This educational paper presents a modular workshop framework for teaching quantum programming and hardware-oriented reasoning within microelectronics education. The framework integrates five connected instructional elements. They are Qiskit foundations laboratory on gates, measurement, and Bell-state entanglement; a noise-aware quantum arithmetic module featuring GHZ circuits, ripple-carry addition, and quantum carry-lookahead addition; a structured adder-analysis exercise focused on depth, ancilla, and reversible cleanup; a capstone project on noise-resilient and security-aware 4-bit quantum adders; and a bridge from classical machine learning workflows to quantum machine learning. The paper redefines these activities explicitly as learning modules with student learning outcomes, module learning outcomes, assessment options, and discussion prompts. The resulting sequence helps learners bridge from syntax and circuit building to architecture-level trade-offs, noise interpretation, approximate design, and research-oriented thinking, all of which are well-suited for the quantum workforce.
This educational paper presents a modular workshop framework for teaching quantum programming and hardware-oriented reasoning within microelectronics education. The framework integrates five connected instructional elements. They are Qiskit foundations laboratory on gates, measurement, and Bell-state entanglement; a noise-aware quantum arithmetic module featuring GHZ circuits, ripple-carry addition, and quantum carry-lookahead addition; a structured adder-analysis exercise focused on depth, ancilla, and reversible cleanup; a capstone project on noise-resilient and security-aware 4-bit quantum adders; and a bridge from classical machine learning workflows to quantum machine learning. The paper redefines these activities explicitly as learning modules with student learning outcomes, module learning outcomes, assessment options, and discussion prompts. The resulting sequence helps learners bridge from syntax and circuit building to architecture-level trade-offs, noise interpretation, approximate design, and research-oriented thinking, all of which are well-suited for the quantum workforce.
Quantum computing promises solutions to problems that are beyond the reach of classical machines; however, current noisy intermediate-scale quantum (NISQ) devices require both theoretical insight and hardware expertise.This paper presents a six-module curriculum designed to bridge that gap for undergraduates from diverse disciplines.The modules progress from Python programming to electronics and circuits, vacuum technology, cryogenic measurements, microwave and RF systems, and culminate with quantum simulations and computations.Implemented at Winston-Salem State University with thirty students across computer science, information technology, mathematics, chemistry, physics, and biology, the program combined lectures, group projects, and presentations.Hands-on activities included resistor network analysis, magnetic levitation, vacuum chamber assembly, cryogenics measurements, RF component analysis, and entanglement experiments, enabling students to connect abstract principles with laboratory practice.Outcomes showed increased confidence, collaboration, critical thinking, and skill set building, even among those with no prior background.Students advanced to research-oriented projects like secure quantum communication protocols and the Quantum Mini Tennis game, which explored gate operations and qubit transformations, demonstrating that hardware-aware quantum education lowers barriers, fosters interdisciplinary participation, and prepares students for NISQ-era challenges.
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.
Quantum computing education is becoming urgent as industry demand and national initiatives grow rapidly. This paper introduces the Quantum Learning Pyramid (QLP), a unified pedagogical framework for undergraduate and graduate education in quantum information and computing. The QLP follows a four-tier structure that integrates phenomenological understanding, computational thinking, hardware-aware development, and societal context. The curriculum is designed using spiral progression, competency-based pathways, and authentic assessment. Instruction is grounded in active and project-based learning, aligned with ACM/IEEE curriculum guidelines. Core topics include quantum mechanics fundamentals, qubit operations, and key algorithms, while advanced modules address error correction, cryptography, and quantum hardware. Hands-on learning is supported through simulation platforms, cloud-accessible quantum processors, and hybrid laboratory environments. Interdisciplinary case studies and real-system experimentation are embedded throughout. The proposed framework bridges theory and practice and provides a scalable roadmap for developing a quantum-ready workforce and scientifically informed citizens.
Quantum computing and artificial intelligence (AI) are two influential technologies that are beginning to shape how we solve problems in cybersecurity and education. As cyber threats become progressively complex, there is a growing need for students to understand how these advanced tools work and how to apply them in real-world sites. This paper presents a simple yet effective framework for designing hands-on labs that teach students about quantum machine learning (QML), which leverages the strengths of both quantum computing and Artificial Intelligences. These labs are meant to help students learn by solving practical cybersecurity challenges while developing technical skills in coding, quantum circuits, and machine learning models. We also reviewed key research from experts in the fields of quantum computing, cybersecurity, and education. Their insights helped us determine how to organize the topics and where they should be placed in a learning program. The goal is to make the learning process more interactive, scalable, and relevant to the digital world today. This method not only teaches students new technologies but also provides them with the chance to experience how these tools can be used to defend against real-world cyber threats.
This poster explores strategies for supporting quantum computing education in academic libraries and invites collaborative discussion on building sustainable support models. As quantum computing emerges as a transformative technology with potential to solve complex research questions across disciplines, institutions of higher education must develop effective and scalable support frameworks. Successful integration of quantum computing education and support may position institutions at the forefront of breakthrough computational research and confer benefits such as strengthening the institutional ability to attract top faculty and students, boost workforce development, and deliver a competitive advantage for funding opportunities. We present our initial framework for quantum computing support developed at the University Libraries, University of Alabama. Our instructional model employs backward design principles, emphasizing hands-on engagement with open-source quantum development tools and focusing on transferable computational thinking skills that extend beyond specific tools. Our approach centers on workshops that prioritize achievable learning outcomes tailored to diverse student backgrounds, incorporate active learning strategies, and emphasize real-world applications to build quantum literacy across disciplines. Both our successes and ongoing challenges are shared to inform broader discussions. More broadly, we invite engagement with our framework and discussion across four critical dimensions of emerging technology support including pedagogical approach, assessment strategies, technology platforms and infrastructure, and collaborative support models. Pedagogically, we seek to explore which instructional methods most effectively build quantum computing literacy for students with diverse academic backgrounds as well as strategies to strike the balance between theoretical foundations and hands-on practice. With assessment, we seek meaningful assessment approaches that can address varied student backgrounds and account for steep learning curves accompanying emerging technologies. For technology, we endeavor to discover which platforms will work best within higher education and ensure access and reliability for students and educators on a broad scale. And finally, we seek to examine how academic libraries and other units collaborate to build sustainable, coordinated support infrastructure. As quantum computing and other emerging technologies reshape higher education, academic libraries may be uniquely positioned to lead support for quantum computing by democratizing access to computational resources and bridging gaps between research, instruction, and student success. To that end, we invite discussion on how academic libraries can help to build sustainable, adaptable, and collaborative models to support quantum computing. We seek experiences, challenges, and strategies to inform best practices while aligning with library and other campus computing efforts and meeting the evolving needs of students.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.