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Quantum Machine Learning

Quantum Computing, Math, and Physics (QCaMP): Introducing quantum computing in high schools

arXiv

Authors: Megan Ivory, Alisa Bettale, Rachel Boren, Ashlyn D. Burch, Jake Douglass, Lisa Hackett, Boris Kiefer, Alina Kononov, Maryanne Long, Mekena Metcalf, Tzula B. Propp, Mohan Sarovar

Year

2023

Paper ID

54320

Status

Preprint

Abstract Read

~2 min

Abstract Words

125

Citations

N/A

Abstract

The nascent but rapidly growing field of Quantum Information Science and Technology has led to an increased demand for skilled quantum workers and an opportunity to build a diverse workforce at the outset. In order to meet this demand and encourage women and underrepresented minorities in STEM to consider a career in QIST, we have developed a curriculum for introducing quantum computing to teachers and students at the high school level with no prerequisites. In 2022, this curriculum was delivered over the course of two one-week summer camps, one targeting teachers and another targeting students. Here, we present an overview of the objectives, curriculum, and activities, as well as results from the formal evaluation of both camps and the outlook for expanding QCaMP in future years.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
The nascent but rapidly growing field of Quantum Information Science and Technology has led to an increased demand for skilled quantum workers and an opportunity to build a...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2309.16788 [2] arXiv https://arxiv.org/abs/2309.16788 [3] Publisher https://arxiv.org/abs/2309.16788

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