Unsupervised Machine Learning on the Rigetti Quantum Computer

Event Sponsor: 
Quantum Computing Tutorial Series
Start Date: 
May 9 2018 - 12:00pm
WebEx from ERC 161 Eckhardt Research Center
Johannes Jotterbach
Speaker(s) Title: 
Rigetti Computing

Recent years have seen a stunning progress in the control of quantum systems and the scalable manufacturing of super-conducting quantum hardware. Along with this progress came a focus shift in the study of quantum algorithms giving rise to new hybrid quantum/classical algorithms that can be run on near-term quantum devices without immediate need for fault tolerance. These algorithms focus on short-depth parameterized circuits and use quantum computation as a subroutine in a larger classical optimization loop. At Rigetti, we build a computing platform targeting such applications via a flexible cloud API. This talk gives a gentle introduction to the physics behind gate-based quantum computation. I introduce Quil, the Quantum Instruction Language, as a programming language abstraction akin to quantum assembler dialects, to enable these computations via the Forest cloud API. Finally, I show how the full computing stack can be used to run a hybrid quantum/classical algorithm for unsupervised machine learning on a 19-qubit processor.

Miscellaneous Information: 

The fourth tutorial in the Quantum Computing Tutorial Series will be held on Wednesday, May 9, 2018, via webex Join WebEx meeting from ERC 161 Eckhardt Research Center.
Please register with India Gordon (igordon@anl.gov).  A link to a Box folder containing slide decks, presentation materials and video recordings will be distributed to all registrants after the session.