Quantum Hash Inversion for Consistent Data Compression
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Solution Overview
Problem
Classical data compression techniques face limitations in compression ratios and variable efficiency, straining industries with exponential data growth.
Innovation Solution
A computer-implemented method combining quantum and classical computing for data compression, involving metadata extraction, hashing, quantum and classical filtering, and AI-assisted hashing function selection to achieve efficient data compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If classical data compression techniques are used, then data can be compressed and stored, but the compression ratios are limited and extremely variable
Solution Approach 1:
The patent replaces classical computational mechanisms with quantum computing mechanisms for data compression. Specifically, it uses quantum parallelism to evaluate multiple compression algorithms simultaneously and employs quantum annealing to optimize compression parameters, thereby achieving both higher and more consistent compression ratios than classical techniques alone.
Solution Approach 2:
The patent creates multiple copies of the data in different quantum states to test various compression algorithms in parallel. By maintaining superposition states of multiple compression approaches simultaneously, the system can evaluate and select the optimal compression method while achieving reliable and consistent compression ratios across different data types.
2Productivity
If quantum computing is used for data compression, then compression efficiency is enhanced, but device complexity increases
Solution Approach 1:
The patent divides the data compression system into distinct quantum and classical segments. The quantum computing component handles the complex optimization and algorithm evaluation, while the classical computing component manages data input/output and result processing. This segmentation allows the system to leverage quantum advantages without requiring the entire system to be quantum, thereby managing device complexity more effectively.
Solution Approach 2:
The patent introduces a hybrid interface layer that acts as an intermediary between quantum computing resources and classical data processing systems. This intermediary manages the conversion and coordination between quantum states and classical data formats, simplifying the integration complexity and allowing standard classical systems to interface with quantum compression algorithms without direct complex quantum hardware requirements.
3Reliability
If quantum filtering operation is performed to discard calculated files, then false positive files are reduced, but computational time increases
Solution Approach 1:
The patent implements a multi-stage filtering process where quantum filtering and classical filtering are applied periodically and alternately. The quantum filtering operation is performed at key intermediate stages rather than continuously, allowing the system to reduce false positives through quantum interference effects while maintaining reasonable computational timing by batching operations and using periodic rather than continuous filtering.
Data Source
AI summary
A computer-implemented method of data compression leveraging quantum computing and classical computing includes extracting metadata from a binary data to be compressed. The binary data is compressed by iteratively performing a compression operation including selecting a hashing function and hashing the binary data. The quantum computer device performs an inverting of the hashing function and generates a finite number of one or more resultant files associated with the hashed data. A compressed file including the hashes and the metadata used for the decompression is stored upon determining that the one or more resultant files is a single file.