testing upgrades

Author: redx94

ai_engine/quantum_neural_network.py

@@ -1,7 +1,7 @@
 from typing import List, Optional, Tuple
 import torch
 import torch.nn as nn
-from ..q_fabric import QuantumBackend, BackendType
+from q_fabric import QuantumBackend, BackendType
 
 class QuantumLayer(nn.Module):
     def __init__(self, n_qubits: int, n_layers: int):

core/circuits.py

@@ -1,11 +1,12 @@
 from qskit.circuits import hadamard, cx
 
-fef create_anti_error_circuit():
+def create_anti_error_circuit():
     # Creates a basic Quantum Cyber hadamard circuit
     cq = cx.Circuit(1)
     cq.add(hadamard(1))
-    cq.measure(Xcx.M0, key=0)\n    return cq
+    cq.measure(Xcx.M0, key=0)
+    return cq
 
 if __name__ == '__main__':
     circuit = create_anti_error_circuit()
-    print(circuit)
+    print(circuit)

core/performance.py

@@ -9,23 +9,52 @@ def __init__(self):
         self.execution_times = []
 
     #static method to measure quantum circuit performance
-    @lru_cache(maxsize=1000)
-    def cache_quantum_result(circuit_key: str, params: tuple) -> Any:
+    def cache_quantum_result(self, circuit_key: str, params: tuple) -> Any:
         """Cache quantum circuit results for repeated computations"""
         start = time.time()
-        pass  # Implementation depends on specific quantum operations
+        # Placeholder implementation for caching quantum results
+        result = self._execute_quantum_circuit(circuit_key, params)
         self.execution_times.append(time.time() - start)
+        return result
 
-    @staticmethod
-    async def parallel_circuit_execution(circuits: List[any]) []
-        """Execute quantum circuits in parallel with model profiling"""
+    async def parallel_circuit_execution(self, circuits: List[Any]) -> List[Any]:
+        """Execute quantum circuits in parallel with model profiling and error handling"""
         start = time.time()
-        results = [
-            circuit.run() for circuit in circuits
-        ]
+        results = []
+        for circuit in circuits:
+            try:
+                result = await circuit.run()
+                results.append(result)
+            except Exception as e:
+                results.append(f"Error executing circuit: {e}")
         self.execution_times.append(time.time() - start)
         return results
 
+    def profile_execution(self, func, *args, **kwargs):
+        """Profile the execution of a function and log performance metrics"""
+        start = time.time()
+        result = func(*args, **kwargs)
+        end = time.time()
+        execution_time = end - start
+        self.execution_times.append(execution_time)
+        print(f"Function {func.__name__} executed in {execution_time:.4f} seconds")
+        return result
+
+    def optimize_quantum_circuit(self, circuit):
+        """Optimize a quantum circuit by reducing gate operations and depth"""
+        # Placeholder for optimization logic
+        optimized_circuit = circuit.optimize()
+        return optimized_circuit
+
+    def get_performance_report(self) -> dict:
+        """Returns analysis of computation parameters with performance metrics"""
+        return {
+            "execution_times": self.execution_times,
+            "average_execution_time": sum(self.execution_times) / len(self.execution_times) if self.execution_times else 0,
+            "max_execution_time": max(self.execution_times) if self.execution_times else 0,
+            "min_execution_time": min(self.execution_times) if self.execution_times else 0
+        }
+
     def get_performance_report(self) -> dict:
         """Returns analysis of computation parameters with performance metrics"""
         return {

docs/ARCHITECTURE.md

@@ -19,7 +19,47 @@ QuantumAI/
 
 ### Quantum Circuits
 - **Purpose**: Define, simulate, and optimize quantum circuits.
-- **Key Features**: 
+- **Key Features**:
   - Leverages industry-standard frameworks (e.g., Qiskit).
   - Implements abstraction layers for circuit configuration and error correction.
+  - Includes performance monitoring and optimization tools.
 
+### AI Models
+- **Purpose**: Develop and train AI models, including quantum-augmented neural networks.
+- **Key Features**:
+  - Supports hybrid AI-Quantum models.
+  - Includes training routines and performance profiling.
+
+### Cryptographic Utilities
+- **Purpose**: Provide cryptographic routines and quantum-resistant encryption modules.
+- **Key Features**:
+  - Implements post-quantum cryptographic algorithms.
+  - Ensures robust security measures for data protection.
+
+### API Documentation
+- **Purpose**: Provide clear and accurate documentation for contributors and developers.
+- **Key Features**:
+  - Describes inputs, outputs, and examples for all API endpoints.
+  - Includes sections for FastAPI integration, AI-Quantum communication flow, and security considerations.
+  - Ensures all API routes are correctly documented for developers.
+
+### FastAPI Integration
+- **Purpose**: Integrate FastAPI to create a robust and scalable API for QuantumAI.
+- **Key Features**:
+  - Defines API endpoints for quantum circuit operations, AI model training, and cryptographic functions.
+  - Implements input validation and error handling.
+  - Supports asynchronous operations for improved performance.
+
+### AI-Quantum Communication Flow
+- **Purpose**: Describe the communication flow between AI and quantum components.
+- **Key Features**:
+  - Details the data flow between AI models and quantum circuits.
+  - Explains how quantum computations are integrated into AI training and inference processes.
+  - Includes diagrams or flowcharts to illustrate the communication process.
+
+### Security Considerations
+- **Purpose**: Ensure robust security measures for data protection.
+- **Key Features**:
+  - Describes the implementation of quantum-resistant cryptographic algorithms.
+  - Details the security measures for API endpoints and data transmission.
+  - Includes guidelines for secure key management and access control.

docs/CI_CD.md

@@ -9,7 +9,7 @@ This document outlines the Continuous Integration and Continuous Deployment (CI/
 - **Secure Deployment**: Enforce code signing, audit logging, and secret management throughout the build and deploy process.
 
 ## Example CI/CD Workflow with GitHub Actions
-Create a workflow file at `.github/workflows/ci_cd_yml` with the following content:
+Create a workflow file at `.github/workflows/ci_cd.yml` with the following content:
 
 ```yaml
 name: QuantumAI CI/CD Pipeline
@@ -20,46 +20,54 @@ on:
   pull_request:
     branches: [ main ]
 
- 
-jobs: 
+jobs:
   build:
     runs-on: ubuntu-latest
-    steps: 
+    steps:
       - name: Checkout Code
-        uses: actions/checkout/v2
+        uses: actions/checkout@v2
 
       - name: Set up Python
-
-        uses: actions/setup-python:/v2
+        uses: actions/setup-python@v2
         with:
           python-version: '3.9'
 
       - name: Install Dependencies
         run: |
-          pip -i --upgrade pip
+          pip install --upgrade pip
           pip install -r requirements.txt
 
       - name: Run Unit & Integration Tests
         run: |
           pytest tests/
 
-
   deploy:
     needs: build
     runs-on: ubuntu-latest
     if: github.ref == 'refs/heads/main'
     steps:
       - name: Checkout Code
-        uses: actions/checkout/v2
+        uses: actions/checkout@v2
 
-      - name: Build docker image
+      - name: Build Docker image
         run: |
-          docker build t quantumai:latest
+          docker build -t quantumai:latest .
 
       - name: Push Docker image
         run: |
-          docker tag quantumai:latest
+          docker tag quantumai:latest your-docker-repo/quantumai:latest
+          docker push your-docker-repo/quantumai:latest
 
       - name: Deploy to Kubernetes
         run: |
-          k8 apply -f deployment/k8s_deployment.yaml
+          kubectl apply -f deployment/k8s_deployment.yaml
+
+### Additional CI/CD Best Practices
+- **Code Signing**: Ensure all build artifacts are signed to verify their integrity.
+- **Audit Logging**: Implement comprehensive logging for all CI/CD activities.
+- **Secret Management**: Use secure methods to manage and store sensitive information like API keys and passwords.
+- **Environment Isolation**: Maintain separate environments for development, testing, and production to avoid conflicts.
+- **Automated Rollbacks**: Implement automated rollback mechanisms to revert to a stable state in case of deployment failures.
+- **Security Scanning**: Integrate security scanning tools to detect vulnerabilities in the codebase and dependencies.
+- **Performance Monitoring**: Monitor the performance of the deployed application and CI/CD pipeline to identify bottlenecks and optimize processes.
+- **Documentation**: Maintain up-to-date documentation for the CI/CD pipeline, including configuration details, troubleshooting guides, and best practices.

docs/CONTRIBUTING.md

@@ -1,2 +1,94 @@
+# QuantumAI Contribution Guidelines
 
-<!-- ...existing code from CONTRIBUTING.md... -->
+## Introduction
+Welcome to the QuantumAI project! This document outlines the guidelines for contributing to the development of the most advanced quantum-enhanced AI and AGI framework.
+
+## How to Contribute
+1. **Fork the Repository**: Start by forking the QuantumAI repository on GitHub.
+2. **Clone Your Fork**: Clone your fork to your local machine.
+3. **Create a Branch**: Create a new branch for your feature or bugfix.
+4. **Make Changes**: Implement your changes and commit them with clear messages.
+5. **Submit a Pull Request**: Push your branch to GitHub and submit a pull request to the main repository.
+
+### Setting Up the Development Environment
+1. **Install Dependencies**: Ensure you have Python 3.9 and Docker installed.
+2. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/your-username/QuantumAI.git
+   cd QuantumAI
+   ```
+3. **Create a Virtual Environment**:
+   ```bash
+   python -m venv venv
+   source venv/bin/activate
+   ```
+4. **Install Python Dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+5. **Build the Docker Image**:
+   ```bash
+   docker build -t quantumai:latest .
+   ```
+6. **Run the Docker Container**:
+   ```bash
+   docker run -p 8080:8080 quantumai:latest
+   ```
+
+### Additional Setup Steps
+- **Configure Environment Variables**: Set up environment variables for configuration settings and secrets.
+- **Set Up Kubernetes**: Install and configure Kubernetes for local development and testing.
+- **Set Up CI/CD Tools**: Install and configure CI/CD tools like GitHub Actions for automated testing and deployment.
+- **Set Up Monitoring Tools**: Install and configure monitoring tools like Prometheus and Grafana for tracking application performance.
+- **Set Up Logging Tools**: Install and configure logging tools like ELK Stack for troubleshooting and debugging.
+- **Set Up Security Tools**: Install and configure security tools like OWASP ZAP for identifying vulnerabilities.
+- **Set Up Code Quality Tools**: Install and configure code quality tools like SonarQube for maintaining code quality.
+- **Set Up Documentation Tools**: Install and configure documentation tools like MkDocs for generating documentation.
+   ```
+
+## Areas of Contribution
+- **Quantum Models**: Develop new quantum circuits and hybrid models.
+  - **Steps**:
+    1. Fork and clone the repository.
+    2. Create a new branch for your feature.
+    3. Implement your quantum circuit in `quantum_circuits/`.
+    4. Write tests in `tests/`.
+    5. Submit a pull request.
+- **AI Algorithms**: Enhance AI models with quantum features.
+  - **Steps**:
+    1. Fork and clone the repository.
+    2. Create a new branch for your feature.
+    3. Implement your AI model in `ai_models/`.
+    4. Write tests in `tests/`.
+    5. Submit a pull request.
+- **Security**: Implement and improve post-quantum cryptographic methods.
+  - **Steps**:
+    1. Fork and clone the repository.
+    2. Create a new branch for your feature.
+    3. Implement your cryptographic method in `crypto_utils/`.
+    4. Write tests in `tests/`.
+    5. Submit a pull request.
+- **Documentation**: Write guides, tutorials, and research papers.
+  - **Steps**:
+    1. Fork and clone the repository.
+    2. Create a new branch for your documentation.
+    3. Write your documentation in `docs/`.
+    4. Submit a pull request.
+
+## Research Discussions
+We host monthly research discussions to share progress, ideas, and challenges. Join our community on Slack and GitHub Discussions to participate.
+
+### Participating in Research Discussions
+1. **Join the Slack Community**: Join our Slack workspace to participate in real-time discussions.
+2. **Join GitHub Discussions**: Participate in our GitHub Discussions to share your ideas and insights.
+
+## Hackathons
+Participate in our quarterly hackathons to collaborate with other contributors and push the boundaries of quantum-AI research.
+
+### Participating in Hackathons
+1. **Register for Hackathons**: Register for our quarterly hackathons on our website.
+2. **Prepare Your Contributions**: Work on your contributions in advance to be ready for the hackathon.
+3. **Collaborate and Innovate**: Collaborate with other contributors and push the boundaries of quantum-AI research.
+
+## Conclusion
+Thank you for contributing to QuantumAI! Together, we can build the future of hybrid intelligence.

docs/DEPLOYMENT.md

@@ -23,7 +23,7 @@ FROM python:3.9-slim
 WORKDIR /app
 
 # Install dependencies
-COPY requirements.tx `./app 
+COPY requirements.txt ./app
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Copy application code
@@ -33,7 +33,7 @@ COPY . .
 EXPOSE 8080
 
 # Command to run the application
-CMT ["python", "main.py"]
+CMD ["python", "main.py"]
 ```
 
 ## Building & Running the Image
@@ -75,8 +75,28 @@ spec:
         env:
         - name: ENVIRONMENT
           value: "production"
+        - name: SECRET_KEY
+          valueFrom:
+            secretKeyRef:
+              name: quantumai-secrets
+              key: secret-key
+        - name: DATABASE_URL
+          valueFrom:
+            secretKeyRef:
+              name: quantumai-secrets
+              key: database-url
 ```
 
 ## Secure Deployment
 - Authorization and secret management via Kubernetes.
-- Separation of environment specific settings.
+- Separation of environment specific settings.
+
+### Additional Deployment Best Practices
+- **Environment Variables**: Use environment variables to manage configuration settings and secrets.
+- **Health Checks**: Implement health checks to monitor the application's status and ensure it is running correctly.
+- **Scaling**: Configure auto-scaling policies to handle varying loads and ensure high availability.
+- **Backup and Recovery**: Implement backup and recovery strategies to protect against data loss.
+- **Monitoring and Logging**: Integrate monitoring and logging tools to track application performance and troubleshoot issues.
+- **Security Audits**: Conduct regular security audits to identify and mitigate vulnerabilities.
+- **Compliance**: Ensure compliance with industry standards and regulations, such as GDPR and HIPAA.
+- **Documentation**: Maintain up-to-date documentation for the deployment process, including configuration details, troubleshooting guides, and best practices.

docs/README.md

@@ -3,4 +3,4 @@
 - [API Reference](docs/api/README.md)
 - [Development Guide](docs/guides/development.md)
 - [Smart Contracts](docs/contracts/README.md)
-- [QuantumAI Overview](docs/README.md)
+- [QuantumAI Overview](docs/overview.md)