Added quantum circuit probability predictor

Prediction Models/Quantum Circuit Probability prediction using advanced Machine Learning techniques/ML Quantum Prediction tool

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Prediction Models/Quantum Circuit Probability prediction using advanced Machine Learning techniques/README.md

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+# Quantum-Circuit-Probability-Prediction-using-ML
+
+The Quantum Circuit Probability Predictor is a machine learning-based application designed to predict the probability of measuring a specific quantum state after applying a series of quantum gates to a qubit. Leveraging the principles of quantum mechanics and classical machine learning, this project aims to create a robust model that accurately estimates the probabilities associated with different quantum states resulting from varied input parameters.
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+The core functionalities include:
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+Quantum Circuit Simulation: 
+-----------------------------
+Utilizing Qiskit's advanced quantum simulation capabilities, the project creates quantum circuits that implement rotations around the X-axis based on user-defined angles.
+
+State Probability Calculation:
+-------------------------------
+The application computes the probabilities of measuring the |0⟩ and |1⟩ states for various angles, using statevector sampling to retrieve the state vector of the quantum circuit after the operations are performed.
+
+Model Training: 
+----------------
+A machine learning model is trained on the computed probabilities to predict outcomes for angles not seen during training, enabling the model to generalize well to new inputs.
+
+Interactive Visualization: 
+--------------------------
+The project features an intuitive interface that allows users to input angles and visualize the resulting probabilities and model predictions, enhancing the understanding of quantum state dynamics.
+
+Educational Tool:
+-----------------
+This project serves as an educational resource for students and enthusiasts interested in quantum computing and machine learning, demonstrating the intersection of these fields through hands-on experience.
+
+Technologies Used:
+------------------
+Quantum Computing Framework: Qiskit
+Machine Learning: Python, NumPy, and relevant ML libraries (e.g., scikit-learn, TensorFlow, or PyTorch)
+Data Visualization: Matplotlib or similar libraries for plotting probabilities and predictions
+User Interface: Streamlit or Flask for creating a web application interface (to be deployed soon after making model more optimized)
+
+Sample Output of predicted probability 
+![1000034342](https://github.com/user-attachments/assets/bc3fc538-ef41-47bb-a685-a2ff63d942cc)
+![1000034344](https://github.com/user-attachments/assets/dc0d666e-8417-4c9c-88eb-2ae33b85ccc0)

Prediction Models/Quantum Circuit Probability prediction using advanced Machine Learning techniques/gitignore.txt

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+__pycache__/
+*.py[cod]
+*$py.class
+env/
+venv/
+.venv/
+.Python
+env.bak/
+.ipynb_checkpoints
+.DS_Store
+Thumbs.db
+ehthumbs.db
+*.swp
+*.swo
+*.swn
+*.bak
+*.log
+*.tmp
+.vscode/
+.pytest_cache/
+*.cfg
+*.config

Prediction Models/Quantum Circuit Probability prediction using advanced Machine Learning techniques/requirements.py

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+qiskit==0.43.1
+numpy==1.24.3
+matplotlib==3.7.1

Prediction Models/Quantum Circuit Probability prediction using advanced Machine Learning techniques/src/Model Output/Images_output

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