Quantum Circuit Error Classification

A machine learning system for detecting and classifying quantum circuit errors using hardware-inspired quantum metrics and predictive modeling.

---

Overview

Quantum computers are highly sensitive to decoherence, gate imperfections, and environmental noise. This project explores how classical machine learning can be used to classify quantum circuit errors through structured hardware-inspired datasets.

The repository demonstrates a complete machine learning workflow:

• Exploratory Data Analysis
• Data preprocessing
• Feature engineering
• Model training
• Hyperparameter optimization
• Cross-validation
• Performance evaluation

---

Architecture

flowchart LR
    A[Quantum Circuit Dataset] --> B[Preprocessing]
    B --> C[Feature Engineering]
    C --> D[Model Training]
    D --> E[Hyperparameter Tuning]
    E --> F[Evaluation]
    F --> G[Error Classification]

Loading

---

Dataset Features

Feature	Description
Gate Fidelity	Accuracy of quantum gate operations
Circuit Depth	Complexity of the quantum circuit
T1 Coherence	Energy relaxation time
T2 Coherence	Phase decoherence time
Readout Error	Measurement instability
Error Type	Target classification label

---

Models Used

Model	Purpose
Logistic Regression	Baseline classification
Random Forest Classifier	Main predictive model
GridSearchCV	Hyperparameter optimization
K-Fold Cross Validation	Model validation

---

Tech Stack

Python
Scikit-learn
Pandas
NumPy
Matplotlib
Seaborn
Jupyter Notebook

---

Repository Structure

Quantum-Circuit-Error-Classification/
│
├── quantum-circuit-error-classification.ipynb
├── quantum_circuit_errors.csv
├── README.md
└── LICENSE

---

Installation

Clone Repository

git clone https://github.com/edwingeorgeshaji/Quantum-Circuit-Error-Classification.git
cd Quantum-Circuit-Error-Classification

Install Dependencies

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

Run Notebook

jupyter notebook

---

Key Features

• Quantum circuit error classification
• Hardware-inspired feature analysis
• Correlation heatmaps and visualizations
• Feature importance analysis
• Cross-validation evaluation
• Hyperparameter optimization

---

Future Scope

• IBM Quantum hardware integration
• Real-time quantum monitoring
• Deep learning-based classification
• Interactive visualization dashboard
• Advanced ensemble learning methods

---

Author

Edwin George Shaji

Computer Science Engineering Student focused on Quantum Computing, Artificial Intelligence, and Machine Learning.

GitHub:

https://github.com/edwingeorgeshaji

LinkedIn:

https://www.linkedin.com/in/edwingeorgeshaji

---