MSQC604

Quantum Computing Architectures and Algorithms

Quantum computing aims to utilize quantum properties of matter to efficiently solve problems that classical computing systems would take too long to solve. This course reviews modern noisy-intermediate scale quantum (NISQ) quantum computing architectures and algorithms for these platforms. We focus on mapping of optimization and machine learning problems onto NISQ architectures and also discuss how to leverage state-of-the-art classical simulation methods for these quantum-inspired algorithms. We review several NISQ architectures and associated software interfaces, we analyze performance for optimization and statistical sampling. We survey current literature to review and implement methods for mapping optimization and machine learning problems onto NISQ architectures and modern simulators and use them to solve and study example problems.

Spring 2025

Anne Carswell

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Craig Potter

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Daniel Butts

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Daniel Zimmerman

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Diba Alvi

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Ellen Lau

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Gerard Kunkel

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Gregory Miller

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Hee-Jung Song

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Hilary Bierman

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Irina Muresanu

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Barbara Haggh-Huglo

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Colenso Speer

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Daniel Foster

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Amanda Chicoli

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Andrea Brown

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Ann Kennedy

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Colleen Carrion

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David Neely

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Gretchen Horlacher

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Chan Lin

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Allison Durbin

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Ana Navarro Cebrian

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Darren Shillingford

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Hart Guonjian-pettit

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Corinne Hayes

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David Mann

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Gabriella Roselle

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Anna Nelson

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Avia Pasternak

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Past Semesters

Paul Lott

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Include P/F semesters During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.

Average GPA of 3.40 between 11 students*

A-: 9.09%

A: 27.27%

A+: 45.45%

C-: 9.09%

W: 9.09%

* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.