notebooks is using the IBM Quantum Experience web interface. Please create an account here before the tutorial (you might have to create an IBM ID if you do not have one): https://​quan​tum​-com​put​ing​.ibm​.com/

Mobile notebooks will be available from github in advance of the workshop. Download hands-on notebooks from https://​github​.com/​y​u​r​i​a​l​e​x​e​e​v​/​A​r​g​o​n​n​e​-​Q​u​a​n​t​u​m​-​C​o​m​p​u​t​i​n​g​-​T​u​t​o​r​i​a​l​-2022.

• There is no fee to attend the tutorial. However, space is limited, and registration is required.
• Registration is limited to only students, postdocs, and staff members from the National Quantum Center Q-NEXT, Chicago Quantum Exchange, JPMorgan, and Menten AI organizations.

To REGISTER, click on the REGISTER button at the right of the page and enter passcode: quantumcomputing2022.