Hybrid AI System with Quantum-Classical Segmentation
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Solution Overview
Problem
Current systems lack an effective method to combine classical and quantum computing components into a hybrid system that leverages the strengths of both, particularly for complex problem-solving and artificial intelligence applications, where classical computers face limitations in processing difficult computations and quantum computers are not yet operational as universal machines.
Innovation Solution
A hybrid AI system is proposed, where a plurality of quantum computers are embedded within a classical computing framework, with each quantum computer programmed by a local classical computer, decomposing larger problems into smaller ones, and each node in the network serving as a solver for these smaller problems, utilizing classical computers to direct quantum computations and coordinate timing through an external controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computers are used to process difficult computations, then computation speed and efficiency are improved, but device complexity and operational reliability deteriorate due to current quantum computing limitations
Solution Approach 1:
The system divides computation tasks into segments: classical computers handle routine operations while quantum computers handle specific difficult computations. This segmentation allows leveraging quantum computational power without requiring the entire system to be quantum, thus improving productivity while managing device complexity.
Solution Approach 2:
A hybrid classical-quantum interface acts as an intermediary, translating classical computational problems into quantum operations and back. This mediator layer enables classical systems to utilize quantum computing power without direct quantum hardware complexity, resolving the contradiction between improved computation speed and system complexity.
2Adaptability or versatility
If quantum computers are integrated into classical computing frameworks, then problem-solving capability is improved, but system reliability deteriorates due to current quantum computing noise and error rates
Solution Approach 1:
The system employs quantum computers for partial computations only where their capabilities provide advantage, rather than requiring full quantum reliability. By using quantum processing selectively for specific problem portions, the system gains enhanced problem-solving capability while mitigating reliability issues through continued dependence on stable classical computing for critical operations.
3Device complexity
If classical computers are used alone for complex computations, then system simplicity is maintained, but processing time and energy consumption increase
Solution Approach 1:
The patent merges classical and quantum computing systems into a hybrid architecture. This combination maintains the simplicity and reliability of classical computers while integrating quantum processing power for complex computations, thus reducing processing time without completely sacrificing system simplicity.
Solution Approach 2:
The hybrid system creates a universal computing platform that can handle both routine classical computations and complex quantum-accelerated tasks. This multi-functionality allows the system to maintain simplicity for standard operations while gaining reduced processing time for complex problems through quantum capabilities.
Data Source
AI summary
An artificial intelligence node including a classical computer, an input channel and an output channel in communication with the classical compute and a quantum computer in two-way communication with the classical computer, wherein the classical computer configures the quantum computer to encode a plurality of word combinations.