# Kala_Quantum
## Author **N V R K SAI KAMESH SHARMA YADAVALLI**
# Quantum AI Framework with KalaAI Integration
## Overview
The Quantum AI Framework provides tools for quantum state manipulation, gate applications, and optimization while integrating classical neural network capabilities. This repository includes a quantum-powered chatbot framework, `KalaAI`, designed for advanced conversational capabilities leveraging quantum machine learning concepts.
---
## Features
### Quantum Module
- **Quantum Gates**: Provides standard quantum gates (e.g., Pauli-X, Hadamard, RX, RY, RZ) and multi-qubit tensor gate construction.
- **Quantum State**: Enables state vector manipulation, normalization, measurement, and serialization.
- **Quantum Circuit**: Simplifies multi-qubit quantum operations and measurements.
- **Quantum Trainer**: Trains quantum systems to approximate target quantum states.
- **Quantum Optimizer**: Optimizes quantum circuits to minimize loss functions.
### KalaAI Module
- **Customizable Neural Network**: Implements a flexible neural network using PyTorch.
- **Training Integration**: Combines classical neural network training with quantum state alignment.
---
## Installation
```bash
pip install Kala_Quantum
```
---
## Usage
### Quantum Module
#### Quantum Gates
```python
from Kala_Quantum import QuantumGates
# Apply a Pauli-X gate to a single qubit in a 3-qubit system
multi_qubit_gate = QuantumGates.tensor_gate(QuantumGates.X, num_qubits=3, target_qubit=1)
```
#### Quantum State
```python
from Kala_Quantum import QuantumState
# Initialize a quantum state
state = QuantumState([1, 0, 0, 0])
state.apply_gate(QuantumGates.H) # Apply a Hadamard gate
measurement = state.measure() # Measure the state
```
#### Quantum Circuit
```python
from Kala_Quantum import QuantumCircuit
# Create a quantum circuit with 2 qubits
circuit = QuantumCircuit(num_qubits=2)
circuit.apply_gate(QuantumGates.H, qubit=0) # Apply Hadamard to qubit 0
result = circuit.measure() # Measure the circuit
```
#### Quantum Trainer
```python
from Kala_Quantum import QuantumTrainer, QuantumState
# Define initial and target quantum states
initial_state = QuantumState([1, 0])
target_state = [0, 1]
trainer = QuantumTrainer(initial_state, training_data=None)
trainer.train(target_state, epochs=100, learning_rate=0.1)
```
### KalaAI Module
#### Neural Network Training
```python
from Kala_Quantum import initialize_kala_ai
# Define and initialize KalaAI model
model = initialize_kala_ai(input_size=10, hidden_size=20, output_size=1)
```
#### Quantum-Enhanced Training
```python
from torch.utils.data import DataLoader
from Kala_Quantum import train_kala_ai, QuantumTrainer
# Prepare data and quantum trainer
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
quantum_trainer = QuantumTrainer(quantum_state, training_data=None)
train_kala_ai(model, quantum_trainer, target_state, dataloader, epochs=10, learning_rate=0.001)
```
---
## Dependencies
- `numpy`
- `torch`
- `tqdm`
- `termcolor`
- `pickle`
---
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
---
## Contact
For any inquiries, please contact [saikamesh.y@gmail.com].
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"description": "# Kala_Quantum\n## Author **N V R K SAI KAMESH SHARMA YADAVALLI**\n# Quantum AI Framework with KalaAI Integration\n\n## Overview\nThe Quantum AI Framework provides tools for quantum state manipulation, gate applications, and optimization while integrating classical neural network capabilities. This repository includes a quantum-powered chatbot framework, `KalaAI`, designed for advanced conversational capabilities leveraging quantum machine learning concepts.\n\n---\n\n## Features\n\n### Quantum Module\n- **Quantum Gates**: Provides standard quantum gates (e.g., Pauli-X, Hadamard, RX, RY, RZ) and multi-qubit tensor gate construction.\n- **Quantum State**: Enables state vector manipulation, normalization, measurement, and serialization.\n- **Quantum Circuit**: Simplifies multi-qubit quantum operations and measurements.\n- **Quantum Trainer**: Trains quantum systems to approximate target quantum states.\n- **Quantum Optimizer**: Optimizes quantum circuits to minimize loss functions.\n\n### KalaAI Module\n- **Customizable Neural Network**: Implements a flexible neural network using PyTorch.\n- **Training Integration**: Combines classical neural network training with quantum state alignment.\n\n---\n\n## Installation\n```bash\n pip install Kala_Quantum\n\n```\n---\n\n## Usage\n\n### Quantum Module\n\n#### Quantum Gates\n```python\nfrom Kala_Quantum import QuantumGates\n\n# Apply a Pauli-X gate to a single qubit in a 3-qubit system\nmulti_qubit_gate = QuantumGates.tensor_gate(QuantumGates.X, num_qubits=3, target_qubit=1)\n```\n\n#### Quantum State\n```python\nfrom Kala_Quantum import QuantumState\n\n# Initialize a quantum state\nstate = QuantumState([1, 0, 0, 0])\nstate.apply_gate(QuantumGates.H) # Apply a Hadamard gate\nmeasurement = state.measure() # Measure the state\n```\n\n#### Quantum Circuit\n```python\nfrom Kala_Quantum import QuantumCircuit\n\n# Create a quantum circuit with 2 qubits\ncircuit = QuantumCircuit(num_qubits=2)\ncircuit.apply_gate(QuantumGates.H, qubit=0) # Apply Hadamard to qubit 0\nresult = circuit.measure() # Measure the circuit\n```\n\n#### Quantum Trainer\n```python\nfrom Kala_Quantum import QuantumTrainer, QuantumState\n\n# Define initial and target quantum states\ninitial_state = QuantumState([1, 0])\ntarget_state = [0, 1]\n\ntrainer = QuantumTrainer(initial_state, training_data=None)\ntrainer.train(target_state, epochs=100, learning_rate=0.1)\n```\n\n### KalaAI Module\n\n#### Neural Network Training\n```python\nfrom Kala_Quantum import initialize_kala_ai\n\n# Define and initialize KalaAI model\nmodel = initialize_kala_ai(input_size=10, hidden_size=20, output_size=1)\n```\n\n#### Quantum-Enhanced Training\n```python\nfrom torch.utils.data import DataLoader\nfrom Kala_Quantum import train_kala_ai, QuantumTrainer\n\n# Prepare data and quantum trainer\ndataloader = DataLoader(dataset, batch_size=32, shuffle=True)\nquantum_trainer = QuantumTrainer(quantum_state, training_data=None)\n\ntrain_kala_ai(model, quantum_trainer, target_state, dataloader, epochs=10, learning_rate=0.001)\n```\n\n---\n\n## Dependencies\n- `numpy`\n- `torch`\n- `tqdm`\n- `termcolor`\n- `pickle`\n\n---\n\n## License\nThis project is licensed under the MIT License. See the `LICENSE` file for more details.\n\n---\n\n## Contact\nFor any inquiries, please contact [saikamesh.y@gmail.com].\n\n",
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