Hybrid Quantum-Classical Pattern Generation System
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Solution Overview
Problem
Classical computing systems are resource-intensive and costly, struggling to process large volumes of data in real time for pattern recognition, which is essential in various applications, and there is a lack of effective techniques to address this challenge.
Innovation Solution
A system and method utilizing both classical and quantum computing systems, where classical processors preprocess data to generate intermediary patterns and quantum processors further process these patterns to identify correlations and generate final patterns, leveraging the capabilities of quantum computing for fast and accurate real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical computing systems are used for pattern recognition, then pattern recognition can be performed, but the systems are resource-intensive and costly
Solution Approach 1:
The patent combines classical computing systems and quantum computing systems into a hybrid architecture. The classical processor handles data preprocessing and generates intermediary patterns, while the quantum processor performs complex pattern recognition on these intermediary patterns. This merging allows the system to leverage the strengths of both computing paradigms, achieving accurate pattern recognition with reduced resource consumption compared to using only classical systems.
2Quantity of substance
If classical computing systems process huge volume of data, then data processing can be performed, but it becomes impossible to generate patterns in real time
Solution Approach 1:
The patent segments the pattern recognition process into two distinct stages handled by different computing systems. The classical processor segments the task of handling huge data volumes through preprocessing and generating intermediary patterns, while the quantum processor segments the task of identifying complex correlations and generating final patterns in real time. This segmentation allows each system to operate within its optimal performance range, enabling real-time pattern generation even for huge data volumes.
3Ease of operation
If classical computing systems are used, then computations can be performed, but the computations are highly resource intensive and costly
Solution Approach 1:
The patent introduces intermediary patterns as a mediator between the classical and quantum processing stages. The classical processor generates these intermediary patterns from the raw data, which then serve as input for the quantum processor. This intermediary representation simplifies the computation task for the quantum system, reducing the complexity and resource requirements while maintaining the ease of operation for both computing systems.
4Speed
If quantum computing systems are used for processing, then fast processing can be achieved, but the system complexity increases
Solution Approach 1:
The patent segments the overall processing system into two distinct parts: a classical computing component and a quantum computing component. The classical processor handles routine preprocessing tasks with well-established algorithms, while the quantum processor handles only the computationally intensive pattern recognition tasks that require quantum speedup. This segmentation allows the system to achieve fast processing for critical tasks while keeping the overall system architecture manageable through clear division of labor.
Data Source
AI summary
The present disclosure describes a method and system for generating at least one pattern from at least one data set using quantum computing. The system comprises at least one quantum processor; and at least one non-quantum processor operatively coupled to the at least one quantum processor and configured to process the at least one data set to generate at least one first intermediary pattern comprising at least one first keyword and transmit the generated pattern(s) to the quantum processor(s). The quantum processor(s) is configured to receive the data set(s) and the first intermediary pattern(s); process the data set(s) to generate at least one second intermediary pattern and at least one corresponding metadata; and process the first intermediary pattern(s) and the at least one second intermediary pattern(s) to generate the at least one final pattern.