Hybrid Quantum-Classical System for Bias-Free Data Sketching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computers are inefficient in solving optimization problems and classifying large data sets, as they require significant resources and time, and existing methods for sketching large data sets can introduce bias through subsampling.
Innovation Solution
A hybrid quantum-classical system is employed to create and evaluate clustered data sets, using physics-based clustering and quality metrics to ensure that the data sets meet specific criteria, with a quantum processor aiding in the evaluation and reclustering process to enhance data representation and reduce bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computers are used to solve optimization problems and classify large data sets, then the problems can be solved using existing technology, but the computation requires significant resources and time
Solution Approach 1:
The patent combines quantum computing and classical computing systems into a hybrid architecture. The quantum processor handles specific computational tasks (optimization problems, data classification) while the classical processor manages other operations, creating a synergistic system that leverages the strengths of both computing paradigms to improve productivity while managing resource requirements
Solution Approach 2:
The patent replaces traditional classical computing mechanisms with quantum computing mechanisms for specific computational tasks. Quantum processors use quantum mechanical phenomena (superposition, entanglement) to perform computations that would require excessive classical computational resources and time, thereby improving productivity for particular problem types
2Quantity of substance
If existing methods for sketching large data sets are used, then the data set size is reduced for processing, but bias is introduced through subsampling
Solution Approach 1:
The quantum processor evaluates multiple clustered data sets simultaneously using quantum parallelism, allowing the system to self-assess different clustering configurations and select the optimal one. This self-evaluation capability ensures that the reduced data set maintains high reliability and accuracy by objectively identifying the best clustering solution without introducing bias from traditional subsampling methods
3Productivity
If a quantum processor is used to evaluate multiple clustered data sets simultaneously, then the evaluation speed increases, but the system complexity increases
Solution Approach 1:
The patent divides the computational workload into distinct segments handled by different processors. The quantum processor is responsible specifically for evaluating clustered data sets using quantum parallelism, while the classical processor handles data preparation, clustering algorithm execution, and result interpretation. This segmentation allows the system to achieve high evaluation speed through quantum acceleration while managing complexity through clear functional separation
Data Source
AI summary
In an embodiment, a method of sketching using a hybrid quantum-classical system includes creating a set of clustered data sets from a first data set. In an embodiment, the method includes evaluating, using a quantum processor and quantum memory, the set of clustered data sets. In an embodiment, the method includes evaluating, using the quantum processor and quantum memory, a set of quality metrics for the set of clustered data sets. In an embodiment, the method includes reclustering, responsive to at least one of the set of quality metrics failing to meet a quality criterion, the first data set.