OGAI-Quantum / README.md
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metadata
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
License

πŸš€ 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.
  • πŸ“š Quantum-RAG for Technical Knowledge Retrieval – Advanced AI-driven document retrieval for energy data.

πŸ“Œ 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.

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

πŸ“¦ 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

πŸš€ 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

πŸ”§ 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.


πŸ”— Resources


πŸ“š Citing OGAI-Quantum

@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}
}