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1. Exploring Quantum Reversibility with Young Learners

   https://doi.org/10.1145/3372782.3406255  

   Franklin, Diana; Palmer, Jen; Jang, Woorin; Lehman, Elizabeth M.; Marckwordt, Jasmine; Landsberg, Randall H.; Muller, Alexandria; Harlow, Danielle (August 2020, 2020 ACM Conference on International Computing Education Research (ICER '20))

   null (Ed.)

   Quantum computing is poised to revolutionize some critical intractable computing problems; but to fully take advantage of this computation, computer scientists will need to learn to program in a new way, with new constraints. The challenge in developing a quantum computing curriculum for younger learners is that two dominant approaches, teaching via the underlying quantum physical phenomenon or the mathematical operations that emerge from those phenomenon, require extensive technical knowledge. Our goal is to extract some of the essential insights in the principles of quantum computing and present them in contexts that a broad audience can understand. In this study, we explore how to teach the concept of quantum reversibility. Our interdisciplinary science, science education, computer science education, and computer science team is co-creating quantum computing (QC) learning trajectories (LT), educational materials, and activities for young learners. We present a draft LT for reversibility, the materials that both influenced it and were influenced by it, as well as an analysis of student work and a revised LT. We find that for clear cases, many 8-9 year old students understand reversibility in ways that align with quantum computation. However, when there are less clear-cut cases, students show a level of sophistication in their argumentation that aligns with the rules of reversibility for quantum computing even when their decisions do not match. In particular, students did not utilize the idea of a closed system, analyzing the effects to every item in the system. This blurred the distinction between between reversing (undoing) an action, recycling to reproduce identical items with some of the same materials, or replacing used items with new ones. In addition, some students allowed for not restoring all aspects of the original items, just the ones critical to their core functionality. We then present a revised learning trajectory that incorporates these concepts. 

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2. Investigating student interpretations of the differences between classical and quantum computers: Are quantum computers just analog classical computers?

   https://doi.org/10.1119/perc.2022.pr.Meyer  

   Meyer, Josephine C.; Passante, Gina; Pollock, Steven J.; Wilcox, Bethany R. (January 2022, 2022 Physics Education Research Conference Proceedings)

   Frank, B. W.; Jones, D. L.; and Ryan, Q. X. (Ed.)

   Significant attention in the PER community has been paid to student cognition and reasoning processes in undergraduate quantum mechanics. Until recently, however, these same topics have remained largely unexplored in the context of emerging interdisciplinary quantum information science (QIS) courses. We conducted exploratory interviews with 22 students in an upper-division quantum computing course at a large R1 university crosslisted in physics and computer science, as well as 6 graduate students in a similar graduate-level QIS course offered in physics. We classify and analyze students' responses to a pair of questions regarding the fundamental differences between classical and quantum computers. We specifically note two key themes of importance to educators: (1) when reasoning about computational power, students often struggled to distinguish between the relative effects of exponential and linear scaling, resulting in students frequently focusing on distinctions that are arguably better understood as analog-digital than classical-quantum, and (2) introducing the thought experiment of analog classical computers was a powerful tool for helping students develop a more expertlike perspective on the differences between classical and quantum computers. 

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3. Qupcakery: A Puzzle Game that Introduces Quantum Gates to Young Learners

   https://doi.org/10.1145/3545945.3569837  

   Liu, Tianle; Gonzalez-Maldonado, David; Harlow, Danielle B.; Edwards, Emily E.; Franklin, Diana (March 2023, Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023))

   Quantum computing (QC) is an emerging field at the intersection of computer science and physics. Harnessing the power of quantum mechanics, QC is expected to solve otherwise intractable problems significantly faster, including in encryption, drug development, and optimization. High-quality and accessible QC resources are needed to help students develop the critical skills and confidence to contribute to the field. However, existing programs are often aimed at college students with an advanced mathematics or physics background, shutting out potential innovators. To make quantum learning resources for a broad, young audience, we designed Qupcakery, a puzzle game that introduces players to several core QC concepts: quantum gates, superposition, and measurement. We present preliminary testing results with both middle school and high school students. Using in-game data, observation notes, and focus group interviews, we identify student challenges and report student feedback. Overall, the game is at an appropriate level for high school students but middle school students need more levels to practice when new concepts are introduced. 

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4. Engaging the public in quantum information science through interactive gamified learning

   Skinner, R; Wesolowski, K; Christman, D; Williams, G; Gonzalez-Maldonado, D; Garcia, L; Harlow, D; Franklin, D; Edwards, E (August 2025, ASTC dimensions)

   Quantum computers are projected to be able to carry out certain complex calculations that our current, classical computers cannot accomplish efficiently. The word quantum refers to the smallest possible unit of something, which in this context relates to the properties of tiny particles like atoms, electrons, and photons. Quantum computers use these properties to perform complex calculations in ways that are fundamentally different from non-quantum computers. In , quantum computers will be faster than classical computers. A quantum computing revolution requires a new generation of scientists and engineers who are familiar with quantum concepts and principles. Yet, educational efforts to teach the basic concepts of this field to a new generation are lacking [2]. A few efforts have been developed to introduce pre-college students to QIS, including an activity on quantum teleportation for secondary school students [3] and a series of coding-based activities for high-school students [4]. However, high-quality activities to promote QIS at the K-12 level are scarce, despite research showing that middle school is a crucial time for students as they begin to contemplate possible career paths [5,6]. This article describes the adaptation of an existing online educational computer game to introduce quantum computing concepts to an interactive science center audience from age seven to adult 

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5. Training of quantum circuits on a hybrid quantum computer

   https://doi.org/10.1126/sciadv.aaw9918  

   Zhu, D.; Linke, N. M.; Benedetti, M.; Landsman, K. A.; Nguyen, N. H.; Alderete, C. H.; Perdomo-Ortiz, A.; Korda, N.; Garfoot, A.; Brecque, C.; et al (October 2019, Science Advances)

   Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes. 

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