Learning Quantum Mechanics

Letty Koopman, Wolter H. Kaper, A.L. Ellermeijer +2 more

GORDON J. TROUP, M. L. Whippman

Tim Bouchée, L.G.A. de Putter Smits, Marieke C. G. Thurlings +1 more

Quantum mechanics (QM) was prominently featured in the Dutch national physics examination for upper secondary school level in 2016 for the first time. It appears that many students have struggled to learn this new subject and had problems answering the associated examination questions. From the literature it is known that QM is difficult to understand, and to visualize in everyday settings. This is likely to hinder students' development of a conceptual understanding of QM. In this proposal we apply the perspective of Conceptual Field Theory to the learning of QM, to develop a deeper understanding of problems that arise in its learning processes. Conceptual Field Theory is a theory that emphasizes action to develop students' concepts, and the action is set in task-based situations presented to students. We argue that the main problem associated with learning QM is that it is difficult for students to come to meaningful representations appropriate for learning QM. Without meaningful representations it is hard to formulate correct propositions that help to handle and analyze presented task-based situations. In this proposal we describe why it is difficult to come to meaningful representations in QM and we suggest how research into learning and teaching QM could proceed.

David E. Gardner

This thesis describes qualitative research conducted to understand the problems students have when learning quantum mechanics. It differs from previous studies on educational issues associated with quantum mechanics in that I have examined the difficulties from the students' perspective. Three questions guided this research: What are the experiences of students learning quantum mechanics? What conceptual difficulties do students have with quantum mechanics? and, How do students approach learning quantum mechanics? From these questions, two themes emerged. First, students do not consider the quantum mechanical concepts of wave-particle duality or the uncertainty principle to be important sources of difficulties for them. Second, many of the difficulties students encounter are not related to conceptual understanding of specific topics, but stem from a mindset that is incongruent with the nature and structure of quantum mechanics. The implications for teaching are that the nature and structure of quantum mechanics should be emphasized and be an explicit part of instruction.

G. J. Troup, M. L. Whippman

Arshdeep, Konika Rani, Karanpreet Singh +3 more

Machine learning, a subset of AI is moving algorithms in the system which can be learned by data and not programmed explicitly. Various applications of AI across professionals like finance, health-care and automation. With the advancements in Machine Learning, Quantum Machine Learning (QML) or more concretely integrating quantum mechanics into classical machine learning tasks is emerging since QM provides some of the principles to increase efficiency from computational benchmarks. From the most used types of machine learning and algorithms to quantum its usage and applications in Quantum Machine Learning (QML) this paper covers everything. We then articulate some benefits and difficulties of merging quantum computing with machine learning, as well the real-life applications across industry backbones.