See how AI attestation badges look in actual use cases
Quantum computing represents a paradigm shift in computational power. Unlike classical computers that process information in binary states, quantum computers leverage the principles of superposition and entanglement to perform calculations that would be impossible for traditional systems...
Recent breakthroughs at major research institutions have brought us closer to practical quantum applications in cryptography, drug discovery, and climate modeling...
/** * Authentication Service * AI-Generated documentation */ export class AuthService { // Authenticate user with credentials async login(email: string, password: string): Promise<User> { // Validate input parameters if (!email || !password) { throw new Error('Email and password required'); } // Hash password and verify against database const hashedPassword = await bcrypt.hash(password, 10); return await this.userRepository.findByCredentials(email, hashedPassword); } }AI_GENERATED
A curated list of quantum computing resources, libraries, and tools
Note: This list was compiled with GPT-4 assistance for research and categorization. All entries have been manually verified.
This paper presents a novel approach to climate prediction using transformer-based neural networks. Our methodology demonstrates a 23% improvement in prediction accuracy compared to traditional models...
Climate modeling has traditionally relied on physics-based simulations. However, the complexity of Earth's climate system presents significant computational challenges...
We employed a transformer architecture with 1.2 billion parameters, trained on 40 years of climate data from multiple sources...
Our model achieved a mean absolute error of 0.82°C for temperature predictions and 12.3mm for precipitation forecasts...
The improved accuracy demonstrates the potential of neural architectures in climate modeling. Future work will explore ensemble methods...
We thank the Climate Data Initiative for providing access to historical datasets. This work was supported by NSF Grant #2024-1234.
AI-ASSISTED · attest.ink/attestation/2025-01-15-ml-climate
# 🚀 QuantumML Framework
[](https://attest.ink/verify/?data=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)
[](https://opensource.org/licenses/MIT)
[](https://github.com/quantumml/framework/actions)
[](https://codecov.io/gh/quantumml/framework)
[](https://www.python.org/downloads/)
A cutting-edge machine learning framework for quantum computing applications. This project leverages AI assistance for documentation, code optimization, and testing strategies.
## ✨ Features
- **Quantum Circuit Optimization** - Automatically optimize quantum circuits for NISQ devices
- **Hybrid Classical-Quantum Models** - Seamlessly integrate classical and quantum components
- **Hardware Agnostic** - Support for IBM Qiskit, Google Cirq, and Rigetti Forest
- **GPU Acceleration** - Leverage CUDA for classical preprocessing
## 🚀 Quick Start
```bash
pip install quantumml
```
```python
from quantumml import QuantumClassifier
# Create a quantum classifier
model = QuantumClassifier(n_qubits=4)
# Train on classical data
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
```
## 📚 Documentation
Full documentation is available at [docs.quantumml.org](https://docs.quantumml.org)
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
This project was developed with AI assistance from Claude 3 Opus. All AI-generated content has been reviewed and verified by human developers.
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Made with ❤️ by the QuantumML Team
Note: This example shows how to integrate AI attestation badges alongside traditional GitHub badges. The AI badge links to a verifiable attestation proving AI assistance in the project.