An appropriate quantum algorithm is needed for each problem to achieve the full performance of a quantum computer. It is necessary to understand the principles of quantum computation to implement quantum algorithms. Quantum computation simulators can assist in understanding the principles and behavior of quantum computation. Combining this with explanations of quantum algorithms using visualization techniques and interactions can further aid learning. In this study, to support the understanding of quantum computation, a difficult concept for beginners, we develop an interactive system in which diagrams and graphs are added to a quantum computation simulator to support the explanation and understanding of qubits and quantum algorithms. To verify the effectiveness of the interaction in supporting learning, a subject experiment is conducted to compare the amount of knowledge understood by a group of novice quantum computer students who learned using the developed system with a group who learned using paper-based materials.
An appropriate quantum algorithm is needed for each problem to achieve the full performance of a quantum computer. It is necessary to understand the principles of quantum computation to implement quantum algorithms. Quantum computation simulators can assist in understanding the principles and behavior of quantum computation. Combining this with explanations of quantum algorithms using visualization techniques and interactions can further aid learning. In this study, to support the understanding of quantum computation, a difficult concept for beginners, we develop an interactive system in which diagrams and graphs are added to a quantum computation simulator to support the explanation and understanding of qubits and quantum algorithms. To verify the effectiveness of the interaction in supporting learning, a subject experiment is conducted to compare the amount of knowledge understood by a group of novice quantum computer students who learned using the developed system with a group who learned using paper-based materials.
Quantum education has been increasingly emphasized at both the post-secondary and secondary education stages, in line with the advent of the quantum computing paradigm. Traditional quantum computing education, however, necessitates an extensive background in physics, mathematics, and information science, which can be challenging for beginners. This study presents the experience of implementing a college curriculum at National Chi Nan University in Taiwan, designed to enable learners at all levels to grasp the concept of a quantum algorithm using accessible math yet rigorous enough for verification. The study succinctly conveys the concept of the quantum Grover algorithm, provides necessary background knowledge, and elucidates the ideas using three distinct visualization methods. Furthermore, this study offers a novel presentation for aligning the results of geometrical representation with the corresponding quantum circuit layout. The hands-on project demonstration motivates learners to explore solutions using their visualization tool, enhancing their engagement and sense of achievement. Developing these tools deepens learners' understanding and promotes active peer education, thereby improving their participation and retention of quantum knowledge. The visualization tools developed through this process serve as a valuable contribution to quantum education, offering an effective quantum tool and a user-friendly interface for learning, teaching, researching, and providing fresh insights and perspectives. By making quantum computing concepts more understandable through accessible math and visualization, we aim to lower the entry-level barrier to learning quantum computing, encouraging more talents to engage in quantum computing for their research studies.
We present a practical course targeting graduate students with prior knowledge of the basics of quantum computing. The practical aims to deepen students' understanding of fundamental concepts in quantum computing by implementing quantum circuit simulators. Through hands-on experience, students learn about different methods to simulate quantum computing, including state vectors, density matrices, the stabilizer formalism, and matrix product states. By implementing the simulation methods themselves, students develop a more in-depth understanding of fundamental concepts in quantum computing, including superposition, entanglement, and the effects of noise on quantum systems. This hands-on experience prepares students to do research in the field of quantum computing and equips them with the knowledge and skills necessary to tackle complex research projects in the field. In this work, we describe our teaching approach and the structure of our practical, and we discuss evaluations and lessons learned.
In the last year, quantum computing has rapidly transitioned from a purely theoretical field into applied engineering, where multiple algorithms can approach combinatorial optimization problems, machine learning, and cryptography in novel ways, often achieving speedup compared to classical solutions. In this context, there is an increasing need for educational tools that connect mathematical principles from quantum information theory with practical experimentation. Yet, most existing educational resources remain either highly theoretical or provide fragmented code examples, with limited analysis and applications to real-world use cases. As a result, students’ experiences appear restricted, with non-interactive tools for learning. This work presents a comprehensive framework for teaching quantum computing through interactive resources, developed via MATLAB Live Scripts. By combining theory, executable code, and visualization tools within a single environment, this framework enhances learners’ ability to see and reason about quantum algorithms step by step. Following the principles of Project-Based Learning in STEM education, we present a diverse collection of quantum resources, including algorithms for optimization, communication, and error correction. Each project operates as a self-contained learning unit, offering modularity, white-box implementations, and tools for visualization and analysis. Beyond its educational impact, this framework also employs and extends the MATLAB Support Package for Quantum Computing, offering an open-source, reproducible environment suitable for courses and independent study.
[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name a few. To prepare students for such opportunities, it is important to give them a strong foundation in the basics of QISE, in which quantum computing plays a central role. In this study, we discuss the development, validation, and evaluation of a Quantum Interactive Learning Tutorial, on the basics and applications of quantum computing. These include an overview of key quantum mechanical concepts relevant to quantum computation (including ways a quantum computer is different from a classical computer), properties of single- and multiqubit systems, and the basics of single-qubit quantum gates. The tutorial uses guided inquiry-based teaching-learning sequences. Its development and validation involved conducting cognitive task analysis from both expert and student perspectives and using common student difficulties as a guide. For example, before engaging with the tutorial, after traditional lecture-based instruction, one reasoning primitive that was common in student responses is that a major difference between an N -bit classical and N -qubit quantum computer is that various things associated with a number N for a classical computer should be replaced with the number 2 N for a quantum computer (e.g., 2 N qubits must be initialized and 2 N bits of information are obtained as the output of the computation on the quantum computer). This type of reasoning primitive also led many students to incorrectly think that there are only N distinctly different states available when computation takes place on a classical computer. Research suggests that this type of reasoning primitive has its origins in students learning that quantum computers can provide exponential advantage for certain problems, e.g., Shor’s algorithm for factoring products of large prime numbers, and that the quantum state during the computation can be in a superposition of 2 N linearly independent states. The inquiry-based learning sequences in the tutorial provide scaffolding support to help students develop a functional understanding. The final version of the validated tutorial was implemented in two distinct courses offered by the physics department with slightly different student populations and broader course goals. Students’ understanding was evaluated after traditional lecture-based instruction on the requisite concepts and again after engaging with the tutorial. We analyze and discuss their improvement in performance on concepts covered in the tutorial. Published by the American Physical Society 2024
With the rapid progress of quantum computation recently, it attracts a substantial amount of people to learn quantum computation. Quantum computation differs a lot from classical computation. To help novices learn quantum computation, we conducted a interview with people who are learning quantum computation and found that novices feel confused about 1) how quantum gates contribute to the final results in a complicated quantum circuit and 2) how the final results generate steps along the quantum circuits. Thus, we present QuFlow, an interactive visualization tool for teaching the fundamentals of quantum computation. Users can use it to build a quantum circuit, then QuFlow will simulate the quantum circuit in a classical computer. After simulation, QuFlow will not only present the final output results of quantum circuits but also shows how the parameters change along the quantum circuits. A qualitative user study was carried out among target users, and the results suggested that QuFlow could be effective for learning quantum computation.
Quantum computers are a new generation of computers, whose principles for designing algorithms have already been defined, despite their hardware implementation still being under development. Attracting more scientists to the field is crucial to developing new algorithms in order to make the most of quantum computers once they are available. Serious games can contribute a lot to this effort. This article focuses on the pilot evaluation of the Quantum Escape serious game, which is based on a 3D format for motivating students and a conceptual simplification strategy for making abstract concepts accessible. Specifically, the game design is grounded in the established Four-Dimensional framework, and it aims to transform abstract concepts like superposition and entanglement, as well as common quantum gates, into intuitive puzzles presented through probabilities. The design of Quantum Escape was evaluated by 27 Computer Science students and graduates, in terms of perceived player experience and perceived short-term learning using a questionnaire based on the established MEEGA+ evaluation framework. The design choices of Quantum Escape seem to be validated, since most novice participants found the game understandable and requiring no prior knowledge, while participants with relevant background in quantum computing also largely endorsed the content’s accuracy. Limitations in the extent of quantum computing concepts and the number of participants dictate further development and evaluation of the game.