In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas.
In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas.
This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging properties like superposition and entanglement for information representation and manipulation. Quantum machine learning applies these principles to enhance classical machine learning models, potentially reducing network size and training time on quantum hardware. The paper covers basic quantum mechanics principles, including superposition, phase space, and entanglement, and introduces the concept of quantum gates that exploit these properties. It also reviews classical deep learning concepts, such as artificial neural networks, gradient descent, and backpropagation, before delving into trainable quantum circuits as neural networks. An example problem demonstrates the potential advantages of quantum neural networks, and the appendices provide detailed derivations. The paper aims to help researchers new to quantum mechanics and machine learning develop their expertise more efficiently.
Quantum Machine Learning refers to the interdisciplinary area of research, blending principles from quantum physics and machine learning that can process information on quantum computers. Quantum machine learning (QML) intentions to exploit the superposition (qubit can exist in multiple states) property and other effects of quantum mechanics for enhanced efficiency and speed in specific machine learning computations, promising exponential-power superiority in particular computations. This paper focusses on Quantum Machine Learning Models to develop the framework to learn complicated associations within the data through the adjustment of the variational parameters during training. This framework serves as a foundational structure for implementing quantum neural networks, allowing for further experimentation and growth in quantum machine learning applications. It highlights the quantum circuits to process and learn from data in a mode distinct from classical neural networks. As the retrieve of quantum computing grows, such frameworks can be adapted and expanded to explore more sophisticated quantum algorithms and architectures. In the circumstances of Quantum Neural Networks, gradient descents is used in minimizing cost or loss function by iteratively adjusting the variational parameters based onto the gradient of the loss with admire to those parameters. The output that indicates the process of installing the Quantum information science Kit (Qiskit) library in Python specifically version 3.7 along with its dependencies.
Quantum machine learning combines quantum computing with machine learning to solve complex computational problems more efficiently than classical approaches. This survey provides an introduction to the foundations, algorithms, frameworks, data and applications of quantum machine learning, serving as a resource for researchers and practitioners. We begin by reviewing existing surveys to identify gaps that this work addresses, followed by a detailed discussion of the foundational principles of quantum mechanics and machine learning essential for quantum machine learning. Key algorithms are examined, highlighting their mechanisms, advantages, and applications across various domains. Current frameworks and platforms for implementing quantum machine learning algorithms are explored, emphasizing their unique features and suitability for different contexts. Existing quantum datasets for practical usage are also reported and commented on. This survey also reviews over 135 articles, categorized into theoretical and practical contributions, to identify key advances, limitations, and application areas within quantum machine learning. Critical challenges such as hardware limitations, error rates, and scalability are analyzed to detect the obstacles that must be addressed for practical deployment. By synthesizing these elements into a structured overview, this survey aims at serving as both an introduction and a guide for advancing research and development in this disruptive field.
Abstract Quantum neural networks (QNNs) are emerging as a promising paradigm at the intersection of quantum computing and artificial intelligence. However, their conceptual abstraction and physical implementation remain challenging in educational contexts. In this work, we present a didactic framework to teach the fundamentals of QNNs by leveraging classical artificial neural networks (ANNs), Python simulation with Qiskit, and analysis of training dynamics using the Iris dataset. The paper begins with a theoretical comparison between classical and quantum neurons, followed by the design of a full QNN training algorithm implemented in Python. Through simulated results and pedagogical discussion, we show how learners can explore key aspects of quantum learning—including superposition, variational circuits, probabilistic measurement, and optimization—without access to quantum hardware. The use of graphical outputs and code-structure correspondence enhances student engagement and promotes algorithmic thinking. This approach supports quantum science education by offering a reproducible, interactive, and conceptually aligned method for introducing QNNs in undergraduate and graduate classrooms.
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
Machine learning techniques give impressive results in many areas. However, due to the physical limitation of integrated circuits which restricts their computational power growth, and the rapid advances in quantum computing, lots of research studies on quantum machine learning (QML) have been done recently. QML is a technique that uses quantum algorithms as parts of the implementation. Quantum algorithms use quantum mechanics and have the potential to outperform classical algorithms for a given problem. In this paper, three widely used machine learning algorithms are discussed and their quantum versions are presented, namely: quantum neural network, quantum autoencoder, and quantum kernel method. In addition, we discuss the potential capabilities of these QML algorithms and review recent work employing them. Moreover, a quantum neural network prototype is implemented using Qiskit as a proof of concept and tested on a real quantum computer. Empirical results show that quantum neural networks can be trained efficiently.