Introduction to Quantum Data Science
Introduction to Quantum Data Science covering quantum probability, statistics, sensing, information, machine learning, and data science applications to quantum information.
Summary
Lectures are small, with a median of 6 enrolled students in the recorded sections.
What you’ll be able to do
Demonstrate understanding of quantum probability, including axioms, observable, outcome, expectation, and distribution.
Master quantum statistics methods such as likelihood, information inequality, quantum hypothesis test, and quantum tomography (quantum sensing).
Comprehend concept of quantum computation and quantum information, including qubit and its properties, quantum entropy, and quantum cryptography.
Understand essential elements of quantum algorithms and quantum machine learning.
Understand statistics in quantum computational advantage (supremacy) studies.
Source: UW Guide
Grade history
↗No recorded grade history.
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Prerequisites
Course mapSTAT 601, (STAT 609 and 610), (STAT 611 and 612), or (MATH/STAT 709 and 710), and graduate/professional standing
- STAT 601
- take all
- take all
- take all
- graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
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- STAT 601
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- graduate/professional standing
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Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Quantum computation issues, including probability, statistics, sensing, information, machine learning, and applying data science to quantum information science.
difficulty & workload
No workload feedback recorded.
Topics
- Quantum computation, probability, statistics, sensing, information, and machine learning.
- Statistics in quantum computational advantage and supremacy studies.
Skills
- Quantum probability axioms and distributions; quantum statistical methods including likelihood, information inequalities, hypothesis testing, and tomography.
- Core concepts of quantum computation, qubits, quantum entropy, cryptography, algorithms, and quantum machine learning.
Grades
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Catalog & offerings
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Raw records
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"task_version": "17",
"output_id": "d859badfbcb9074edcc1778212f93de5db8e83ae4f8d2389e42ee1f9f9602109",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}