Quantum Output Data Compression for Faster Post-Processing
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Solution Overview
Problem
Traditional approaches for quantum computing fail to efficiently and quickly post-process large amounts of data produced by quantum computers, leading to delayed delivery of results to users or services.
Innovation Solution
A system comprising a processor and memory that compresses quantum output data using multiple compression levels, with a compression management component determining the appropriate level based on defined criteria, and employs machine learning to adaptively adjust compression algorithms for optimal data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum output data is post-processed using traditional approaches, then data processing can be performed, but the post-processing takes an undesirably long time
Solution Approach 1:
The patent extracts and separates the compression function from the quantum processing pipeline by introducing a dedicated compression component that operates independently on quantum output data. This extraction allows compression to occur in parallel with quantum logic processing, eliminating the sequential bottleneck where post-processing had to wait for complete quantum output generation.
Solution Approach 2:
The compression component performs preliminary compression on quantum output data as it is being generated, before the complete dataset is available. This preliminary action reduces the data volume early in the process, allowing subsequent post-processing operations to work on already-compressed data, significantly reducing overall processing time.
2Quantity of substance
If quantum output data is transmitted to users or services, then results can be delivered, but large amounts of data increase communication time and storage requirements
Solution Approach 1:
The patent changes the parameter of data representation by applying compression algorithms that transform the quantum output data into a more compact form. This parameter change reduces the data volume while preserving essential information, directly addressing the issue of large data volumes affecting transmission and storage efficiency.
3Quantity of substance
If compression is applied to quantum output data, then data volume is reduced, but determining the appropriate compression level adds system complexity
Solution Approach 1:
The compression component automatically determines appropriate compression levels and algorithms based on the characteristics of the quantum output data itself, without requiring external management input. This self-service approach reduces system complexity by making the compression process autonomous and adaptive to data properties.
Data Source
AI summary
Techniques for managing and compressing quantum output data (QOD) associated with quantum computing are presented. In response to receiving QOD from a quantum computer, a compressor component can compress QOD at first compression level to generate first compressed QOD, and can compress QOD at second compression level to generate second compressed QOD, the second compressed QOD can be less compressed than the first compressed QOD. Compressor management component (CMC) can determine whether first QOD includes sufficient data to enable it to be suitably processed by quantum logic. If so, CMC can allow first compressed QOD to continue to be sent to quantum logic and can discard second compressed QOD. If not sufficient, CMC can determine that second compressed QOD is to be processed by quantum logic. If CMC determines second compressed QOD does not include sufficient data, CMC can determine that the QOD is to be processed by quantum logic.