Teaching Quantum Neural Network (QNN) Concepts Through Artificial Neural Networks (ANNs): A Python Example Using the Iris Dataset

Teaching Quantum Neural Network (QNN) Concepts Through Artificial Neural Networks (ANNs): A Python Example Using the Iris Dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Teaching Quantum Neural Network (QNN) Concepts Through Artificial Neural Networks (ANNs): A Python Example Using the Iris Dataset Ernane J X Costa, Jose Lucas de Melo Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7660789/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Special Education Quantum Neural Networks Science Education Computational Thinking Python Programming Quantum Simulation Machine Learning Education Figures Figure 1 1. Introduction Teaching the fundamental concepts of an emerging technology through clear and objective examples is essential to engage students and foster understanding of complex topics. Quantum computing represents an imminent disruption in the field of hardware, offering computational capabilities significantly beyond those of classical machines ( NYIRAHABIMANA, 2023 ). In this context, Quantum Neural Networks (QNNs) emerge as a new frontier in artificial intelligence algorithms, leveraging the unique features of quantum mechanics to enhance machine learning and problem-solving in high-dimensional and non-convex domains. Quantum computing is finally on the path to technological disruption, especially with the announcement of novel quantum devices, which may mark a turning point toward building functional and scalable quantum computers (AHMET, 2020 ). Additionally, Nguyen ( 2025 ) argues that quantum technology represents not only an innovation but also a potential source of systemic risk in finance, emphasizing the broad transformative impact this field may have across industries and scientific domains. Quantum computing has evolved significantly since its conceptual foundations, with major breakthroughs such as Shor’s algorithm for integer factorization and Grover’s algorithm for database search. These developments demonstrate the potential of quantum systems to solve problems considered intractable by classical computation. Current research in quantum computing spans the development of stable quantum hardware, efficient algorithms, and applications in cryptography, materials simulation, and artificial intelligence. Recent literature highlights the importance of interdisciplinary approaches to overcome the technical and theoretical challenges that still persist ( Nyirahabimana , 2023). Parallel to technological advances, the teaching of quantum mechanics faces ongoing challenges due to its abstract concepts and difficulties in contextualizing them for learners. Studies suggest that innovative didactic strategies, such as analogies, simulations, and interdisciplinary approaches, may help students comprehend quantum principles. For instance, the integration of literature and art into science instruction has been explored as a strategy to make abstract ideas more relatable and to stimulate student interest in modern physics (SOUZA, 2024). The Journal of Science Education and Technology has published numerous studies proposing pedagogical methods to enhance the teaching of quantum mechanics. These works emphasize the need to rethink teaching practices, promoting more meaningful learning experiences connected to students’ prior knowledge and lived experiences. The incorporation of digital technologies and multimedia resources is also highlighted as a means of enriching learning environments and bridging the gap between abstract quantum concepts and students’ understanding ( Nyirahabimana , 2023). In light of these technological and educational trends, it becomes essential to bridge the gap between traditional artificial intelligence education and the emerging principles of quantum computing. Given that many students are already familiar with classical neural networks (ANNs), this article proposes a didactic approach to introduce Quantum Neural Networks (QNNs) through a practical example implemented in Python using the Qiskit framework and the well-known Iris dataset. This strategy aims to ease the conceptual transition from ANNs to QNNs by exploring their structural analogies and highlighting the distinctive features of quantum processing in an accessible and reproducible learning environment. 2. Theoretical Foundations 2.1 Artificial Neural Networks (ANNs) Artificial Neural Networks (ANNs) are computational models inspired by the behavior of biological neurons. A fundamental component of an ANN is the artificial neuron, which performs a weighted sum of its inputs followed by a nonlinear activation function that defines its output. The mathematical model of a classical neuron is described by the Eq. ( 1 ): $$\:y=F\left({\sum\:}_{i=1}^{N}{w}_{i}{x}_{i}+b\right)$$ 1 Onde: x i are the input values, typically real numbers representing features of the input data (e.g., petal length in the Iris dataset). w i are the weights associated with each input. They determine the importance of each feature in the computation. Larger weights assign more significance to the corresponding inputs. b is the bias term, added to the weighted sum to allow shifting the activation function and improving the model’s flexibility. \(\:{\sum\:}_{i=1}^{N}{w}_{i}{x}_{i}+b\) is the weighted sum that represents the local induced field of the neuron and determines how “activated” it will be. F(.) is the activation function, a nonlinear transformation such as the sigmoid, ReLU, or tanh. It introduces non-linearity, enabling the network to model complex patterns. y is the final output of the neuron, passed either to other neurons in deeper layers or used as the model’s prediction. The implementation of a classical neuron is illustrated in the Python code in Box 1, which serves as a didactic tool to reinforce the mathematical formulation through computational practice. By translating the neuron model into executable code, students can directly observe how weighted sums and activation functions operate, thus strengthening their conceptual understanding through hands-on experimentation. Box 1 – Python implementation of a classical neuron. This code demonstrates the computation of a simple artificial neuron using NumPy and a sigmoid activation function. 2.2. Quantum Neural Networks (QNNs) Quantum Neural Networks (QNNs) are machine learning models that integrate the principles of quantum computing. Unlike classical neurons that use real-valued inputs and weights, QNNs operate on qubits—quantum information units that can exist in superposition and exhibit entanglement. A QNN typically involves three stages: Encoding of classical data into quantum states (feature map) ; Application of a variational quantum circuit , composed of parameterized quantum gates such as RX, RY, and CNOT; Measurement , collapsing the quantum state to obtain classical outputs. The quantum neuron does not rely on a weighted sum but instead applies a unitary transformation to the quantum state. Its behavior is expressed by Eq. (2): |ψ⟩ = U(θ)|ϕ(x)⟩ (2) Where: |ϕ(x)⟩ is the quantum state representing the encoded classical input. U(θ) is a parameterized unitary transformation (the quantum circuit), where θ are trainable parameters analogous to weights in classical neural networks. |ψ⟩ is the final state of the system after processing. The output is obtained via quantum measurement , typically reported as the probability of observing a particular state (e.g., ∣1⟩), which serves as the equivalent of the neuron’s activation. To enable practical experimentation without requiring access to real quantum hardware, we use Qiskit Aer, a high-performance simulator designed to emulate quantum circuits under various configurations. Aer supports multiple simulation backends, including ideal noiseless environments ( statevector ) as well as realistic noisy models, making it suitable for both conceptual demonstrations and more advanced studies involving decoherence and gate errors (Silva, 2024 ). The AerSimulator backend allows the execution of quantum circuits over thousands of repeated trials ( shots ), providing students with an intuitive understanding of probabilistic measurement outcomes and the role of quantum noise. Moreover, Aer is fully operable on local machines, which facilitates its integration into academic settings with limited computational resources. Its flexible architecture, including support for statevector , density matrix , and GPU-accelerated modes, enables educators to tailor simulations to different instructional levels (Abraham et al., 2019 ). To complement the theoretical formulation presented above, Box 2 provides a simple Python implementation of a quantum neuron using Qiskit. This example serves as a computational parallel to the classical neuron presented in Box 1 and allows students to explore the quantum encoding, variational processing, and probabilistic output of a basic QNN in practice. Box 2 – Python implementation of a quantum neuron.This code illustrates a simple quantum neuron built with Qiskit. The feature map encodes classical data via RX rotations; the variational circuit applies parameterized RY rotations and entanglement; measurement extracts the probability output. Table 1 summarizes the main differences between a classical neuron and a quantum neuron, considering their fundamental operations, mathematical representations, and implementation characteristics. Table 1 – Comparison between the main characteristics of classical and quantum neurons. Characteristic Classical Neuron (ANN) Quantum Neuron (QNN) Input data Real-valued vectors Qubits (superposition of 0 and 1) Processing Weighted sum + activation Unitary transformation + measurement Trainable parameters Weights and bias Rotation angles of quantum gates Output Continuous value after activation Probability of measuring a specific quantum state Implementation Classical matrix operations Quantum circuits and unitary gates 3. Methodology and Implementation This study adopts a didactic and comparative methodology, in which the implementation of a Quantum Neural Network (QNN) is developed side-by-side with a Classical Artificial Neural Network (ANN) using Python. The aim is to facilitate the conceptual transition between classical and quantum paradigms for students already familiar with basic machine learning models. The methodology follows four pedagogical and computational stages: 3.1. Dataset and Problem Framing To ensure familiarity and replicability, we use the Iris dataset, a classical benchmark introduced by Fisher in 1936, with 150 instances and four numeric features. Its pedagogical prominence comes from: Simplicity with educational value: small, real, and clean dataset, yet non-trivial for classification tasks (Ghosh et al, 2024) Historical significance: connects students to the origins of statistical learning and machine learning. (Fisher, 1936 ) Educator consensus: widely used in curricula to teach feature selection, model comparison, and linear separability concepts (Goswami and Chatterjee 2020) Constructivist advantages: enhances students' conceptual understanding, as observed in AI education research. (Chen et al, 2024) For simplicity and to conform to QNN’s binary output, we restrict the task to two classes only (Iris setosa vs. Iris versicolor), ensuring clarity and reducing computational overhead. A classic benchmark in supervised learning, readily available in the scikit-learn library. For simplicity and to ensure binary classification—suitable for one-qubit quantum circuits—only the first two classes (Setosa and Versicolor) are selected, totaling 100 data points with four numerical features each. 3.2. Classical Neuron as Foundation As described in Section 2.1 , a classical artificial neuron is implemented using a weighted sum and a sigmoid activation function. This implementation is included in Box 1 and serves as the conceptual and computational foundation upon which the quantum neuron is later built. Students are encouraged to run and modify the ANN code first to reinforce their understanding of key concepts such as weight adjustment, bias, and nonlinear activation. From a pedagogical standpoint, introducing the classical model first allows students to activate prior knowledge and provides a structured scaffold for learning more abstract quantum concepts. As supported by educational research in computational thinking and analogical reasoning, building upon familiar models enhances cognitive transfer and improves retention when dealing with complex or novel content (Grover & Pea, 2013). In this context, the classical neuron acts as a cognitive anchor: by seeing how data are transformed, parameters are adjusted, and outputs are produced in a known environment, learners are better equipped to make sense of similar processes in the quantum domain, such as gate parametrization, feature encoding, and probabilistic measurement. This methodological progression—from classical to quantum—is not only conceptually coherent but also aligns with best practices in STEM education, where layered abstraction and practical experimentation play a central role in student engagement and understanding. 3.3. Quantum Neural Network Construction The Quantum Neural Network (QNN) is implemented using the Qiskit framework and executed on the Aer simulator, avoiding dependence on physical quantum hardware. The QNN is executed on the Aer simulator, a high-performance simulation backend provided by Qiskit. This simulator emulates quantum circuits classically, enabling educators and students to explore quantum algorithms without requiring access to real quantum hardware. By simulating ideal quantum gates and measurements, Aer provides a stable and reproducible environment for teaching foundational quantum computing concepts. The implementation is designed not only to be functional but also to serve as a didactic instrument that complements students' understanding of neural networks from a quantum computing perspective. The process involves three main stages: Feature Mapping: Each of the four input features is encoded into the quantum circuit via RX rotations, transforming classical data into quantum states. Variational Circuit: A sequence of entangling operations (CNOT gates) and parameterized RY rotations is applied. The RY gates are adjusted by a vector of parameters θ\thetaθ, which play a role analogous to trainable weights in classical neural networks. Measurement: One qubit (typically qubit 0) is measured repeatedly—over 10,000 shots—and the probability of observing the state ∣1⟩|1⟩∣1⟩ is interpreted as the output of the quantum neuron. To reinforce conceptual understanding, Box 2 presents the complete Python implementation of a quantum neuron, written with didactic clarity. Each line is commented to help students draw parallels with the classical neuron from Box 1, and to observe how encoding, transformation, and measurement are used to emulate the functional behavior of a neuron using quantum operations. From a pedagogical perspective, this structured implementation enables students to manipulate quantum circuits in a hands-on environment, identify the role of each quantum gate in the model’s logic, and relate abstract quantum operations to familiar neural network mechanisms. The complete QNN pipeline mimics the behavior of an ANN by adjusting the parameters θ through a finite difference gradient estimation method. Since quantum circuits are not differentiable in the classical sense, this approximation allows the training loop to follow a similar pattern to that of gradient descent in traditional machine learning. 3.4. Training and Evaluation The model is trained over 30 epochs. For each training example, the output probability of the QNN is compared to the target label (0 or 1), and the loss is calculated as the squared error. The gradient is computed numerically and used to update the parameters. A simple loop-based training scheme is provided to enhance transparency for students, rather than using an opaque optimization library. At the end of training, the model’s accuracy is evaluated on the test set using a thresholding approach (e.g., output < 0.5 ⇒ class 0; output ≥ 0.5 ⇒ class 1). Students are encouraged to compare the QNN’s performance with the ANN under the same input and structure constraints, allowing them to observe similarities and differences in behavior, accuracy, and computational complexity. The QNN was trained using a gradient-based approach, as summarized in the algorithm presented in Box 3, which outlines the data flow, optimization steps, and probabilistic measurement involved in training the quantum model. Box 3 – Training a Quantum Neural Network (QNN). This algorithm summarizes the training loop for a quantum neural network using a finite-difference gradient approach. 4. Results and Discussion To evaluate the learning behavior of the quantum neural network described in the previous sections, we implemented the full training algorithm using Python and the Qiskit framework. This implementation, presented in Box 4 (see Appendix A), faithfully follows the logical structure defined in the pseudocode shown in Box 3. By maintaining this close correspondence, we ensure that students and readers can not only understand each computational step in the model but also relate it to the abstract workflow defined earlier. The mapping between algorithmic steps and implementation code is summarized in Table 2 . Each function in the script corresponds to a key stage in the QNN pipeline: data encoding, parameterized quantum transformation, measurement, loss evaluation, gradient estimation via finite differences, and weight updates. This alignment reinforces the educational value of the example, as it bridges theory and practice in a modular, traceable way. Table 2 – Correspondence Between Algorithm and Code Step in Algorithm (Box 3) Corresponding Code (Box 4) Data encoding via rotations feature_map(X) Application of variational circuit variational_circuit(qc, theta) Probabilistic measurement qc.measure(...) + backend.run(...) Loss computation calc_loss(pred, target) Gradient estimation gradient(X_i, Y_i, theta) Parameter update theta = theta - eta * gradient(...) Epoch-based training loop for epoch in range(...) The training plots in Fig. 1 display the evolution of the QNN’s performance over 30 epochs. The left panel shows the loss (mean squared error), while the right panel presents the accuracy calculated on the training set. In early epochs, the loss starts at a relatively high value (~ 0.29) and fluctuates as parameter updates are applied. A notable spike in accuracy occurs in Epoch 2 , jumping from 20.9% to over 80%. This sudden improvement can be pedagogically emphasized as a "quantum leap" that demonstrates how even small parameter changes in variational quantum circuits can drastically affect the output probabilities. Throughout the training, both metrics—loss and accuracy—exhibit non-monotonic behavior. This instability is characteristic of quantum neural networks due to: • the stochastic nature of quantum measurements, which rely on repeated sampling and introduce inherent variability; • the non-differentiable and rugged optimization landscape of parameterized quantum circuits; • the use of a finite-difference method for gradient estimation, which can amplify noise and small measurement errors. From a pedagogical standpoint, these fluctuations offer a rich opportunity for student learning. Analyzing such irregularities enables discussions on: • the difficulties of optimization in quantum systems, contrasting with the smoother convergence often seen in classical ANNs; • the role of quantum randomness and noise in shaping learning outcomes; • the importance of data visualization and exploratory analysis in understanding model behavior. Educational research underscores the value of computational and visual tools in teaching complex scientific concepts. For instance, Nyirahabimana et al. ( 2023 ) demonstrated that multimedia-aided technologies significantly improve students’ understanding of quantum phenomena by allowing them to interact with and visualize quantum behavior in real time. Similarly, Seskir et al. ( 2022 ) highlight how interactive simulations and narrative visualizations support deeper engagement and conceptual grasp in quantum computing contexts. In our study, the QNN reaches over 80% accuracy in several epochs, fluctuating due to the probabilistic and noisy nature of quantum measurements. Such behavior aligns with the epistemic uncertainty central to quantum learning, offering a valuable teaching moment for students to reflect on hybrid modeling, measurement noise, and the interpretive aspects of quantum outcomes. By guiding students to analyze training curves, educators can foster critical thinking, reinforce theoretical concepts, and connect computational outcomes with quantum theory in a meaningful, didactic experience. 5. Conclusion This study demonstrates that quantum neural networks (QNNs), despite their conceptual and technical complexity, can be taught effectively through the use of classical simulation, code-based exploration, and graphical interpretation. By building a bridge between classical artificial neural networks (ANNs) and their quantum counterparts, we provided a concrete pathway for students and educators to grasp the fundamental principles of quantum machine learning within a familiar learning framework. The step-by-step modeling process, aligned with Python implementation and visual analytics, allows learners to engage with the core mechanics of quantum computation—such as probabilistic measurement, variational circuits, and parameter optimization—without the need for real quantum hardware. From a science education perspective, this approach reinforces the value of computational thinking, algorithmic literacy, and visual reasoning in teaching advanced topics like quantum information processing. The use of the Iris dataset further grounds the experience in a well-known classification task, making the pedagogical objectives transparent and relatable. We believe this work contributes to the growing literature on quantum education by offering a reproducible, open-source, and conceptually scaffolded model for introducing quantum learning systems to students in engineering, computing, and physics. We hope that this contribution not only supports instructors in updating their curriculum to include emerging quantum technologies, but also encourages further research into hybrid educational strategies that integrate simulation, coding, and theoretical abstraction in quantum science teaching. The methodology and materials presented herein are adaptable, extensible, and open to future enhancements—including noise modeling, circuit depth exploration, and multi-class extensions. For these reasons, we believe this manuscript aligns well with the mission of the Journal of Science Education and Technology and addresses the intersection of scientific knowledge, pedagogy, and computational innovation. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of Interest The authors declare that they have no conflict of interest. Authors’ Contributions All authors contributed equally to the development, writing, and revision of the manuscript. References Nyirahabimana, P., Minani, E., Nduwingoma, M. et al.(2023). Multimedia-Aided Technologies for Effective Learning of Quantum Physics at the University Level. J Sci Educ Technol 32, 686–696 . https://doi.org/10.1007/s10956-023-10064-x Ahmet, E. (2020). Anticipating the Disruptive Innovations Brought by Quantum Computing. ISACA Journal, 1 January 2020. Retrieved from https://www.isaca.org/resources/isaca-journal/issues/2020/volume-1/ NGUYEN, P. N. Quantum technology: a financial risk assessment. Digital Finance, 2025. Available at: https://doi.org/10.1007/s42521-025-00127-6. SOUZA, D. S.; LIMA, N. W.; KARAM, R. A. S. A gênese da interpretação probabilística em livros didáticos de mecânica quântica. Revista Brasileira de Ensino de Física, v. 46, e20230238, 2024. https://doi.org/10.1590/1806-9126-RBEF-2023-0238. Nyirahabimana, P., Minani, E., Nduwingoma, M. et al. Multimedia-Aided Technologies for Effective Learning of Quantum Physics at the University Level. J Sci Educ Technol 32, 686–696 (2023). https://doi.org/10.1007/s10956-023-10064-x Seskir, Z. C., Migdał, P., Weidner, C., et al. (2022). Quantum games and interactive tools for quantum technologies outreach and education," Opt. Eng. 61(8) 081809 (1 July 2022) https://doi.org/10.1117/1.OE.61.8.081809 Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x Abraham, H., Akhalwaya, I. Y., Alexander, T., et al. (2019). Qiskit: An Open-source Framework for Quantum Computing. https://qiskit.org Silva, V. (2024). Qiskit, Awesome SDK for Quantum Programming in Python. In: Quantum Computing by Practice. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9991-3_6 Ghosh, A., Das, A., & Ghosh, R. (2024). Quantum convolutional neural network: A hybrid quantum-classical approach for Iris dataset classification. arXiv. https://arxiv.org/abs/2410.16344 Additional Declarations The authors declare no competing interests. Supplementary Files Apendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":136892,"visible":true,"origin":"","legend":"\u003cp\u003eQNN training loss and accuracy over 30 epochs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7660789/v1/c92047eca62319a57f7aba25.png"},{"id":91819587,"identity":"f8c32367-b43b-40ea-b8d2-eaf3b5b9f6fe","added_by":"auto","created_at":"2025-09-22 07:07:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":879043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7660789/v1/7cdca3c7-664a-4fce-94d1-ae2454a253ba.pdf"},{"id":91819250,"identity":"46e39946-dd98-4104-ada7-5c1d29b47779","added_by":"auto","created_at":"2025-09-22 07:06:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33426,"visible":true,"origin":"","legend":"","description":"","filename":"Apendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7660789/v1/7154c30ab9993bc5aa83784d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTeaching Quantum Neural Network (QNN) Concepts Through Artificial Neural Networks (ANNs): A Python Example Using the Iris Dataset\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTeaching the fundamental concepts of an emerging technology through clear and objective examples is essential to engage students and foster understanding of complex topics. Quantum computing represents an imminent disruption in the field of hardware, offering computational capabilities significantly beyond those of classical machines (\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNYIRAHABIMANA, 2023\u003c/span\u003e ). In this context, Quantum Neural Networks (QNNs) emerge as a new frontier in artificial intelligence algorithms, leveraging the unique features of quantum mechanics to enhance machine learning and problem-solving in high-dimensional and non-convex domains.\u003c/p\u003e\u003cp\u003eQuantum computing is finally on the path to technological disruption, especially with the announcement of novel quantum devices, which may mark a turning point toward building functional and scalable quantum computers (AHMET, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, Nguyen (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argues that quantum technology represents not only an innovation but also a potential source of systemic risk in finance, emphasizing the broad transformative impact this field may have across industries and scientific domains.\u003c/p\u003e\u003cp\u003eQuantum computing has evolved significantly since its conceptual foundations, with major breakthroughs such as Shor\u0026rsquo;s algorithm for integer factorization and Grover\u0026rsquo;s algorithm for database search. These developments demonstrate the potential of quantum systems to solve problems considered intractable by classical computation. Current research in quantum computing spans the development of stable quantum hardware, efficient algorithms, and applications in cryptography, materials simulation, and artificial intelligence. Recent literature highlights the importance of interdisciplinary approaches to overcome the technical and theoretical challenges that still persist (\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNyirahabimana\u003c/span\u003e, 2023).\u003c/p\u003e\u003cp\u003eParallel to technological advances, the teaching of quantum mechanics faces ongoing challenges due to its abstract concepts and difficulties in contextualizing them for learners. Studies suggest that innovative didactic strategies, such as analogies, simulations, and interdisciplinary approaches, may help students comprehend quantum principles. For instance, the integration of literature and art into science instruction has been explored as a strategy to make abstract ideas more relatable and to stimulate student interest in modern physics (SOUZA, 2024). The Journal of Science Education and Technology has published numerous studies proposing pedagogical methods to enhance the teaching of quantum mechanics. These works emphasize the need to rethink teaching practices, promoting more meaningful learning experiences connected to students\u0026rsquo; prior knowledge and lived experiences. The incorporation of digital technologies and multimedia resources is also highlighted as a means of enriching learning environments and bridging the gap between abstract quantum concepts and students\u0026rsquo; understanding (\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNyirahabimana\u003c/span\u003e, 2023).\u003c/p\u003e\u003cp\u003eIn light of these technological and educational trends, it becomes essential to bridge the gap between traditional artificial intelligence education and the emerging principles of quantum computing. Given that many students are already familiar with classical neural networks (ANNs), this article proposes a didactic approach to introduce Quantum Neural Networks (QNNs) through a practical example implemented in Python using the Qiskit framework and the well-known Iris dataset. This strategy aims to ease the conceptual transition from ANNs to QNNs by exploring their structural analogies and highlighting the distinctive features of quantum processing in an accessible and reproducible learning environment.\u003c/p\u003e"},{"header":"2. Theoretical Foundations","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Artificial Neural Networks (ANNs)\u003c/h2\u003e\n \u003cp\u003eArtificial Neural Networks (ANNs) are computational models inspired by the behavior of biological neurons. A fundamental component of an ANN is the artificial neuron, which performs a weighted sum of its inputs followed by a nonlinear activation function that defines its output.\u003c/p\u003e\n \u003cp\u003eThe mathematical model of a classical neuron is described by the Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:y=F\\left({\\sum\\:}_{i=1}^{N}{w}_{i}{x}_{i}+b\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eOnde:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ex\u003csub\u003ei\u003c/sub\u003e are the input values, typically real numbers representing features of the input data (e.g., petal length in the Iris dataset).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ew\u003csub\u003ei\u003c/sub\u003e are the weights associated with each input. They determine the importance of each feature in the computation. Larger weights assign more significance to the corresponding inputs.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eb is the bias term, added to the weighted sum to allow shifting the activation function and improving the model\u0026rsquo;s flexibility.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{N}{w}_{i}{x}_{i}+b\\)\u003c/span\u003e\u003c/span\u003e is the weighted sum that represents the local induced field of the neuron and determines how \u0026ldquo;activated\u0026rdquo; it will be.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eF(.) is the activation function, a nonlinear transformation such as the sigmoid, ReLU, or tanh. It introduces non-linearity, enabling the network to model complex patterns.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ey is the final output of the neuron, passed either to other neurons in deeper layers or used as the model\u0026rsquo;s prediction.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe implementation of a classical neuron is illustrated in the Python code in Box 1, which serves as a didactic tool to reinforce the mathematical formulation through computational practice. By translating the neuron model into executable code, students can directly observe how weighted sums and activation functions operate, thus strengthening their conceptual understanding through hands-on experimentation.\u003c/p\u003e\n \u003cp\u003eBox 1 \u0026ndash; Python implementation of a classical neuron. This code demonstrates the computation of a simple artificial neuron using NumPy and a sigmoid activation function.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"670\" height=\"466\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Quantum Neural Networks (QNNs)\u003c/h2\u003e\n \u003cp\u003eQuantum Neural Networks (QNNs) are machine learning models that integrate the principles of quantum computing. Unlike classical neurons that use real-valued inputs and weights, QNNs operate on qubits\u0026mdash;quantum information units that can exist in superposition and exhibit entanglement.\u003c/p\u003e\n \u003cp\u003eA QNN typically involves three stages:\u003c/p\u003e\u003cspan\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eEncoding of classical data into quantum states (feature map)\u003c/strong\u003e;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eApplication of a variational quantum circuit\u003c/strong\u003e, composed of parameterized quantum gates such as RX, RY, and CNOT;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e, collapsing the quantum state to obtain classical outputs.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/span\u003e\n \u003cp\u003eThe quantum neuron does not rely on a weighted sum but instead applies a unitary transformation to the quantum state. Its behavior is expressed by Eq.\u0026nbsp;(2):\u003c/p\u003e\n \u003cp\u003e|\u0026psi;\u0026rang; = U(\u0026theta;)|ϕ(x)\u0026rang; (2)\u003c/p\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e|ϕ(x)\u0026rang; is the quantum state representing the encoded classical input.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eU(\u0026theta;) is a parameterized unitary transformation (the quantum circuit), where \u0026theta; are trainable parameters analogous to weights in classical neural networks.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e|\u0026psi;\u0026rang; is the final state of the system after processing.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe output is obtained via \u003cstrong\u003equantum measurement\u003c/strong\u003e, typically reported as the probability of observing a particular state (e.g., ∣1\u0026rang;), which serves as the equivalent of the neuron\u0026rsquo;s activation.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eTo enable practical experimentation without requiring access to real quantum hardware, we use Qiskit Aer, a high-performance simulator designed to emulate quantum circuits under various configurations. Aer supports multiple simulation backends, including ideal noiseless environments (\u003cem\u003estatevector\u003c/em\u003e) as well as realistic noisy models, making it suitable for both conceptual demonstrations and more advanced studies involving decoherence and gate errors (Silva, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The AerSimulator backend allows the execution of quantum circuits over thousands of repeated trials (\u003cem\u003eshots\u003c/em\u003e), providing students with an intuitive understanding of probabilistic measurement outcomes and the role of quantum noise. Moreover, Aer is fully operable on local machines, which facilitates its integration into academic settings with limited computational resources. Its flexible architecture, including support for \u003cem\u003estatevector\u003c/em\u003e, \u003cem\u003edensity matrix\u003c/em\u003e, and GPU-accelerated modes, enables educators to tailor simulations to different instructional levels (Abraham et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo complement the theoretical formulation presented above, Box 2 provides a simple Python implementation of a quantum neuron using Qiskit. This example serves as a computational parallel to the classical neuron presented in Box 1 and allows students to explore the quantum encoding, variational processing, and probabilistic output of a basic QNN in practice.\u003c/p\u003e\n \u003cp\u003eBox 2 \u0026ndash; Python implementation of a quantum neuron.This code illustrates a simple quantum neuron built with Qiskit. The feature map encodes classical data via RX rotations; the variational circuit applies parameterized RY rotations and entanglement; measurement extracts the probability output.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"670\" height=\"850\"\u003e\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the main differences between a classical neuron and a quantum neuron, considering their fundamental operations, mathematical representations, and implementation characteristics.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026ndash; Comparison between the main characteristics of classical and quantum neurons.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassical Neuron (ANN)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuantum Neuron (QNN)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInput data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal-valued vectors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQubits (superposition of 0 and 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcessing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted sum\u0026thinsp;+\u0026thinsp;activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnitary transformation\u0026thinsp;+\u0026thinsp;measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrainable parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeights and bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRotation angles of quantum gates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutput\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous value after activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability of measuring a specific quantum state\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClassical matrix operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuantum circuits and unitary gates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Methodology and Implementation","content":"\u003cp\u003eThis study adopts a didactic and comparative methodology, in which the implementation of a Quantum Neural Network (QNN) is developed side-by-side with a Classical Artificial Neural Network (ANN) using Python. The aim is to facilitate the conceptual transition between classical and quantum paradigms for students already familiar with basic machine learning models. The methodology follows four pedagogical and computational stages:\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Dataset and Problem Framing\u003c/h2\u003e\u003cp\u003eTo ensure familiarity and replicability, we use the Iris dataset, a classical benchmark introduced by Fisher in 1936, with 150 instances and four numeric features. Its pedagogical prominence comes from:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSimplicity with educational value: small, real, and clean dataset, yet non-trivial for classification tasks (Ghosh et al, 2024)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHistorical significance: connects students to the origins of statistical learning and machine learning. (Fisher, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1936\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEducator consensus: widely used in curricula to teach feature selection, model comparison, and linear separability concepts (Goswami and Chatterjee 2020)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConstructivist advantages: enhances students' conceptual understanding, as observed in AI education research. (Chen et al, 2024)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFor simplicity and to conform to QNN\u0026rsquo;s binary output, we restrict the task to two classes only (Iris setosa vs. Iris versicolor), ensuring clarity and reducing computational overhead. A classic benchmark in supervised learning, readily available in the scikit-learn library. For simplicity and to ensure binary classification\u0026mdash;suitable for one-qubit quantum circuits\u0026mdash;only the first two classes (Setosa and Versicolor) are selected, totaling 100 data points with four numerical features each.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Classical Neuron as Foundation\u003c/h2\u003e\u003cp\u003eAs described in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e, a classical artificial neuron is implemented using a weighted sum and a sigmoid activation function. This implementation is included in Box 1 and serves as the conceptual and computational foundation upon which the quantum neuron is later built. Students are encouraged to run and modify the ANN code first to reinforce their understanding of key concepts such as weight adjustment, bias, and nonlinear activation.\u003c/p\u003e\u003cp\u003eFrom a pedagogical standpoint, introducing the classical model first allows students to activate prior knowledge and provides a structured scaffold for learning more abstract quantum concepts. As supported by educational research in computational thinking and analogical reasoning, building upon familiar models enhances cognitive transfer and improves retention when dealing with complex or novel content (Grover \u0026amp; Pea, 2013). In this context, the classical neuron acts as a cognitive anchor: by seeing how data are transformed, parameters are adjusted, and outputs are produced in a known environment, learners are better equipped to make sense of similar processes in the quantum domain, such as gate parametrization, feature encoding, and probabilistic measurement.\u003c/p\u003e\u003cp\u003eThis methodological progression\u0026mdash;from classical to quantum\u0026mdash;is not only conceptually coherent but also aligns with best practices in STEM education, where layered abstraction and practical experimentation play a central role in student engagement and understanding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Quantum Neural Network Construction\u003c/h2\u003e\u003cp\u003eThe Quantum Neural Network (QNN) is implemented using the Qiskit framework and executed on the Aer simulator, avoiding dependence on physical quantum hardware. The QNN is executed on the Aer simulator, a high-performance simulation backend provided by Qiskit. This simulator emulates quantum circuits classically, enabling educators and students to explore quantum algorithms without requiring access to real quantum hardware. By simulating ideal quantum gates and measurements, Aer provides a stable and reproducible environment for teaching foundational quantum computing concepts. The implementation is designed not only to be functional but also to serve as a didactic instrument that complements students' understanding of neural networks from a quantum computing perspective.\u003c/p\u003e\u003cp\u003eThe process involves three main stages:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFeature Mapping: Each of the four input features is encoded into the quantum circuit via RX rotations, transforming classical data into quantum states.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eVariational Circuit: A sequence of entangling operations (CNOT gates) and parameterized RY rotations is applied. The RY gates are adjusted by a vector of parameters θ\\thetaθ, which play a role analogous to trainable weights in classical neural networks.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMeasurement: One qubit (typically qubit 0) is measured repeatedly\u0026mdash;over 10,000 shots\u0026mdash;and the probability of observing the state ∣1⟩|1⟩∣1⟩ is interpreted as the output of the quantum neuron.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo reinforce conceptual understanding, Box 2 presents the complete Python implementation of a quantum neuron, written with didactic clarity. Each line is commented to help students draw parallels with the classical neuron from Box 1, and to observe how encoding, transformation, and measurement are used to emulate the functional behavior of a neuron using quantum operations.\u003c/p\u003e\u003cp\u003eFrom a pedagogical perspective, this structured implementation enables students to manipulate quantum circuits in a hands-on environment, identify the role of each quantum gate in the model\u0026rsquo;s logic, and relate abstract quantum operations to familiar neural network mechanisms.\u003c/p\u003e\u003cp\u003eThe complete QNN pipeline mimics the behavior of an ANN by adjusting the parameters θ through a finite difference gradient estimation method. Since quantum circuits are not differentiable in the classical sense, this approximation allows the training loop to follow a similar pattern to that of gradient descent in traditional machine learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Training and Evaluation\u003c/h2\u003e\u003cp\u003eThe model is trained over 30 epochs. For each training example, the output probability of the QNN is compared to the target label (0 or 1), and the loss is calculated as the squared error. The gradient is computed numerically and used to update the parameters. A simple loop-based training scheme is provided to enhance transparency for students, rather than using an opaque optimization library.\u003c/p\u003e\u003cp\u003eAt the end of training, the model\u0026rsquo;s accuracy is evaluated on the test set using a thresholding approach (e.g., output\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026rArr; class 0; output\u0026thinsp;\u0026ge;\u0026thinsp;0.5 \u0026rArr; class 1). Students are encouraged to compare the QNN\u0026rsquo;s performance with the ANN under the same input and structure constraints, allowing them to observe similarities and differences in behavior, accuracy, and computational complexity.\u003c/p\u003e\u003cp\u003eThe QNN was trained using a gradient-based approach, as summarized in the algorithm presented in Box 3, which outlines the data flow, optimization steps, and probabilistic measurement involved in training the quantum model.\u003c/p\u003e\u003cp\u003eBox 3 \u0026ndash; Training a Quantum Neural Network (QNN). This algorithm summarizes the training loop for a quantum neural network using a finite-difference gradient approach.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"746\" height=\"643\"\u003e\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eTo evaluate the learning behavior of the quantum neural network described in the previous sections, we implemented the full training algorithm using Python and the Qiskit framework. This implementation, presented in Box 4 (see Appendix A), faithfully follows the logical structure defined in the pseudocode shown in Box 3. By maintaining this close correspondence, we ensure that students and readers can not only understand each computational step in the model but also relate it to the abstract workflow defined earlier.\u003c/p\u003e\u003cp\u003eThe mapping between algorithmic steps and implementation code is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each function in the script corresponds to a key stage in the QNN pipeline: data encoding, parameterized quantum transformation, measurement, loss evaluation, gradient estimation via finite differences, and weight updates. This alignment reinforces the educational value of the example, as it bridges theory and practice in a modular, traceable way.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Correspondence Between Algorithm and Code\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep in Algorithm (Box 3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorresponding Code (Box 4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData encoding via rotations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efeature_map(X)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApplication of variational circuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evariational_circuit(qc, theta)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbabilistic measurement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eqc.measure(...)\u0026thinsp;+\u0026thinsp;backend.run(...)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoss computation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecalc_loss(pred, target)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGradient estimation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egradient(X_i, Y_i, theta)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter update\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etheta\u0026thinsp;=\u0026thinsp;theta - eta * gradient(...)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpoch-based training loop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efor epoch in range(...)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe training plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e display the evolution of the QNN\u0026rsquo;s performance over 30 epochs. The left panel shows the loss (mean squared error), while the right panel presents the accuracy calculated on the training set.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn early epochs, the loss starts at a relatively high value (~\u0026thinsp;0.29) and fluctuates as parameter updates are applied. A notable spike in accuracy occurs in \u003cb\u003eEpoch 2\u003c/b\u003e, jumping from 20.9% to over 80%. This sudden improvement can be pedagogically emphasized as a \"quantum leap\" that demonstrates how even small parameter changes in variational quantum circuits can drastically affect the output probabilities.\u003c/p\u003e\u003cp\u003eThroughout the training, both metrics\u0026mdash;loss and accuracy\u0026mdash;exhibit non-monotonic behavior. This instability is characteristic of quantum neural networks due to:\u003c/p\u003e\u003cp\u003e\u0026bull; the stochastic nature of quantum measurements, which rely on repeated sampling and introduce inherent variability;\u003c/p\u003e\u003cp\u003e\u0026bull; the non-differentiable and rugged optimization landscape of parameterized quantum circuits;\u003c/p\u003e\u003cp\u003e\u0026bull; the use of a finite-difference method for gradient estimation, which can amplify noise and small measurement errors.\u003c/p\u003e\u003cp\u003eFrom a pedagogical standpoint, these fluctuations offer a rich opportunity for student learning. Analyzing such irregularities enables discussions on:\u003c/p\u003e\u003cp\u003e\u0026bull; the difficulties of optimization in quantum systems, contrasting with the smoother convergence often seen in classical ANNs;\u003c/p\u003e\u003cp\u003e\u0026bull; the role of quantum randomness and noise in shaping learning outcomes;\u003c/p\u003e\u003cp\u003e\u0026bull; the importance of data visualization and exploratory analysis in understanding model behavior.\u003c/p\u003e\u003cp\u003eEducational research underscores the value of computational and visual tools in teaching complex scientific concepts. For instance, Nyirahabimana et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that multimedia-aided technologies significantly improve students\u0026rsquo; understanding of quantum phenomena by allowing them to interact with and visualize quantum behavior in real time. Similarly, Seskir et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlight how interactive simulations and narrative visualizations support deeper engagement and conceptual grasp in quantum computing contexts.\u003c/p\u003e\u003cp\u003eIn our study, the QNN reaches over 80% accuracy in several epochs, fluctuating due to the probabilistic and noisy nature of quantum measurements. Such behavior aligns with the epistemic uncertainty central to quantum learning, offering a valuable teaching moment for students to reflect on hybrid modeling, measurement noise, and the interpretive aspects of quantum outcomes. By guiding students to analyze training curves, educators can foster critical thinking, reinforce theoretical concepts, and connect computational outcomes with quantum theory in a meaningful, didactic experience.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that quantum neural networks (QNNs), despite their conceptual and technical complexity, can be taught effectively through the use of classical simulation, code-based exploration, and graphical interpretation. By building a bridge between classical artificial neural networks (ANNs) and their quantum counterparts, we provided a concrete pathway for students and educators to grasp the fundamental principles of quantum machine learning within a familiar learning framework. The step-by-step modeling process, aligned with Python implementation and visual analytics, allows learners to engage with the core mechanics of quantum computation\u0026mdash;such as probabilistic measurement, variational circuits, and parameter optimization\u0026mdash;without the need for real quantum hardware.\u003c/p\u003e\u003cp\u003eFrom a science education perspective, this approach reinforces the value of computational thinking, algorithmic literacy, and visual reasoning in teaching advanced topics like quantum information processing. The use of the Iris dataset further grounds the experience in a well-known classification task, making the pedagogical objectives transparent and relatable. We believe this work contributes to the growing literature on quantum education by offering a reproducible, open-source, and conceptually scaffolded model for introducing quantum learning systems to students in engineering, computing, and physics.\u003c/p\u003e\u003cp\u003eWe hope that this contribution not only supports instructors in updating their curriculum to include emerging quantum technologies, but also encourages further research into hybrid educational strategies that integrate simulation, coding, and theoretical abstraction in quantum science teaching. The methodology and materials presented herein are adaptable, extensible, and open to future enhancements\u0026mdash;including noise modeling, circuit depth exploration, and multi-class extensions. For these reasons, we believe this manuscript aligns well with the mission of the \u003cem\u003eJournal of Science Education and Technology\u003c/em\u003e and addresses the intersection of scientific knowledge, pedagogy, and computational innovation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the development, writing, and revision of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNyirahabimana, P., Minani, E., Nduwingoma, M. et al.(2023). Multimedia-Aided Technologies for Effective Learning of Quantum Physics at the University Level. J Sci Educ Technol 32, 686\u0026ndash;696 . https://doi.org/10.1007/s10956-023-10064-x\u003c/li\u003e\n\u003cli\u003eAhmet, E. (2020). Anticipating the Disruptive Innovations Brought by Quantum Computing. ISACA Journal, 1 January 2020. Retrieved from https://www.isaca.org/resources/isaca-journal/issues/2020/volume-1/\u003c/li\u003e\n\u003cli\u003eNGUYEN, P. N. Quantum technology: a financial risk assessment. Digital Finance, 2025. Available at: https://doi.org/10.1007/s42521-025-00127-6.\u003c/li\u003e\n\u003cli\u003eSOUZA, D. S.; LIMA, N. W.; KARAM, R. A. S. A g\u0026ecirc;nese da interpreta\u0026ccedil;\u0026atilde;o probabil\u0026iacute;stica em livros did\u0026aacute;ticos de mec\u0026acirc;nica qu\u0026acirc;ntica. Revista Brasileira de Ensino de F\u0026iacute;sica, v. 46, e20230238, 2024. https://doi.org/10.1590/1806-9126-RBEF-2023-0238.\u003c/li\u003e\n\u003cli\u003eNyirahabimana, P., Minani, E., Nduwingoma, M. et al. Multimedia-Aided Technologies for Effective Learning of Quantum Physics at the University Level. J Sci Educ Technol 32, 686\u0026ndash;696 (2023). https://doi.org/10.1007/s10956-023-10064-x\u003c/li\u003e\n\u003cli\u003eSeskir, Z. C., Migdał, P., Weidner, C., et al. (2022). Quantum games and interactive tools for quantum technologies outreach and education,\u0026quot; Opt. Eng. 61(8) 081809 (1 July 2022) https://doi.org/10.1117/1.OE.61.8.081809\u003c/li\u003e\n\u003cli\u003eFisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179\u0026ndash;188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x\u003c/li\u003e\n\u003cli\u003eAbraham, H., Akhalwaya, I. Y., Alexander, T., et al. (2019). Qiskit: An Open-source Framework for Quantum Computing. https://qiskit.org\u003c/li\u003e\n\u003cli\u003eSilva, V. (2024). Qiskit, Awesome SDK for Quantum Programming in Python. In: Quantum Computing by Practice. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9991-3_6\u003c/li\u003e\n\u003cli\u003eGhosh, A., Das, A., \u0026amp; Ghosh, R. (2024). Quantum convolutional neural network: A hybrid quantum-classical approach for Iris dataset classification. arXiv. https://arxiv.org/abs/2410.16344\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade de São Paulo","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quantum Neural Networks, Science Education, Computational Thinking, Python Programming, Quantum Simulation, Machine Learning Education","lastPublishedDoi":"10.21203/rs.3.rs-7660789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7660789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantum 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.\u003c/p\u003e","manuscriptTitle":"Teaching Quantum Neural Network (QNN) Concepts Through Artificial Neural Networks (ANNs): A Python Example Using the Iris Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 07:00:30","doi":"10.21203/rs.3.rs-7660789/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc64c99d-4cbb-417c-bf29-7dc2a16c681f","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55032742,"name":"Special Education"}],"tags":[],"updatedAt":"2025-09-22T07:00:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 07:00:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7660789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7660789","identity":"rs-7660789","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}