lqft-python-engine 0.3.0

LQFT Engine: Memory Circuit Breaker & Strict Type Safety (v5.0 Stable)

Log-Quantum Fractal Tree (LQFT) 🚀

📌 Project Overview

The Log-Quantum Fractal Tree (LQFT) is a high-performance, scale-invariant data structure engine designed for massive data deduplication and persistent state management.

It synthesizes Hash Array Mapped Trie (HAMT) routing with Merkle-DAG structural folding to provide:

• Deterministic ( O(1) ) search latency
• Sub-linear ( O(\Sigma) ) space complexity

By offloading core associative logic to a native C extension (v4.4), the LQFT bypasses the Python Global Interpreter Lock (GIL) and achieves significant memory reduction for versioned, redundant, or patterned datasets.

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🧠 Core Architecture

1. Scale-Invariant Time Complexity: ( O(1) )

• The LQFT utilizes a fixed 64-bit hash space partitioned into 13 levels.
• Unlike standard balanced trees that grow in height as data increases ( O(\log N) ), the LQFT's pathing is physically capped.

Key Properties:

• Deterministic Latency: Every search or insertion requires exactly 13 pointer hops.
• Scale-Invariance: Performance remains constant whether the dataset contains ( 10^3 ) or ( 10^9 ) items.

2. Entropy-Based Space Complexity: ( O(\Sigma) )

Using a global C-Registry, the engine implements structural interning.
Nodes are identified by the cryptographic hash of their contents and child pointers (Merkle-DAG).

Key Mechanisms:

• Structural Folding: Identical sub-trees are shared physically in memory across different branches or versions.
• Efficient Versioning: Saving a new version of a state-space requires only ( O(\log N) ) new nodes (the path to the change), while the remainder is shared with previous versions.

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🚀 Performance Benchmarks

Metric	Result
Environment	Python 3.12 | GCC -O3 (Native C-Extension)
Search Latency (p50)	~500 ns
Read Throughput	~1.8 Million ops/sec
Space Efficiency	Up to 1,500× reduction in versioned graph simulations
Stability	Zero-drift memory reclamation via Dynamic C-Registry

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🛠️ Getting Started

Installation

The engine requires a C compiler (GCC/MinGW or MSVC) to build the native extension.

# Clone the repository
git clone https://github.com/ParjadM/Log-Quantum-Fractal-Tree-LQFT-
cd Log-Quantum-Fractal-Tree-LQFT-

# Build the native C-extension
python setup.py build_ext --inplace