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Intuitive Learning of Quantum Computing

 

This course provides the end-to-end description from quantum phenomena to quantum computing hardware, software, and algorithms to hands-on use of IBM Q systems. This course is specifically designed to educate business users of quantum computers without requirements of advanced physics and mathematics for quantum mechanics. The course will compose the following four parts:

I: Why we need to engage in quantum computing education? How can we effectively engage quantum computing?

II: Intuitive & graphic explanation of quantum phenomena, including wave-particle duality, superposition, measurement, and entanglement.

III Intuitive & graphic explanation of quantum computing. (IBM Qiskit interactive hands-on practices)

IV: Quantum computing business applications, and examples of quantum computing results.

Learning Outcomes:

By the end of this course, students will be able to:

  • Recognize basic quantum phenomena that enable the functions of QC algorithms.
  • Formulate and implement QC gates using IBM Qiskit software.
  • Relate business insights for what QC capabilities could support decision-makers timely and actionable that your organization never had before.
  • Gain insights to determine what and how to build QC workforce with right QC tools for the purpose and unique requirements of your organization.

Course typically offered: Online (asynchronous) during our Spring and Fall academic quarters.

Software: This course will utilize IBM Qiskit, an open source SDK for working with quantum computers at the level of pulses, circuits and application modules.

Prerequisites: Some basic familiarity with Linear Algebra. A bachelor’s degree or work experience in managing or using computers..

Next steps: Upon completion, consider enrolling in LEAN Thinking for Big Data Analytics and other Technology-related coursework.

Contact: For more information about this course, please contact unex-techdata@ucsd.edu.

Course Number: CSE-41343
Credit: 3.00 unit(s)
Related Certificate Programs: Selected Topics in Artificial Intelligence

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