<!--HTML--> Abstract This hands-on session focuses on understanding the fundamental concepts of quantum computing, which are introduced during the presentation, such as superposition and entanglement, as well as one of the most well-known quantum algorithms, Grover's algorithm. The students will explore the practical usage of quantum computing with one of the most commonly used quantum computing languages, Qiskit, and learn how qubits form the foundation of quantum computation. By the end of the session, they should be able to code the quantum algorithm on their own and run it on different quantum simulators. Bio Graduated Master in Physics at EPFL in Switzerland, Su Yeon undertook her research on Quantum Generative Adversarial Networks to simulate High-Energy detectors. She is now working as a PhD student at EPFL and CERN to investigate quantum algorithms, especially focused on quantum machine learning for image classification and generation.
<!--HTML--> Abstract This hands-on session focuses on understanding the fundamental concepts of quantum computing, which are introduced during the presentation, such as superposition and entanglement, as well as one of the most well-known quantum algorithms, Grover's algorithm. The students will explore the practical usage of quantum computing with one of the most commonly used quantum computing languages, Qiskit, and learn how qubits form the foundation of quantum computation. By the end of the session, they should be able to code the quantum algorithm on their own and run it on different quantum simulators. Bio Graduated Master in Physics at EPFL in Switzerland, Su Yeon undertook her research on Quantum Generative Adversarial Networks to simulate High-Energy detectors. She is now working as a PhD student at EPFL and CERN to investigate quantum algorithms, especially focused on quantum machine learning for image classification and generation.
<!--HTML--> Abstract In this basic introduction to quantum computing, the underlying mathematical concepts will be presented together with quantum mechanical phenoma of interest such as superposition and entanglement in order to enable the understanding of basic quantum circuits. Bio Alice is an aerospace engineer by background. After working 5 years in the space and defence industry, she started her quantum journey as a PhD student in Quantum Computing at CERN. She is working on quantum algorithms for dynamical systems, including ODE solving and ML based time series analysis.
Quantum computing is an emerging form of computing that uses the rules of quantum physics to process information in ways that are far different from how computers do it now. Quantum computing uses quantum bits, or qubits, which can exist in superpositions of states and get entangled with each other, which is different from classical computing, which uses a bit that represents binary values (0 or 1). These characteristics allow quantum devices to execute parallel computations, enabling significant speedups for specific categories of problems considered intractable for classical machines. This chapter gives a thorough introduction to quantum computing, starting with a historical overview of its foundations in quantum physics and significant theoretical advancements. It analyzes fundamental ideas including superposition, entanglement, quantum interference, and the no-cloning theorem, explaining how these elements distinguish quantum computation from traditional approaches. The advancement of quantum hardware is examined through several architectures, trapped ions, superconducting circuits, photonics, and topological qubits, together with software frameworks like Qiskit, Cirq, and PennyLane, which support algorithm development and simulation. The chapter examines fundamental quantum algorithms, such as Grover’s algorithm for database search, Shor’s algorithm for integer factorization, and hybrid variational methods designed for noisy intermediate-scale quantum devices. The practical applications in domains including as encryption, optimization, machine learning, chemistry, and finance emphasize the real-world significance of quantum technology. Challenges like decoherence, noise, scalability, and algorithmic maturity are rigorously analyzed, followed by an anticipated discourse on future trajectories including fault-tolerant architectures, quantum networking, and the development of quantum cloud services. This chapter presents a fundamental introduction for students, researchers, and practitioners seeking to comprehend the essential principles, functionalities, and possibilities of quantum computing in the post–Moore’s law period.
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.
The basics of quantum computing will be discussed. The introduction to different types of computing environments will be done. The concept of qubits will be introduced and compared with bits. The concepts of superposition and quantum entanglement will be discussed. The application of Shor’s and Grover’s algorithm will be discussed briefly and how these algorithms contribute for the enormous power of quantum computing will be elaborated. The brief description of quantum gates will be introduced. Algorithms like Quantum Hadamard Edge Detection can identify outlines and features significantly faster than classical methods will be explained. Topics on protection of visual data through quantum encryption and quantum key distribution will be explained. The pattern recognition using hybrid quantum machine learning models for image classification and segmentation will be elaborated.
Quantum computing is redefining computational boundaries, offering solutions to complex problems that classical computing struggles to address. A deep understanding of quantum principles, algorithms, and practical tools is essential for professionals and researchers navigating this transformative technology. Quantum computing is no longer a distant dream—it is rapidly becoming a cornerstone of modern innovation. This book provides a structured introduction to quantum computing, covering foundational principles such as superposition and entanglement, quantum gates, and circuits. Readers will gain hands-on experience with Qiskit and IBM Q, explore quantum error correction, and understand the real-world applications of quantum computing across industries. Alongside theoretical concepts, readers are introduced to hands-on quantum programming using Qiskit and IBM Q. The book also addresses practical challenges like quantum error correction and showcases real-world applications across industries, including cybersecurity, artificial intelligence, and material science. By the end of this book, readers will have a solid grasp of quantum computing fundamentals and hands-on experience with practical tools. Equipped with both conceptual understanding and hands-on experience, readers will be prepared to contribute meaningfully to research, solve industry-relevant problems, and engage confidently with the rapidly evolving field of quantum computing. --
<!--HTML--> Abstract In this basic introduction to quantum computing, the underlying mathematical concepts will be presented together with quantum mechanical phenoma of interest such as superposition and entanglement in order to enable the understanding of basic quantum circuits. Bio Ema is a computer scientist pursuing PhD in physics at CERN and Universitat Autonoma de Barcelona, specializing in quantum and quantum-inspired algorithms for anomaly detection with applications in high-energy physics and medical imaging.