Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms.
TITLE: Empowering Quantum Machine Learning Research with Q#
SPEAKER: Dr Christopher Granade
AFFILIATION: Quantum Systems, Microsoft, Washington, USA
HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information
ABSTRACT:
In this talk, I will demonstrate how the Q# quantum programming language can be used to start exploring quantum machine learning, using a binary classification problem as an example. I will describe recent work in QML algorithms for classification, and show how Q# allows implementing and using this classifier through high-level quantum development features. Finally, I will discuss how these approaches can be used as part of a reproducible research process to share your explorations with others.
RESOURCES:
Microsoft Quantum Documentation: https://docs.microsoft.com/en-us/quantum/
jupyter: https://hub.gke.mybinder.org/user/microsoft-quantum-dkkrr9lj/tree
Quantum Katas and Tutorials as Jupyter Notebooks: https://hub.gke.mybinder.org/user/microsoft-quantumkatas-ze93xewj/notebooks/index.ipynb
Q# Community: https://qsharp.community/
Open Source Release Welcomes Developers to Contribute and Help Solve Planet-Scale Challenges: https://cloudblogs.microsoft.com/quantum/2019/07/11/microsoft-quantum-oss-available-github/
OTHER LINKS:
Microsoft Quantum: https://www.microsoft.com/en-au/quantum/?rtc=1
UTS Centre for Quantum Software and Information: https://www.uts.edu.au/research-and-teaching/our-research/centre-quantum-software-and-information
Chris Ferrie: https://www.uts.edu.au/staff/christopher.ferrie
