Francisco Angulo de Lafuente
The Quantum-BIO-LLM project aims to enhance the efficiency of Large Language Models (LLMs) both in training and utilization. By leveraging advanced techniques from ray tracing, optical physics, and, most importantly, quantum physics, we strive to improve the overall efficiency of the system.
- Quantum Efficiency: Utilizing quantum mechanics principles to optimize processing and data handling.
- Ray Tracing Techniques: Implementing ray tracing for better data visualization and processing efficiency.
- Bioinspired Memory System: Our memory architecture is inspired by the human brain, activating different sectors, clusters, and specialized neural groupings, making it significantly more efficient.
quantum_bio_llm/
├── src/
│ ├── memory/
│ └── ...
├── examples/
│ ├── basic_usage.py
│ └── rag_memory.py
├── README.md
└── .gitignore
Our system tokenizes words into intensities of light, colors, and textures, creating a holographic memory system that produces emergent responses to queries based solely on the color mixtures of the words and the new color generated from that mixture.
This system is bioinspired by the human brain, where not all sectors are activated simultaneously. Instead, different sectors and specialized clusters are activated based on the context, leading to a more efficient processing mechanism.
To install the necessary dependencies, run:
pip install -r requirements.txtContributions are welcome! Please submit a pull request or open an issue for any improvements or suggestions.
This project is licensed under the MIT License. See the LICENSE file for details.
The system consists of three interconnected main modules:
-
Quantum Simulation Module (QSM)
- Implementation in Python using Qiskit and Cirq
- Simulation of quantum states for holographic memory
- Management of quantum coherence and entanglement
-
Bioinspired Neural Networks Module (BNNM)
- Base framework: PyTorch with custom extensions
- Implementation of holographic neural layers
- Dynamic adaptation system inspired by bacterial colonies
-
Holographic Memory Module (HMM)
- Hybrid implementation in Python/JavaScript
- Distributed storage system
- Associative retrieval mechanisms
- CPU: Minimum 32 cores, recommended 64 cores
- RAM: Minimum 128GB, recommended 256GB
- GPU: NVIDIA A100 or higher with at least 40GB VRAM
- Storage: NVMe SSD with a minimum of 2TB
- Network: 100Gbps for communication between nodes
- Operating System: Linux (Ubuntu 22.04 LTS or higher)
- Python: 3.10 or higher
- CUDA: 12.0 or higher
- Node.js: 18 LTS or higher
class QuantumSimulator:
def __init__(self, n_qubits, coherence_time):
self.n_qubits = n_qubits
self.coherence_time = coherence_time
self.quantum_circuit = None
def initialize_circuit(self):
"""Initialize the base quantum circuit"""
pass
def apply_holographic_encoding(self, data):
"""Encode data into quantum states"""
pass
def measure_state(self):
"""Measure the current quantum state"""
pass- REST API for communication with other modules
- Protocol for quantum state serialization
- Queue system for managing simulations
class BioInspiredLayer(nn.Module):
def __init__(self, input_dim, output_dim, adaptation_rate):
super().__init__()
self.weights = nn.Parameter(torch.randn(input_dim, output_dim))
self.adaptation_rate = adaptation_rate
self.bacterial_memory = None
def forward(self, x):
"""Forward propagation with bioinspired adaptation"""
pass
def adapt_weights(self, environmental_feedback):
"""Weight adaptation based on feedback"""
pass- Bioinspired optimization algorithm
- Environmental feedback mechanisms
- Distributed bacterial memory system
class HolographicMemory {
constructor(dimensions, capacity) {
this.dimensions = dimensions;
this.capacity = capacity;
this.memoryMatrix = null;
}
encode(data) {
// Holographic data encoding
}
retrieve(pattern) {
// Associative pattern retrieval
}
}- gRPC-based protocols for high efficiency
- Asynchronous event system
- Circular buffer for shared memory management
class StateManager:
def __init__(self):
self.quantum_state = None
self.neural_state = None
self.memory_state = None
def synchronize_states(self):
"""Synchronize states between modules"""
pass
def checkpoint_state(self):
"""Save system checkpoints"""
pass- Parallelization of quantum simulations
- Load distribution in clusters
- Smart caching for frequent patterns
- Retrieval latency: < 100ms
- Throughput: > 10k operations/second
- Energy efficiency: 50% reduction vs traditional systems
- Retrieval accuracy: > 95%
#!/bin/bash
# Build script for quantum-bio-neural system
# Compile quantum simulator
python -m build quantum_sim
# Build neural network module
pytorch-build bio_neural
# Deploy holographic memory
node build.js holographic_memory- Distributed logging system
- Real-time metrics
- Predictive alerts
- Quantum encryption for sensitive data
- Access auditing
- Distributed state backups
- Automatic rollback system
- Replication of critical states
- Dynamic load balancing
class QuantumBioAPI:
def __init__(self, config_path):
self.config = load_config(config_path)
self.initialize_systems()
def train_model(self, data):
"""Train the model with data"""
pass
def inference(self, input_data):
pass- Development of base quantum simulator (3 months)
- Implementation of bioinspired neural networks (3 months)
- Integration of holographic memory (2 months)
- Testing and optimization (2 months)
- Documentation and deployment (2 months)
- Functional prototype of the quantum simulator
- Operational holographic memory system
- Complete module integration
- Finalized technical documentation
- Deployed and validated system
- Modular design for horizontal growth
- Adaptability to different data scales
- Compatibility with existing systems
- Periodic updates of components
- Continuous performance monitoring
- Iterative optimization based on metrics
The Quantum-BIO-LLM project aims to enhance the efficiency and scalability of Large Language Models (LLMs) both in training and inference. By integrating advanced techniques such as quantum computing, ray tracing, bioinspired neural architectures, and holographic memory systems, the project sets out to revolutionize the way we process and interact with language models.
This system is built with modularity, optimization, and real-world applications in mind, offering a powerful framework that leverages cutting-edge technologies from quantum mechanics, neural networks, and optical physics.
-
Quantum Optimization: Leveraging quantum simulations for efficient processing and state entanglement, enhancing energy and computational performance.
-
Ray Tracing Integration: Implementing ray tracing techniques to simulate optical data pathways, enabling efficient visualization and interaction with neural networks.
-
Bioinspired Memory System: A memory architecture inspired by the human brain, activating specific clusters dynamically based on context, reducing overall energy consumption and improving speed.
-
Holographic Memory: Tokenizes information into colors, textures, and intensities of light to create a distributed and associative memory system, offering highly efficient query responses.
quantum_bio_llm/
├── src/ # Holographic memory system
│ ├── memory/ # Holographic memory system
│ ├── neural/ # Bioinspired neural network modules
│ ├── quantum/ # Quantum simulation modules
│ └── state_manager.py # State synchronization between modules
├── examples/ # Usage examples and demos
│ ├── basic_usage.py
│ └── rag_memory.py
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── LICENSE # License details
- Operating System: Linux (Ubuntu 22.04 LTS recommended) or Windows 10
- Python: Version 3.10 or higher
- CUDA: Version 12.0 or higher (for GPU acceleration)
-
Clone the repository:
git clone https://github.com/Agnuxo1/Unified-Holographic-Neural-Network.git cd Unified-Holographic-Neural-Network/JavaScript_DEMO -
Install Python dependencies:
pip install -r requirements.txt
-
Run an example:
python examples/basic_usage.py
- Frameworks: Qiskit, Cirq
- Capabilities:
- Simulation of multi-qubit systems
- Quantum state entanglement and coherence management
- Encoding and decoding of holographic data into quantum states
- Framework: PyTorch
- Features:
- Dynamic neural layers with bioinspired adaptation mechanisms
- Memory bacterial colony-inspired weight optimization
- Integration with holographic memory for context-driven inference
- Implementation: Hybrid Python/JavaScript
- Key Features:
- Associative data retrieval based on pattern matching
- Tokenization of input into light intensities and colors
- Context-aware memory activation, inspired by human cognition
- Purpose: Ensure consistent states between quantum, neural, and memory modules
- Implementation:
- Circular buffer for shared memory
- REST API for inter-module communication
- Checkpointing system for fault tolerance
- Retrieval Latency: < 100 ms
- Throughput: > 10,000 operations/second
- Energy Efficiency: 50% improvement over conventional LLM systems
- Accuracy: > 95% for associative retrieval tasks
- Preskill, J. (2018). "Quantum Computing in the NISQ Era and Beyond." Quantum, 2, 79. Link
- Harrow, A., Hassidim, A., & Lloyd, S. (2009). "Quantum Algorithm for Linear Systems of Equations." Physical Review Letters, 103(15). Link
- Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS. Link
- Borgeaud, S., et al. (2021). "Improving Language Models by Retrieving from Trillions of Tokens." arXiv preprint arXiv:2112.04426. Link
- Chowdhery, A., et al. (2022). "PaLM: Scaling Language Modeling with Pathways." Google Research. Link
- Shoeybi, M., et al. (2020). "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism." NVIDIA Research. Link
- Foley, T., et al. (2017). "NVIDIA OptiX Ray Tracing Engine." ACM Transactions on Graphics. Link
- Patwary, M., et al. (2021). "Efficient Large-Scale Language Model Training on GPU Clusters." NVIDIA Technical Report. Link
We welcome contributions from the community! Whether it’s improving code, documentation, or proposing new features, feel free to submit a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for more details.