qpac 0.2.1

Quantum-Inspired Pattern-Aware Compression (QPAC) — Hybrid entropy-aware compression inspired by quantum entropy and LZ4.

QPAC: Quantum-Inspired Pattern-Aware Compression

QPAC (Quantum-Inspired Pattern-Aware Compression) is a Python library for hybrid entropy-aware compression of data. Inspired by LZ4, it supports multiple compression strategies and can adaptively choose the best method for each block of data.

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Features

• Hybrid Compression: Automatically selects between raw, RLE, dictionary, delta, and hybrid strategies.
• Entropy-Guided: Compression decisions are guided by Shannon entropy to maximize efficiency.
• Fast and Lightweight: Designed for small blocks (e.g., IoT or memory-constrained environments).
• Reversible: Supports both compression and decompression for data integrity.
• Extensible: Easily add custom compression strategies.

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Installation

From PyPI (Test)

pip install -i https://test.pypi.org/simple qpac

From PyPI (Production)

pip install qpac

Usage

import qpac
from qpac import compress_block, decompress_block, CompressedBlock

# Sample data
data = bytearray([1,1,1,2,2,3,3,3,4,5,6,7,8])

# Compress
cb: CompressedBlock = compress_block(data)
print(f"Original size: {cb.original_size}")
print(f"Used strategy: {cb.strategy}, compressed size: {cb.compressed_size}")

# Decompress
decompressed = decompress_block(cb)
print(f"Roundtrip ok: {decompressed == data}")

API Reference

compress_block(block: bytes) -> CompressedBlock

Compresses a block of data using the most appropriate strategy based on entropy and patterns.

decompress_block(cb: CompressedBlock) -> bytes

Reverses the compression and returns the original data.

CompressedBlock

Represents a compressed block with the following attributes:

• strategy: Compression strategy used
• original_size: Original block size
• compressed_size: Compressed size
• data: Compressed byte array

Entropy

calculate_entropy(data: bytes) -> float

Calculates the Shannon entropy of a data block.

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Compression Strategies

Strategy	Description
STRATEGY_RAW	No compression; stores original data
STRATEGY_RLE	Run-length encoding for repeated patterns
STRATEGY_DICT	Dictionary-based compression
STRATEGY_DELTA	Delta encoding for numerical sequences
STRATEGY_HYBRID	Adaptive combination based on entropy

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License

This project is licensed under the MIT License © Soumyapriya Goswami.
For full license details, see the LICENSE file.

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References

• LZ4 Compression: https://lz4.github.io/lz4/
• Shannon Entropy: C. E. Shannon, A Mathematical Theory of Communication, 1948

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Contact

Soumyapriya Goswami
📧 Email: [e-mail]
💻 Project Repository: GitHub