Edge Quantum Computing Latency Reduction
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Solution Overview
Problem
The increasing number and complexity of computations in IoT devices, smart transportation, and smart cities lead to significant processing time and latency when using classical processing at central clouds, degrading application performance.
Innovation Solution
Distributing application processing by network location and type, enabling quantum processing at edge nodes, where a machine learning component determines whether to process applications at a central cloud or edge node and whether to use classical or quantum computing, thereby reducing latency and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If classical processing at central cloud is used, then processing capacity is sufficient, but latency increases and application performance degrades
Solution Approach 1:
The patent segments the centralized cloud processing architecture into distributed edge computing nodes positioned geographically closer to end devices. This segmentation reduces the distance data must travel, thereby reducing latency while distributing computational tasks across multiple edge nodes rather than a single centralized cloud, balancing processing capacity with response time requirements.
Solution Approach 2:
The patent introduces a new spatial dimension to the processing architecture by deploying edge computing nodes at intermediate locations between central cloud and end devices. This creates a multi-layered processing hierarchy (central cloud → edge nodes → end devices) that adds geographical proximity as a new dimension for optimizing latency without sacrificing processing capacity.
2Measurement precision
If quantum processing at edge node is used, then computational accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies quantum processing selectively at specific edge nodes rather than universally across all processing locations. The machine learning component determines which applications require quantum processing based on their specific computational requirements, applying quantum resources locally only where needed to achieve higher accuracy while avoiding unnecessary quantum processing for simpler tasks.
Solution Approach 2:
The patent implements partial quantum processing by using quantum computing resources for only the portions of applications that benefit from quantum algorithms, while classical processing handles other portions. This partial application of quantum processing achieves accuracy improvements for critical computations without requiring full quantum processing for all applications, thereby managing complexity more effectively.
3Productivity
If machine learning component determines processing location and type, then resource allocation optimizes, but processing overhead increases
Solution Approach 1:
The machine learning component operates autonomously at each edge node, making independent decisions about processing location and type based on local conditions and application requirements. This self-service capability allows the system to dynamically optimize resource allocation without requiring centralized control for every decision, improving efficiency while distributing the computational overhead of the machine learning component across multiple edge nodes.
Data Source
AI summary
Systems and methods are described for enabling quantum computing at an edge node of a network. For example, a machine learning component residing on each of a plurality of edge nodes of the network may be implemented to distribute application processing by network location and processing type, including distribution among classical processing at a central cloud, classical processing at an edge node, and quantum processing at a quantum edge node including a quantum computing device. By distributing certain applications, such as latency-sensitive applications of a higher order of complexity, to an edge node, and particularly a quantum edge node, latency may be reduced and complex application code may be processed more quicky using quantum computations. For applications to be processed using quantum processing, the machine learning component may further identify qubits for the quantum processing and define containers based on the qubits for deployment by the quantum computing device.