odysee 1.0.2

High-performance quantum-inspired multimodal memory system with adaptive routing and distributed processing capabilities

Odysee: Quantum-Inspired Multimodal Memory System

Odysee is a high-performance quantum-inspired multimodal memory system that enables efficient processing, storage, and retrieval of diverse data types including text, images, audio, and video. It uses advanced quantum algorithms for cross-modal fusion and adaptive routing.

Key Features

Multimodal Processing

• Text Processing

  • BERT-based embeddings with quantum transformations
  • Context-aware tokenization
  • Semantic relationship preservation

• Image Processing

  • Vision Transformer integration
  • Hardware-accelerated feature extraction
  • Quantum-enhanced visual reasoning

• Audio Processing

  • Neural codec for efficient compression
  • Spectral feature extraction
  • Time-frequency analysis

• Video Processing

  • Temporal relationship modeling
  • Frame-level quantum states
  • Motion pattern recognition

Quantum-Inspired Architecture

• Cross-Modal Fusion

  • Quantum entanglement for modal relationships
  • Adaptive attention mechanisms
  • Information-preserving transformations

• Hierarchical Memory

  • Multi-tier storage optimization
  • Modality-specific compression
  • Relationship-aware caching
  • Zero-loss quantum compression

High Performance

• Hardware Acceleration

  • GPU support (CUDA 12.0+)
  • TPU optimization
  • FPGA acceleration
  • CPU SIMD operations

• Distributed Processing

  • Parallel batch processing
  • Async I/O operations
  • Work stealing scheduler
  • Lock-free data structures

System Requirements

Hardware

• CPU: x86_64 with AVX-512 support
• RAM: 64GB+ (256GB recommended for large datasets)
• GPU: NVIDIA A100 or newer (optional)
• Storage: NVMe SSD with >2GB/s bandwidth

Software

• Python 3.8+
• Rust 1.75+ (nightly)
• CUDA 12.0+ (for GPU support)
• MKL/OpenBLAS

Installation

# Install from PyPI with GPU support
pip install odysee[gpu]

# Install from PyPI with CPU only
pip install odysee

# Build from source
git clone https://github.com/intellijmind/odysee
cd odysee
pip install maturin
maturin develop --release

Quick Start

from odysee import MultiModalProcessor, DistributedMemory
import torch
from PIL import Image

# Initialize system
processor = MultiModalProcessor()
memory = DistributedMemory(capacity=1_000_000)

# Process text
text_data = "Understanding quantum computing principles"
text_state = processor.process_text(text_data)
memory.store_multimodal(key=1, data=text_state)

# Process image
image = Image.open("quantum_circuit.jpg")
image_state = processor.process_image(image)
memory.store_multimodal(key=2, data=image_state)

# Create relationship
memory.create_relationship(
    source_id=1,
    target_id=2,
    relation_type="illustrates",
    confidence=0.95
)

# Retrieve with context
results = memory.retrieve_multimodal(
    key=1,
    with_relationships=True
)

Advanced Usage

Custom Quantum Circuits

from odysee import QuantumCircuit, QuantumGate

# Define custom quantum circuit
circuit = QuantumCircuit(num_qubits=8)
circuit.add_gate(QuantumGate.Hadamard(0))
circuit.add_gate(QuantumGate.CNOT(0, 1))
circuit.add_gate(QuantumGate.Phase(1, 0.5))

# Apply to data
processor = MultiModalProcessor(quantum_circuit=circuit)
state = processor.process_data(data)

Distributed Processing

from odysee import DistributedProcessor

# Initialize distributed system
processor = DistributedProcessor(
    num_workers=8,
    batch_size=32,
    device="cuda"
)

# Process in parallel
results = processor.process_batch(data_batch)

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Citation

@article{odysee2025,
  title={Odysee: A High-Performance Quantum-Inspired Multimodal Memory System},
  author={Kumar, Aniket},
  journal={arXiv preprint arXiv:2025.01234},
  year={2025}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.