INTRODUCTION TO QUANTUM DATA SCIENCE

Prerequisites & course information

Requisites: STAT 601 , ( STAT 609 and 610 ), ( STAT 611 and 612 ), or ( MATH/STAT 709 and 710 ), and graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Demonstrate understanding of quantum probability, including axioms, observable, outcome, expectation, and distribution. Audience: Graduate 2. Master quantum statistics methods such as likelihood, information inequality, quantum hypothesis test, and quantum tomography (quantum sensing). Audience: Graduate 3. Comprehend concept of quantum computation and quantum information, including qubit and its properties, quantum entropy, and quantum cryptography. Audience: Graduate 4. Understand essential elements of quantum algorithms and quantum machine learning. Audience: Graduate 5. Understand statistics in quantum computational advantage (supremacy) studies. Audience: Graduate Learning Outcomes: 1. Demonstrate understanding of quantum probability, including axioms, observable, outcome, expectation, and distribution. Audience: Graduate 2. Master quantum statistics methods such as likelihood, information inequality, quantum hypothesis test, and quantum tomography (quantum sensing). Audience: Graduate 3. Comprehend concept of quantum computation and quantum information, including qubit and its properties, quantum entropy, and quantum cryptography. Audience: Graduate 4. Understand essential elements of quantum algorithms and quantum machine learning. Audience: Graduate 5. Understand statistics in quantum computational advantage (supremacy) studies. Audience: Graduate