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Seminar | Mathematics and Computer Science Division

Unsupervised Machine Learning on the Rigetti Quantum Computer

MCS Seminar

Abstract: Recent years have seen stunning progress in the control of quantum systems and the scalable manufacturing of superconducting quantum hardware. Along with this progress came a 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.

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.