A major challenge for quantum workforce development is the need to both understand and reliably assess student learning of quantum information science (QIS) fundamentals. Yet student thinking is notoriously difficult to probe, even for seasoned education researchers. This article presents the story of Item 15 on the Quantum Computing Conceptual Survey (QCCS). This assessment item underwent more revision and discussion within the team than the remaining 19 assessment questions combined. This paper provides a behind-the-scenes look at the development of this assessment question: a story that both reveals interesting findings about student reasoning in quantum computing and illustrates why quantum education researchers insist on triangulating diverse quantitative and qualitative data sources when developing and refining assessment items, with implications for any researcher looking to understand and measure student conceptual reasoning in quantum computing, as well as for QIS curriculum and workforce development more broadly.
A major challenge for quantum workforce development is the need to both understand and reliably assess student learning of quantum information science (QIS) fundamentals. Yet student thinking is notoriously difficult to probe, even for seasoned education researchers. This article presents the story of Item 15 on the Quantum Computing Conceptual Survey (QCCS). This assessment item underwent more revision and discussion within the team than the remaining 19 assessment questions combined. This paper provides a behind-the-scenes look at the development of this assessment question: a story that both reveals interesting findings about student reasoning in quantum computing and illustrates why quantum education researchers insist on triangulating diverse quantitative and qualitative data sources when developing and refining assessment items, with implications for any researcher looking to understand and measure student conceptual reasoning in quantum computing, as well as for QIS curriculum and workforce development more broadly.
Quantum information science (QIS) is a critical interdisciplinary field, deeply rooted in physics, that requires a well-educated workforce in the near future. Numerous researchers and educators have been actively investigating how to best educate and prepare such a workforce. An open issue has been the lack of a tool with a strong validation argument to assess QIS understanding without requiring college-level math. In this paper, we present the systematic development of and content validation argument for a new assessment instrument called the Quantum Information Science Concept Introductory Test (QISCIT). With feedback from 11 QIS experts, we have developed a 31-item version of QISCIT that covers concepts like quantum states, quantum measurement, qubits, entanglement, coherence and decoherence, quantum gates and computing, and quantum teleportation.
Quantum information science (QIS) is a critical interdisciplinary field that requires a well-educated workforce in the near future. Numerous researchers and educators have been actively investigating how to best educate and prepare such a workforce. An open issue has been the lack of a validated tool to asses QIS understanding without requiring college-level math. In this paper, we present the systematic development and content validation of a new assessment instrument called the Quantum Information Science Concept Introductory Test (QISCIT). With feedback from 11 QIS experts, we have developed and validated a 31-item version of QISCIT that covers concepts like quantum states, quantum measurement, qubits, entanglement, coherence and decoherence, quantum gates and computing, and quantum communication. In addition to openly sharing our new concept inventory, we discuss how introductory QIS instructors can use it in their courses.
Abstract The revolutionary new field of quantum computing (QC) continues to gain attention in industry, academia, and government in both research and education. At educational institutions, there is a proliferation of introductory courses at various academic levels signaling a growing interest and recognition of the significance of this field. A crucial and often overlooked aspect is the development of research-based materials and pedagogical approaches to effectively teach the complexities of QC to diverse cohorts of learners across multiple disciplines. There is a great need for empirical investigations of the effectiveness of learning materials and pedagogical approaches in this new interdisciplinary field. We present an empirical investigation done at an R1 institution using the multiple case study method. We compare a case study on students in an introductory QC course without research-based mini-tutorials to a study of students taking the QC course with research-based mini-tutorials. We compare the strengths and difficulties of students in the two courses, discuss the general strengths and difficulties of students across both courses, postulate the effectiveness of the mini-tutorials and discuss how they can be revised. Strengths across both classes include the ability to apply single-qubit and two-qubit gates, favoring application of Dirac notation, and a reasonable understanding of normalization, probability and teleportation described qualitatively. Difficulties across both classes included use of matrix representation, use of rotation gates, and an ability to recall and analyze quantitatively a circuit representing teleportation. We postulate that the mini-tutorials on gates, normalization, probability and a qualitative description of teleportation may have been effective in increasing students’ understanding of the concepts. We also identify mini-tutorials that may need revision.
Quantum Computing is an exciting field that draws from information theory, computer science, mathematics, and quantum physics to process information in fundamentally new ways. There is an ongoing race to develop practical/reliable quantum computers and increase the quantum workforce. This needs to be accompanied by the development of quantum computing programs, courses and curricula coupled with the development and adoption of evidence-based materials and pedagogies to support the education of the next generation of quantum information scientists and workforce. At our university, we introduced an introductory course in Quantum Computing to undergraduate students and conducted a case study to investigate the strengths and difficulties of these students in quantum computing after taking the introductory course. Our goal is to contribute to the improvement of quantum computing education while understanding the topics that the students find easy to comprehend and topics that are difficult to comprehend. We conducted a series of interviews to identify these strengths and difficulties. This investigation is essential to help train the next generation of quantum scientists and researchers because it gives information and direction to quantum computing education, and it also helps us to develop research-based teaching and learning materials. We report on the results of these interviews and our initial work on the development of evidence-based materials for teaching an introductory course in Quantum Computing.
Quantum computing education requires students to move beyond classical programming intuitions related to state, determinism, and debugging, and to develop reasoning skills grounded in probability, measurement, and interference. This paper reports on the design and delivery of a combined undergraduate and graduate course at Louisiana State University that employed a lab-integrated learning model to support conceptual change and progressive understanding. The course paired lectures with weekly programming labs that served as environments for experimentation and reflection. These labs enabled students to confront misconceptions and refine their mental models through direct observation and evidence-based reasoning. Instruction began with Quantum Without Linear Algebra (QWLA), which introduced core concepts such as superposition and entanglement through intuitive, dictionary representations. The course then transitioned to IBM Qiskit, which provided a professional framework for circuit design, noise simulation, and algorithm implementation. Analysis of student work and feedback indicated that hands-on experimentation improved confidence, conceptual clarity, and fluency across representations. At the same time, it revealed persistent challenges in debugging, reasoning about measurement, and understanding probabilistic outcomes. This paper presents the course structure, instructional strategies, and lessons learned, and argues that lab-integrated learning offers an effective and accessible approach to teaching quantum computing in computer science education.
Abstract Despite rapid growth of quantum information science (QIS) workforce development initiatives, perceived lack of agreement among faculty on core content has made prior research-based curriculum and assessment development initiatives difficult to scale. To identify areas of consensus on content coverage, we report findings from a survey of N=63 instructors teaching introductory QIS courses at US institutions of higher learning. We identify a subset of content items common across a large fraction (≥ 80%) of introductory QIS courses that are potentially amenable to research-based curriculum development, with an emphasis on foundational skills in mathematics, physics, and engineering. As a further guide for curriculum development, we also examine differences in content coverage by level (undergraduate/graduate) and discipline. Finally, we briefly discuss the implications of our findings for the development of a research-based QIS assessment at the postsecondary level.