https://github.com/pedroteixeiraw/variational_quantum_circuit_binary_classification

This project focuses on developing a Variational Quantum Circuit capable of performing Binary Classification between two classes: red wine and white wine, based on their characteristics using machine learning.
https://github.com/pedroteixeiraw/variational_quantum_circuit_binary_classification

binary-classification cost-function json machine-learning matplotlib numpy pandas qiskit qiskit-machine-learning quantum-machine-learning scikit-learn training-data variational-circuit

Last synced: 2 months ago
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This project focuses on developing a Variational Quantum Circuit capable of performing Binary Classification between two classes: red wine and white wine, based on their characteristics using machine learning.

• Host: GitHub
• URL: https://github.com/pedroteixeiraw/variational_quantum_circuit_binary_classification
• Owner: pedroteixeiraw
• License: mit
• Created: 2025-03-19T10:31:21.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-21T17:14:42.000Z (about 1 year ago)
• Last Synced: 2025-03-21T18:25:07.267Z (about 1 year ago)
• Topics: binary-classification, cost-function, json, machine-learning, matplotlib, numpy, pandas, qiskit, qiskit-machine-learning, quantum-machine-learning, scikit-learn, training-data, variational-circuit
• Language: Jupyter Notebook
• Homepage:
• Size: 1.21 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Variational Quantum Circuit for Binary Classification  

![System Schema](variational_circtuit_system.png)

This project focuses on developing a **Variational Quantum Circuit** capable of performing **Binary Classification** between two classes: **red wine** and **white wine**, based on their characteristics using machine learning. Our system consists of both quantum and classical components.  

- **Quantum Component (Quantum Circuit):**  

  A block responsible for encoding/embedding, a block responsible for the variational algorithm, and measurement blocks.  

- **Classical Component:**  

  Post-processing (associating measurements with labels), loss function computation, and optimization of variational parameters (θ). In some cases, special classical preprocessing may also be necessary, such as dimensionality reduction of the dataset using PCA techniques.  

**Python Stack:**  qiskit, json, numpy, pandas, sklearn, itertools, matplotlib, qiskit_machine_learning, sys