Abstract:
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.

References:
- Smith et al., A Practical Quantum Instruction Set Architecture, arXiv:1608.03355
- Otterbach et al., Unsupervised Machine Learning on a Hybrid Quantum Computer, arXiv:1712.0577

Short Bio:
Johannes Otterbach received his Ph.D. in Physics from the University of Kaiserslautern, Germany, in topics related to photonic many body interactions. After his graduation he worked as a PostDoc at Harvard University, followed by positions as Software Engineer at Palantir and Data Scientist at LendUp. At Rigetti he focuses on building application for near-term quantum computers, specifically algorithms for optimization and machine learning.

This seminar will be streamed. See details at https://anlpress.cels.anl.gov/cels-seminars/