QUBO Model Compression Using Lookup Tables and Entropy Coding

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Solution Overview

Problem

Quantum computing systems face challenges in efficiently compressing and transmitting large QUBO models due to their extensive size, which leads to increased storage and bandwidth requirements.

Innovation Solution

The proposed solution involves constructing lookup tables for unique variable names and coefficients in QUBO models, translating these into index-based representations, and applying entropy encoding and quantization to achieve lossy or lossless compression.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If QUBO models are transmitted or stored in their original format, then data accuracy is maintained, but storage space and bandwidth requirements increase significantly

Engineering Contradiction:

Improvestorage spaceVSAvoiddata accuracy

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The QUBO model is segmented into distinct components: variable names, coefficients, and their relationships. Lookup tables are constructed to encode these components separately, allowing efficient compression while maintaining the ability to reconstruct the original model accurately.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The model parameters (variable names and coefficients) are transformed into compact representations using lookup tables that map them to index values. This parameter transformation enables significant space reduction while preserving the essential information needed for accurate model reconstruction.

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If QUBO models are compressed using traditional methods, then data size is reduced, but decompression complexity and time increase

Engineering Contradiction:

Improvedata sizeVSAvoiddecompression time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

Lookup tables are constructed in advance during the compression phase, organizing variable names and coefficients into efficient index-based representations. This preliminary organization enables rapid decompression by allowing direct lookup and reconstruction without complex processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The original QUBO model information is copied into compact index-based representations through lookup tables. This copying process creates a space-efficient version that can be quickly reconstructed into the original model format without requiring complex decompression algorithms.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250202500A1A compression method for qubos and ising models

Publication Date: 2025.06.19 DELL PROD LP

AI summary

Compressing problems or models configured for execution in quantum computing systems is disclosed. An original problem, which may be represented as a string, for execution in a quantum computing system is classically compressed using classical computing systems. Unique variable names and unique coefficients stored in tables constructed from the original problem are associated with unique identifiers. The problem is then translated to a translated form, such as a translated string, using the tables. The translated string may also be generated after quantizing values in the at least one of the tables. The translated string, whether or not quantized, may be entropy encoded. The final result is a compressed version or string of the original string that can be transmitted and stored in a compressed form. The compressed problem may also be decompressed and submitted for execution in a quantum computing system.