Classical-Quantum Data Confidence Fabric for Trust Tracking
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Solution Overview
Problem
Current quantum computing systems lack mechanisms to track and determine the confidence score of input and output data, and there is no way for classical nodes to assess the trustworthiness of quantum data or processes, making it difficult to integrate quantum computing into classical data confidence fabrics.
Innovation Solution
A hybrid classical-quantum computing system (HCQS) within a data confidence fabric (DCF) assigns confidence scores to classical and quantum data, integrates DCF functionalities, and generates overall confidence scores for hybrid algorithms, enabling classical nodes to determine trustworthiness and support DCF capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum computing is integrated into classical data confidence fabrics, then computational capability and functionality are improved, but tracking confidence scores and determining data trustworthiness becomes difficult
Solution Approach 1:
The patent introduces a confidence score tracking mechanism that acts as an intermediary between quantum and classical computing components. This intermediary system maintains confidence scores for quantum operations and classical data throughout the computational graph, enabling trustworthiness tracking without limiting computational capability. The confidence score is propagated through classical-quantum interfaces, allowing each component to contribute its reliability assessment while maintaining overall system versatility.
2Reliability
If quantum algorithms are executed multiple times to achieve deterministic results, then reliability is improved, but computational complexity and time consumption increase
Solution Approach 1:
The patent applies preliminary action by executing quantum algorithms multiple times in advance to build confidence scores before the final classical processing stage. By performing these repeated quantum operations beforehand and tracking their outcomes, the system establishes reliability metrics without adding complexity to the final computational graph. The confidence score aggregation is prepared in advance, allowing efficient final results without redundant re-execution.
3Reliability
If confidence tracking mechanisms are added to quantum systems, then data trustworthiness is improved, but system complexity increases
Solution Approach 1:
The patent implements universality by designing a multi-functional confidence tracking system that serves multiple purposes simultaneously. The same confidence score mechanism tracks data trustworthiness, monitors quantum operation reliability, and provides auditing capabilities across the entire computational graph. This universal approach avoids adding separate specialized systems for each function, thereby improving reliability without proportionally increasing complexity.
4Adaptability or versatility
If hybrid classical-quantum computational graphs are used, then computational versatility is improved, but tracking overall data confidence becomes difficult
Solution Approach 1:
The patent applies segmentation by dividing the computational graph into distinct classical and quantum segments, each maintaining its own confidence tracking mechanisms. Confidence scores are independently tracked in classical segments and quantum segments, then aggregated at interface points. This segmented approach preserves confidence information throughout the hybrid graph without requiring a monolithic tracking system, thereby maintaining versatility while preventing information loss.
Data Source
AI summary
One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.