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[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] Research-based multiple-choice questions implemented in class with peer instruction have been shown to be an effective tool for improving students’ engagement and learning outcomes. Moreover, multiple-choice questions that are carefully sequenced to build on each other can be particularly helpful for students to develop a systematic understanding of concepts pertaining to a theme. Here, we discuss the development, validation, and implementation of a multiple-choice question sequence (MQS) on the topic of quantum measurement in the context of wave functions in the infinite-dimensional Hilbert space. This MQS was developed using students’ common difficulties with quantum measurements as a guide and was implemented in a junior-/senior-level quantum mechanics course at a large research university in the U.S. We compare student performance on assessment tasks focusing on quantum measurement before and after the implementation of the MQS and discuss how different difficulties were reduced and how to further improve students’ conceptual understanding of quantum measurement in infinite-dimensional Hilbert space. Published by the American Physical Society2025
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Compute Choice: Learning Distributions over Permutations
We discuss the question of learning distribution over permutations of a given set of choices, options or items based on partial observations. This is central to capturing the so called ``choice'' in a variety of contexts: understanding preferences of consumers over a collection of products based on purchasing and browsing data in the setting of retail and e-commerce, learning public opinion amongst a collection of socio-economic issues based on sparse polling data, deciding a ranking of teams or players based on outcomes of games, electing leaders based on votes, and more generally collaborative decision making based on collective judgement such as accepting paper(s) in a competitive academic conference. The question of learning distribution over permutations arises beyond capturing ``choice'' as well. For example, tracking a collection of objects using noisy cameras, or aggregating ranking of web-pages using outcomes of multiple search engines. It is only natural that such a topic has been extensively studied in Economics, Political Science and Psychology for more than a century, and more so recently in Computer Science, Electrical Engineering, Statistics and Operations Research. Here we shall focus on the task of learning distribution over permutations from its marginal distributions of two types: first-order marginals and pair-wise comparisons. There has been a lot of progress made on this topic in the last decade. The ideal goal is to provide a comprehensive overview of the state-of-art on this topic. We shall provide detailed overview of selective aspects, biased by author's perspective of the topic. And provide sufficient pointers to aspects not covered here. We shall emphasize on ability to identify the entire distribution over permutation as well as ``best ranking''.
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- PAR ID:
- 10112491
- Date Published:
- Journal Name:
- Cambridge University Press bulletin
- ISSN:
- 0951-2454
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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