QHACK 2019
A hackathon devoted to all things Quantum Machine Learning (QML).
Location:
Xanadu HQ
777 Bay St, Suite 2902
Toronto, Ontario, Canada
Schedule
| Sun Nov 24 | Mon Nov 25 | Tues Nov 25 |
|---|---|---|
| Evening: Welcome social (optional) |
8am Breakfast & Registration |
9am Hackathon reconvenes (lunch provided) |
| 9am Presentations from invited speakers |
||
| 1pm - Lunch - Team formation - Hackathon overview |
5pm - Participant presentations - Judging - Winners announced - Close and final networking |
|
| 2pm Hackathon (dinner provided) |
||
| 8pm Evening social event (offsite) |
Frequently asked questions
Do I need to do anything to prepare?
QHACK invitees span a variety of backgrounds, experiences, and skill levels. Everyone will be bringing unique perspectives and ideas to the event. We will begin the hackathon with a half-day session of exciting talks from big names in the field. These presentations will cover both introductory material and advanced ideas on quantum machine learning.
However, all participants are encouraged to spend some time familiarizing themselves with the basics of quantum computing, quantum machine learning, and the various quantum software platforms which will be used in the hackathon.
Resources:
How do I form a team?
Participants are encouraged to introduce themselves, discuss ideas, and begin to form up teams using the QHACK slack channel and ths GitHub repository. We will also be having an (optional) social meetup the day before the hackathon begins. This is a great opportunity to meet people in person!
Although it is encouraged, it is not required to form a team before the event. During the hackathon itself, we will have a dedicated period devoted to team forming. People will be able to introduce themselves, share what they can bring to a team, and pitch their ideas.
What do I need to bring?
- Your creativity
- Ideas for hackathon projects
- A laptop
What software should I install?
We hope to provide browser-based environments for hacking, but if you want to work locally on your own device, we recommend to install the following packages
-
Python 3.6 or Python 3.7
If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.
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Jupyter notebooks
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Pennylane from Xanadu
pip install pennylane -
Qiskit from IBM
pip install qiskit pennylane-qiskit -
pyQuil from Rigetti
pip install pyquil pennylane-forest. To install the Forest SDK, see the installation details provided by Rigetti. -
Quantum Development Kit from Microsoft
pip install pennylane-qsharp. To install the Microsoft QDK, see the installation details provided by Microsoft.