Quantum Challenge Resources
-
Getting Started
Element - Join the challenge Element community room to connect and form teams
Project Proposals - team leaders should submit their project proposals using the instructions here
Topical Project Introduction Video - …
-
Orientation Modules
Please complete the following orientation assignments in preparation for the hackathon to familiarize the tools and concepts you’ll need. You will need to create a GitHub account to access these resources. In addition to these orientation modules, we also recommend that you familiarize yourself with Markdown syntax if this is new to you.
For those looking for a refresher on Python programming or to implement a simple … example, see the following links:
-
Quantum computing
Introduction
Simulators
Quantum-inspired computing
Quantum computers and simulators as samplers
-
Python
Programming expertise is not required, but at least beginner Python programming experience is recommended for participation in code-focused projects of the hackathon. For those looking for a brief, interactive refresher on Python programming, see the GitHub Classroom assignment from the first section on this page. For those without prior Python experience, we recommend you complete an introductory Python course in preparation for the hackathon. Some resources are listed below:
If you have no prior programming experience, you may wish to start with the Python Beginners Guide for Non-programmers by Python Software Foundation.
-
Quantum Computing Tools/Packages
Use of the tools listed on this page is not a requirement. A diverse set of packages and implementations is encouraged. Likewise, multiple teams using the same package is not a problem, in part because implementations can remain private during the course of the hackathon.(?) If you’d like to see a specific tool listed here, please navigate to the “Improve this page” link at the bottom of the page and open a pull request. See also the Acceleration Consortium’s curated list of optimization tools.
-
Qiskit
…
-
CudaQ
The Ax Platform is a tool developed by Meta’s Adaptive Experimentation team. It is a user-friendly, modular, and actively developed general-purpose Bayesian optimization platform with support for simple and advanced optimization tasks. It is a high-level wrapper to the widely used BoTorch library, also developed by Meta, which is built on PyTorch.
-
Pennylane
Honegumi (pronounced “ho neh goo mee”, also referred to as “honey gummy”), deriving from the Japanese word for skeletal framework, is a package for interactively creating API tutorials with a focus on optimization packages. You use an interactive grid to select Bayesian optimization characteristics specific to your task and watch the corresponding template dynamically appear. “Open in Colab” and “Open in GitHub” links are also dynamically generated for each template. Honegumi pairs particularly well with LLMs to adapt the templates to real-world tasks.
-
Hugging Face Spaces
The pre-packaged benchmark functions will be available on Hugging Face Spaces, which makes it easy to deploy and use our benchmark tasks through a web GUI, or more relevant to the hackathon - programmatically via a straightforward API. For those who are planning to develop their own benchmark as a hackathon project (Topic #2), we recommend hosting the final benchmark through this same interface. Start by watching the Hugging Face Spaces overview below, which shows how you can get one running in just a few minutes.
To make this more concrete, see our implementation of the Branin function being run via the Hugging Face Spaces interface, which can be set up in just a few minutes. To see how to use it programmatically, click on the “Use with API” button at the bottom of the page (button marked in red in the image below).
-
Guidelines
Submission - The quantum challenge workflow for phase 1 submissions, including the project topic and proposal instructions