Added quantum circuit probability predictor model

[Panchadip-128]

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.

The core functionalities include:

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)

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