What is a good quantum encoding?

Abstract: One aim of quantum machine learning is to determine whether quantum computers can provide advantages over traditional machine learning. This exploration depends on how classical data is encoded onto a quantum computer, a choice that can vary significantly from one learning task to another. Recent studies have demonstrated that identifying relevant mathematical structures in the data can yield valuable insights into what constitutes a good encoding choice. In this talk, I will introduce these concepts from a high level and explain how viewing them through a category-theoretic lens can offer a general, yet principled, perspective on designing quantum encodings that preserve mathematical structure.

Speaker bio: Tai-Danae Bradley is a mathematician and creator at SandboxAQ, a startup company focused on AI and quantum technologies, and a visiting research professor of mathematics at The Master’s University. She has a PhD in mathematics from the CUNY Graduate Center and spent time as a postdoctoral researcher at X, The Moonshot Factory (Google X) before joining SandboxAQ. She is the creator of the mathematics blog Math3ma, as well as a coauthor of the graduate-level textbook Topology: A Categorical Approach and a former co-host of the PBS YouTube channel “Infinite Series”. Her research interests include category theory, machine intelligence, and quantum physics.

Moderator: Ted Theodosopoulos Ted is a mathematician who, after working for years in academia and industry, transitioned to teaching at the pre-college level sixteen years ago, the last eight at Nueva, where he teaches math and economics. Ted’s research background is in the area of interacting stochastic systems, with particular applications in biology and economics.

Computer scienceMathematics

Audience: researchers in the topic

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Relatorium seminar

Series comments: The name "Relatorium" combines "relator" with the Latin root "-ium," meaning "a place for activities" (as in "auditorium" or "gymnasium"). This seminar series is a platform to relate ideas, interact with math, and connect with each other.

In this series, we explore math beyond what we usually hear in standard talks. These sessions fall somewhere between a technical talk and a podcast: moderately formal, yet conversational. The philosophy behind the series is that math is best learned by active participation rather than passive listening. Our aim is to “engage and involve,” inviting everyone to think actively with the speaker. The concepts are accessible, exploratory, and intended to spark questions and discussions.

The idea of relatability has strong ties to compassion — creating space for shared understanding and exploration - which is the spirit of this seminar! This is a pilot project, so we’re here to improvise, learn, and evolve as we go!

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