• What is a Good Quantum Encoding? • Part 1 Over the past couple of years, I’ve been learning a little about the world of quantum machine learning (QML) and the sorts of things people are thinking about there. • I recently gave an high-level talk on some of these ideas in connection to a December 2024 preprint called “Towards Structure-Preserving Quantum Encodings”, coauthored with collaborators at Deloitte (Andrew VlasicandAnh Pham) and MIT (Arthur Parzygnat). • I spoke on this at theAWM Research Symposiumthis past May and have decided to write it up in a series of blog posts, as well. • In short, our preprint translates one aspect of an open problem in QML into the language of category theory, in hopes that by casting the problem in a more mathematically-formal light, we might be led to new tools and techniques that could help. • I’ll explain that in this series of articles.
Article Summaries:
- The author announces a new series of blog posts on “What is a Good Quantum Encoding?” following a December 2024 preprint titled Towards Structure‑Preserving Quantum Encodings, co‑authored with colleagues from Deloitte and MIT. The preprint reframes an open problem in quantum machine learning (QML) using category theory, hoping that this formal perspective will unlock new analytical tools. The author notes that the work is aimed at a QML audience with no background in category theory and that its practical impact remains to be seen. The series also serves to disseminate the author’s broader interests in QML and to gauge community reception.
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