Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification

S. M. Yousuf Iqbal Tomal, Abdullah Al Shafin, Afrida Afaf, Debojit Bhattacharjee

Abstract

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.

Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification

Abstract

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.

Paper Structure

This paper contains 25 sections, 8 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Quantum Circuit Design and Data Encoding
  4. Quantum Feature Encoding
  5. Angle Embedding in Quantum Machine Learning
  6. Mathematical Formulation of Angle Embedding
  7. AngleEmbedding Method
  8. Benefits of Angle Embedding
  9. Integration with Quantum Circuit
  10. Quantum Circuit Architecture
  11. Design Rationale and Considerations
  12. Hybrid Model Architecture
  13. Quantum Layer
  14. Classical Neural Network Layer
  15. Quantum-Classical Hybrid Integration
  16. ...and 10 more sections

Figures (2)

  • Figure 1: Quantum Circuit Diagram: 4 Qubits, 2 Layers of Rotational Gates and Entanglement
  • Figure 2: Hybrid Quantum-Classical Model Architecture