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Course

Intuitive Learning of Quantum Computing

CSE-41343

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.

Course 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 step: After completing this course, consider enrolling in the course LEAN Thinking for Big Data Analytics, or courses in our Selected Topics in Artificial Intelligence certificate, or other Technology-related coursework.

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

Course Information

Online
3.00 units
$775.00

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This course applies to these programs

Technical Aspects of Artificial Intelligence Machine Learning Methods

Course sessions

Closed

Section ID:

183902

Class type:

Online Asynchronous.

This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.

Textbooks:

No textbook required.

Policies:

  • No refunds after: 9/30/2024

Schedule:

No information available at this time.
Closed

Instructor: James Meng

James Meng
James C.S. Meng, Ph.D., MSM is a currently a senior fellow at UC San Diego – Super Computer Center and a visiting scholar at Massachusetts Institute of Technology - Sloan School of Management. Dr. Meng held various positions with the federal government’s senior executive service from June 1998 until June 2015 when he retired as deputy assistance secretary of the Navy, Business Enterprise Solutions. Dr. Meng has a B.S. in mechanical engineering from Taiwan University, an M.S. in engineering physics from UC Berkley, an M.S. in management from the Massachusetts Institute of Technology - Sloan School of Management and a Ph.D. in aeronautical engineering from UC Berkley.

Dr. Meng is an established author with numerous articles published in various journals including the Journal of Fluid Mechanic, Journal of Applied Optics and Journal of Computational Physics. He has published technical reports on laser doppler velocimetry, superconducting electromagnetic thruster (SCEMT ), electric propulsion technology, experimental studies of turbulence reduction, simulations of hydrodynamics wake in oceanic environment, theoretical analysis of internet waves, and hypersonic reentry vehicle dynamics. In addition, Dr. Meng has obtained six patents in SCEMT, elecomagnetohydrodynamic boundary layer control and acoustic remote cavitation.
 
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