---
license: apache-2.0
datasets:
- GainEnergy/quantum-oil-gas-dataset
base_model:
- GainEnergy/OGAI-R1
library_name: transformers
tags:
  - oil-gas
  - quantum-computing
  - hybrid-ai
  - reservoir-engineering
  - well-optimization
  - retrieval-augmented-generation
  - fine-tuned
  - quantum-llm
  - upstrima
model-index:
  - name: OGAI-Quantum
    results:
      - task:
          type: text-generation
          name: Quantum AI for Oil & Gas Engineering
        dataset:
          name: GainEnergy Quantum Oil & Gas Dataset
          type: custom
        metrics:
          - name: Quantum Reservoir Simulation Speedup
            type: benchmark
            value: Coming Soon
          - name: Hybrid AI Computational Efficiency
            type: benchmark
            value: Coming Soon
          - name: Quantum-RAG Retrieval Score
            type: accuracy
            value: Coming Soon
---

# **OGAI-Quantum: The Future of Oil & Gas AI (Coming Soon)**

![Hugging Face](https://img.shields.io/badge/HuggingFace-OGAI--Quantum-blue)  
[![License](https://img.shields.io/github/license/huggingface/transformers.svg)](LICENSE)

🚀 **OGAI-Quantum** is a **next-generation hybrid AI model** that fuses **quantum computing principles with classical deep learning** to deliver breakthrough performance in **reservoir modeling, drilling optimization, seismic analysis, and energy AI workflows**.

🌍 **COMING SOON**: Currently in **final development and quantum validation testing**.

---
## **🫠 Capabilities**
- **⚡ Quantum-Accelerated Simulations** – Faster reservoir modeling and seismic analysis.
- **🧠 Hybrid AI-Quantum Workflows** – Integrates quantum variational circuits with deep learning.
- **📚 Quantum-RAG for Technical Knowledge Retrieval** – Advanced AI-driven document retrieval for energy data.

### **📌 Core Quantum Use Cases**
| **Use Case**                  | **Quantum Advantage** |
|--------------------------------|-----------------------|
| **Reservoir Simulation**       | Multi-state quantum superposition for faster modeling |
| **Seismic Data Processing**    | Quantum-based feature recognition in seismic datasets |
| **Well Placement Optimization** | Quantum annealing for high-dimensional search spaces |
| **Production Optimization**     | Quantum variational circuits for real-time gas lift & production tuning |

---

## 🏢 **Quantum-Classical Hybrid Framework**
OGAI-Quantum is powered by **Upstrima's Quantum AI Engine**, combining **quantum-enhanced decision-making** with traditional deep learning.

```yaml
System Architecture:
├── Quantum Simulation Layer
│   ├── Quantum Gate Operations
│   ├── Qiskit & PennyLane Integration
│   ├── Variational Quantum Circuits (VQC)
│   ├── Quantum Annealing for Optimization
│   ├── Quantum Reservoir Simulation Models
│   ├── Seismic Data Quantum Processing
├── Classical AI Model
│   ├── Fine-Tuned TinyR1-32B Model
│   ├── Hybrid Engineering Knowledge Base
│   ├── Neural Retrieval-Augmented Generation (RAG)
│   ├── Classical Physics-Based Simulations
│   ├── AI-Powered Technical Document Understanding
│   ├── Adaptive Learning & Model Refinement
└── Hybrid Orchestration Layer
    ├── Quantum-Classical Task Partitioning
    ├── Quantum State Virtualization Engine
    ├── Quantum Pipeline API for High-Performance Computing
    ├── Real-Time Quantum State Synchronization
    ├── Cloud & Edge Deployment Support
    ├── API Integration with Upstrima AI Suite
```

---

## 📦 **Model Variants**
| **Model Name**     | **Base Model**   | **Quantum Features** | **Context Window** | **Use Case** |
|-------------------|----------------|----------------|----------------|-------------|
| **OGAI-Quantum**  | OGAI-R1 + Quantum | Yes | TDB tokens | **Hybrid AI for Energy & Engineering** |
| **OGAI-R1**       | TinyR1-32B | No | 128k tokens | **Reservoir AI & RAG** |
| **OGMOE**         | Mixtral-8x7B + MoE | No | 32K tokens | **Drilling Optimization & Decision Support** |

---

## 🚀 **Deployment & Integration**
OGAI-Quantum will be available on:
- **Hugging Face Inference API**
- **AWS Braket for Hybrid Quantum-Classical Workflows**
- **On-Premise Quantum-Classical HPC Deployment**

### **🔧 Technical Stack**
- **Quantum Libraries:** `Qiskit`, `PennyLane`, `Cirq`
- **AI Frameworks:** `Transformers`, `AutoGPTQ`, `PEFT`
- **Data Pipelines:** `FAISS`, `Pinecone`, `LangChain`

---

## ⚠️ **Limitations**
🚧 **Quantum Hardware Dependency** – While designed for hybrid execution, full quantum acceleration requires cloud-based quantum backends.  
🚧 **Experimental Hybrid AI** – Model performance is still undergoing validation for real-world **engineering applications**.  
🚧 **Not General-Purpose** – Optimized specifically for **oil & gas industry workflows**.  

---

## 🔗 **Resources**
- **[Quantum Applications in Oil & Gas](https://huggingface.co/docs/quantum-oil-gas-applications)** – Technical whitepaper on **hybrid AI for energy**.
- **[GainEnergy AI Platform](https://gain.energy)** – Explore AI-powered **quantum-enhanced energy solutions**.
- **[Upstrima Quantum Computing Extension](https://huggingface.co/docs/upstrima-quantum-extension)** – WebAssembly-powered quantum simulation.

---

## 📚 **Citing OGAI-Quantum**
```bibtex
@article{ogai-quantum-2025,
  title={OGAI-Quantum: Hybrid Quantum-Classical AI for Oil & Gas Engineering},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}
```