NLP Similarity Matching for Quantum-Classical Resource Selection
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Solution Overview
Problem
Current techniques fail to provide insight on whether a problem is more suitably solved via quantum computing or classical computing, leading to wastage of computing resources and potential cost escalations.
Innovation Solution
An advisor system utilizing natural language processing similarity matching to determine whether a problem requires quantum computing or classical computing by generating problem embedding vectors and matching them with quantum or classical computing-related problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If quantum computing resources are used for all problems, then computational power for complex problems is improved, but computing resource wastage increases
Solution Approach 1:
The system changes the parameter of computing resource selection by using NLP similarity matching to classify problems into quantum-suitable or classical categories, thereby optimizing resource allocation based on problem characteristics rather than uniformly using quantum resources
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the problem and computing resource selection. This intermediary uses NLP to analyze problem descriptions and determine the appropriate computing paradigm, preventing direct mismatch between problem type and resource allocation
2Adaptability or versatility
If quantum computing is adopted early in application architecture, then future readiness is improved, but cost escalation increases
Solution Approach 1:
The system applies partial action by selectively applying quantum computing only to problems that benefit from it, rather than adopting quantum computing universally. This partial adoption maintains future readiness for quantum-capable problems while avoiding the excessive costs of universal quantum deployment
3Measurement precision
If natural language processing similarity matching is used to classify problems, then computing resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent replaces manual or rule-based problem classification mechanisms with NLP-based semantic analysis. This substitution achieves higher classification accuracy by understanding problem descriptions naturally, while the modular NLP implementation keeps system complexity manageable
Data Source
AI summary
In some implementations, an advisor system may receive a description of a problem to be solved and problem data identifying quantum computing-related and classical computing-related problems. The advisor system may perform natural language processing on the description of the problem and the problem data to respectively generate a problem embedding vector for the problem and to generate embedding vectors that represent the quantum computing-related and classical computing-related problems. The advisor system may process the problem embedding vector and the embedding vectors, with a vector matching model, to determine a semantically closest matching one of the embedding vectors to the problem embedding vector and, accordingly, may generate a recommendation that includes an indication to solve the problem with a classical computing resource, a quantum computing resource, or a combination of a classical computing resource and a quantum computing resource.