Hybrid Classical-Quantum Predictor Scoring for Resource Valuation
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Solution Overview
Problem
Classical computing techniques for predictor valuation and selection are insufficiently accurate, personalized, and fast enough to analyze data at the scale, volume, and velocity needed today, necessitating a more accurate and quickly computable method.
Innovation Solution
A combined classical/quantum predictor evaluation method that scores classical features using a classical processor, divides them into groups based on quantum processor capability, scores quantum features using a quantum processor, adjusts scores according to quantum model accuracy, and combines the results to calculate a resource valuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical computing techniques are used for predictor valuation and selection, then the method is computationally simple and easy to implement, but the accuracy and computational speed are insufficient for analyzing data at the required scale, volume, and velocity
Solution Approach 1:
The patent combines classical computing and quantum computing into a hybrid system. Classical processors handle feature selection, data preprocessing, and initialization of quantum states, while quantum processors perform complex predictor valuation calculations. The results are then integrated through a feedback mechanism, creating a unified system that leverages the strengths of both computing paradigms to achieve both high accuracy and computational efficiency.
2Productivity
If quantum computing is used for predictor valuation, then computational speed and accuracy are significantly improved, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent divides the predictor valuation task into distinct segments handled by different computing systems. Classical processors manage tasks suitable for deterministic computation (feature selection, data normalization, initialization), while quantum processors handle tasks requiring probabilistic parallel computation (predictor valuation, optimization). This segmentation allows each system to operate in its optimal regime, reducing overall system complexity while maintaining high computational speed.
Solution Approach 2:
The patent introduces intermediary components that bridge classical and quantum systems. These include classical-quantum interface modules that translate classical data into quantum states, feedback mechanisms that transfer quantum computation results back to classical systems, and coordination layers that manage the workflow between the two computing paradigms. These intermediaries simplify integration and reduce the complexity burden of combining disparate computing systems.
Data Source
AI summary
Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.