Getting Started

Element - Join the Element community space to connect and form teams.

Project Proposals - team leaders should submit their project proposals using the instructions here.

Orientation Modules

Please familiarize yourself with the tools and concepts which you will need to deliver a project for this challenge. You will need to create a GitHub account to submit your project until the deadline. In addition to git, we also recommend that you familiarize yourself with Markdown syntax if this is new to you.

Quantum Computing

Introduction

• The prospects of quantum computing in computational molecular biology
• Unitary Foundation 2024 Quantum Open Source Survey

Quantum Computing Packages and Simulators

• Qiskit
• pennylane
• Cirq
• cuQuantum
• The Walrus
• Strawberry Fields
• IQM
• pytket
• qrisp
• amazon-braket-sdk

Quantum-inspired Computing

• Simulated Annealing
• Simulated Annealing via GPU-pSAv
• Quantum Annealing via QuantRS2
• Quantum Annealing Enhanced Markov-Chain Monte Carlo

Sampling

• Quantum sampling problems, BosonSampling and quantum supremacy
• Sampling Problems on a Quantum Computer
• Quantum Circuits for the Metropolis-Hastings Algorithm
• Quantum Metropolis Sampling via Weak Measurement

Quantum Computing for Clinical Research

• Quantum computing for clinical research
• How can quantum computing be applied in clinical trial design and optimization?
• Quantum Clinical Trial Optimization Challenge Winner Presentations – Presented by Ingenii & Aqora

Python

Programming expertise is not required, but at least beginner Python programming experience is recommended for participation in code-focused projects of the challenge. 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 challenge. Some resources are listed below:

• Python for Everybody by University of Michigan
• Learn Python 3 by CodeAcademy
• Intro to Python Programming for Materials Engineers
• Python Beginners Guide for Programmers by Python Software Foundation
• The Python Workshop by Packt
• Wrapping R libraries with Rpy
• Calling R from Python with rpy2
• Documentation for rpy2

If you have no prior programming experience, you may wish to start with the Python Beginners Guide for Non-programmers by Python Software Foundation.

Video Courses

• What is Python?
• Python for Beginners
• Python Tutorial for Beginners

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 challenge and since solutions will still vary considerably. If you would 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.

QuTip

• Monte Carlo Solver
• Generating Random Quantum States & Operators
• QuantumToolbox.jl

OpenACC

• OpenACC
• OpenACC and Cuda for Beginners
• OpenACC: More Science Less Programming

quimb

• quimb
• Introducing matrix product states for quantum practitioners
• Simulating quantum circuits with tensor networks
• Qiskit Quimb Simulator
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

Pk/Pd Tools and Packages

• nlmixr: an R package for population PKPD modeling
• ggPMX R package
• Metrum Research Group
• nlmixr2: an R package for population PKPD modeling