Iterative Natural-Language Message Linking with Quantum-Classical Scoring
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Solution Overview
Problem
Classical computing techniques for predictor valuation and selection are insufficiently accurate, personalized, and computationally inefficient for modern data analysis needs, particularly in terms of scale, volume, and velocity.
Innovation Solution
A method that combines classical and quantum data models to score and correlate features, using classical processors for initial scoring and quantum processors for enhanced accuracy, followed by a correlation and valuation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum data model is executed multiple times to improve prediction accuracy, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by determining the optimal number of quantum data model iterations in advance using training data before actual prediction. The system executes the quantum model on training data to identify the point of diminishing returns, then uses this pre-determined iteration count for subsequent predictions, avoiding unnecessary computational time while maintaining accuracy.
Solution Approach 2:
The patent implements feedback by using the results of quantum data model executions to continuously refine and update the classical data model. The system feeds back the quantum model predictions into the classical model, allowing the classical model to learn from quantum results and improve its own prediction capabilities, reducing reliance on repeated quantum executions.
2Measurement precision
If combined classical and quantum data models are used to enhance predictor valuation, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges classical and quantum data models into a unified hybrid system. The classical data model handles initial processing and benefit from quantum model refinements, creating an integrated system that leverages the strengths of both approaches while managing complexity through coordinated interaction between the two model types.
Solution Approach 2:
The patent uses the classical data model as an intermediary between data input and quantum model execution. The classical model prepares data, selects relevant features, and processes quantum model outputs, acting as a mediator that simplifies the interface between classical and quantum components and reduces overall system complexity.
Data Source
AI summary
Using a classical data model executing on a classical processor, a set of classical features is scored. A score of a classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving a resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The quantum data model is executed a number of times previously determined using a set of results of executing the quantum data model on a set of annotated training data. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.