This paper presents the experience of a Quantum Computer course addressed for the first time for undergraduate engineering students. Students’ levels varied from year two to five of their careers and had previous knowledge on Calculus, Linear Algebra and Programming, but not on Quantum Mechanics or on Modern Physics. The main objective of the course was that students acquire programming skills in Quantum Computing, so it had a practical approach. Instead of beginning by introducing the physical phenomenology under the field, the course started directly with presenting the logic of quantum computing from an abstract point of view. The language used was Q#. The curricula was based on the one available for the Microsoft Quantum Network.
This paper presents the experience of a Quantum Computer course addressed for the first time for undergraduate engineering students. Students’ levels varied from year two to five of their careers and had previous knowledge on Calculus, Linear Algebra and Programming, but not on Quantum Mechanics or on Modern Physics. The main objective of the course was that students acquire programming skills in Quantum Computing, so it had a practical approach. Instead of beginning by introducing the physical phenomenology under the field, the course started directly with presenting the logic of quantum computing from an abstract point of view. The language used was Q#. The curricula was based on the one available for the Microsoft Quantum Network.
There is a growing need for scientists and engineers with Quantum Information Science and Technology (QIST) skills.To address this need for 'quantum aware' graduates we describe a two-course sequence in quantum computing suitable for undergraduate students studying electrical engineering, physics, and computer science.Our approach is unique in that it aims to take full advantage of the diverse backgrounds of second and third year undergraduate students from these three STEM disciplines.Students will work in interdisciplinary teams to (1) understand the physics and the computational theory relevant to quantum computing, (2) develop computer code that simulates a quantum computer, (3) understand the relevance and importance of existing quantum computing algorithms, and (4) appreciate the need for future research in quantum computing.Our approach is informed in part by our experience working with a pair of undergraduate students this past summer on the development of a Python-based quantum computer simulator.This experience showed that building a simulator was an effective way to teach the theory underlying quantum computing.Building a simulator also provides an excellent foundation upon which to explore student-accessible research projects in quantum computing, which is a high-impact educational practice.
Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not efficiently possible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing, in particular in quantum programming. However, there are few offerings for non-specialists and little information on best practices for training computer science and engineering students. In this report we describe our experience teaching an undergraduate course on quantum computing using a practical, software-driven approach. We centered our course around teaching quantum algorithms through hands-on programming, reducing the significance of traditional written assignments and relying instead on self-paced programming exercises ("Quantum Katas''), a variety of programming assignments, and a final project. We observed that the programming sections of the course helped students internalize theoretical material presented during the lectures. In the survey results, students indicated that the programming exercises and the final project contributed the most to their learning process. We describe the motivation for centering the course around quantum programming, discuss major artifacts used in this course, and present our lessons learned and best practices for a future improved course offering. We hope that our experience will help guide instructors who want to adopt a practical approach to teaching quantum computing and will enable more undergraduate programs to offer quantum programming as an elective.
Investments in quantum technologies, which are based on the effects of quantum mechanics, have escalated in recent years, with big players such as IBM, Google, and Microsoft engaging in the race for quantum supremacy. Some potential benefits of this cutting-edge technology include developing new drugs and materials through accurate molecule simulation, establishing more secure and reliable communication channels, and finding faster solutions for complex optimization problems. As quantum technologies evolve, however, the demand for new software, interfaces, and end-to-end systems to properly program and explore the advantages of quantum mechanics becomes paramount. This requires engineers, especially software engineers, and computer scientists, to have multidisciplinary knowledge and skills. Unfortunately, introductory quantum computing and technologies courses are mostly available in physics, and most engineering and computer science courses do not have modules on quantum physics, leading to a talent gap in the current job market. Therefore, this paper proposes a short teaching plan for introducing quantum computing to students with a more technology-based background, such as computer science and engineering students. This plan uses active learning methodologies, such as Challege-Based Teaching, to better engage these students and contextualize the many applications of this new technology. The initial teaching plan consists of a 3-hour seminar introducing the main quantum mechanics concepts and the technology applications, a 3-hour introduction to the math fundamentals and quantum logic gates, and a 3-hour handson workshop using the IBM Qiskit platform. Two versions of this plan were executed in two different moments: (1) a one-day event open to undergraduate students from different institutions and backgrounds, and (2) a 3-day internal training course for computer science and information technology management undergraduate students from our higher education institution. Data was collected during both events through online forms and interviews to measure the learning outcomes of the produced material and better understand the public interest in the field. The results identified key elements for better teaching quantum computing for computer scientists and engineers, providing guidelines for developing a more extensive and term-long teaching plan for undergraduate technology courses.
As quantum computers are becoming available for experiments, their enormous potential is being recognized among college communities. The goal of introducing quantum computing in college level computer science programs is becoming attractive. However, this goal comes with opportunities and challenges. of the challenges is that quantum computing appears to require substantial background in physics as well as in computer science. However, it is hard to convince students to major in these two fields. Computer Science students with a minor in physics can also be considered. Several alternative curriculum structures are possible. A non-consecutive two-course framework is proposed here. A course on Introduction to Quantum Computing may be offered to sophomore level students. This introductory course may teach fundamental ideas about quantum computing with basic concepts behind BQP (Bounded Error Quantum Polynomial) and related topics. This introductory course may be followed up by some physics courses (on related topics such as quantum mechanics) and some computer science courses. Then, a senior level course on Advanced Quantum Computing may be offered that may include some well-known algorithms (such as Shor's Algorithm for Quantum Factorization and Grover's Algorithm for Quantum Search) and their implementation and practices on some simulators (such as QuIDE from http://www.quide.eu/) or some prototype quantum processors (such as IBM's prototype quantum processors). For advanced level courses, some course materials are available from various sources (such as MIT OpenCourseWare). The greater challenge would be in teaching the introductory course mentioned earlier that motivates students towards investing time and efforts in learning quantum computing and related physics, math and computational topics. The challenges of teaching the introductory course should not be underestimated. Some flexible strategies can be suggested for this course so that the biggest challenges can be overcome. Some initial assessments of the students may help adjusting the introduction strategies with student's preparation. What is recommended is that the instructor should be able to adjust the coverage of topics, rigor and depth of treatment. That is, the introductory course should not be rigidly defined. It should be recognized that the main emphasis of the introductory course is actually instilling motivations and curiosity in student's mind about potentials of quantum computing through adaptive teaching of some selected topics where traditional digital computers do not provide tractable solutions. Ideally a balance could be achieved among aspects of depth of treatment, rigor, inspiration, motivation and coverage of topics. Students should be able to realize that the emerging quantum computing technology would be able to provide elegant solutions to some of the classical intractable problems combining ideas from physics, math and computer science in an innovative way. One size fits all is not a good approach and discussion of issues may help us face the challenges better by pragmatically considering a range of possible alternatives. Resources that are currently available may also need to be reviewed for the purpose of introducing quantum computing adequately.
This book introduces quantum computing and quantum communications at the undergraduate level for students in the physical sciences, engineering, and mathematics, assuming no prior knowledge of quantum mechanics. It is a self-contained guide assuming only that the reader is familiar with physics at the college level, calculus (up to and including ordinary differential equations), and some matrix algebra and complex numbers. The book brings the reader up to speed with fundamental concepts in quantum information processing and builds a working knowledge of basic quantum algorithms, quantum error correction, and quantum communication protocols. It covers various models of quantum computation and some of the most common physical realizations of qubits. There is a good number of practice problems and exercises that rely on computer programming with the Qiskit language. The book can be used to prepare students of physics, mathematics, electrical and computer engineering, computer science, optics and photonics, and mathematics for internships and research experiences in quantum information processing, both during and after their undergraduate studies. It also helps those who plan to apply to graduate school and do research in this area. Key Features: Self-contained coverage of quantum computing and quantum communications, from the motivations to the fundamentals and applications, including key concepts and contemporary topics. Contains practice problems and exercises, including some that require programming in Qiskit (the python-based, high-level language for programming quantum computers, adopted by nearly all quantum hardware providers and completely open source). Very light background knowledge assumed, making this book accessible to a variety of majors in the natural sciences, engineering, and mathematics.
Computational projects on quantum computing suitable for students in a junior-level quantum mechanics course are described. In these projects students write their own programs to simulate quantum computers. Knowledge is assumed of introductory quantum mechanics through the properties of spin 1/2. Initial, more easily programmed projects treat the basics of quantum computation, quantum gates, and Grover's quantum search algorithm. These are followed by more advanced projects to increase the number of qubits and implement Shor's quantum factoring algorithm. The projects can be run on a typical laptop or desktop computer, using most programming languages. Supplementing resources available elsewhere, the projects are presented here in a self-contained format especially suitable for a short computational module for physics students.