Digital Twin Generation Using Quantum-Inspired Algorithms
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Solution Overview
Problem
Existing methods struggle to generate real-time digital twins of complex physical systems due to high latency and the inability to accurately mimic their behavior, particularly in systems with numerous components and parameters.
Innovation Solution
A method utilizing quantum-inspired algorithms on classical computing devices to process data from physical systems, enabling rapid generation of high-quality digital twin data without requiring quantum computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum-inspired algorithms are used on classical computing devices, then processing speed and data quality improve, but computational complexity increases
Solution Approach 1:
The patent replaces traditional quantum computing hardware with quantum-inspired algorithms that run on classical computing devices. This substitution allows the system to achieve quantum-like processing speed and accuracy without requiring actual quantum hardware, thereby improving productivity while managing computational complexity through software-based simulation of quantum phenomena.
2Measurement precision
If digital twins are generated for complex physical systems with numerous components and parameters, then accuracy of behavior simulation improves, but generation time and latency increase
Solution Approach 1:
The patent applies quantum-inspired algorithms to pre-process and optimize the generation of digital twin data before actual simulation needs arise. By using these advanced algorithms in advance, the system prepares high-accuracy behavioral data that can be quickly deployed when needed, thereby improving simulation accuracy while reducing generation time through proactive computation.
Solution Approach 2:
The patent transforms complex physical system parameters into optimized digital representations using quantum-inspired computational methods. This parameter transformation process efficiently handles numerous components and parameters by applying quantum-like optimization techniques, achieving high behavioral accuracy while maintaining reduced generation time through intelligent parameter mapping and dimensionality reduction.
Data Source
AI summary
Data acquirable from a physical system is received and processed by at least one classical computing device to provide data indicative of a digital twin of the physical system by digitally computing a quantum-inspired algorithm. The data indicative of the digital twin is indicative of a reconfiguration of a physical parameter of the physical system, and the physical parameter has a reconfigurable digital counterpart in the digital twin.