Ion-Trapping Quantum Task Execution With Edge Query Summaries
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Solution Overview
Problem
The proliferation of data from IoT sensors and other sources in value chain networks overwhelms traditional centralized data collection methods, leading to complexity and inefficiencies in data transmission and automated decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, where queries are stored on a dynamic ledger, generating approximate responses based on summary data, and transmitting these responses, with the option to use a blockchain for data storage and a neural network for probability distribution modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If centralized data collection methods are used to gather data from IoT sensors, then comprehensive data can be obtained, but network overhead and complexity increase significantly
Solution Approach 1:
The patent divides the centralized data collection system into distributed edge computing nodes deployed across the value chain network. Each edge device independently processes local data, eliminating the need for a single centralized collection point and reducing network overhead while maintaining comprehensive data gathering capabilities.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing hierarchical data processing with edge devices at the network edge and summary data aggregation at higher levels. This multi-dimensional architecture allows comprehensive data collection without the complexity of traditional centralized systems.
2Loss of information
If all raw data is transmitted through the network for processing, then complete information is available, but network bandwidth and transmission efficiency decrease
Solution Approach 1:
The patent extracts only the essential summary data from raw sensor data at the edge devices. Instead of transmitting all raw data, edge devices process and extract key insights, transmitting only these condensed summaries to the central system, thereby maintaining information completeness while dramatically reducing network bandwidth consumption.
Solution Approach 2:
The patent implements partial data transmission by sending only summary statistics and aggregated insights rather than complete raw data sets. This partial action approach maintains sufficient information for decision-making while optimizing network bandwidth utilization.
3Measurement precision
If traditional centralized processing is used for data analysis, then comprehensive analysis can be performed, but decision-making time increases
Solution Approach 1:
The patent implements preliminary data processing and summary generation at edge devices before data reaches the central system. This preliminary action prepares data in advance, allowing faster central processing and reducing overall decision-making time while maintaining analysis accuracy through pre-processed quality data.
Solution Approach 2:
The patent introduces summary data as an intermediary between raw sensor data and final analysis. This intermediary layer pre-processes and condenses information, enabling faster central system processing while maintaining the precision needed for accurate decision-making.
4Productivity
If edge devices generate and store summary data locally, then processing speed improves, but device memory requirements increase
Solution Approach 1:
The patent changes the parameters of data representation by storing compressed summary statistics rather than raw data. This parameter transformation reduces storage requirements at edge devices while maintaining the processing speed benefits of local data availability.
Solution Approach 2:
The patent implements a strategy where summary data is generated, used for immediate processing, and then discarded or overwritten. This approach allows edge devices to maintain fast processing capabilities without long-term storage burdens, as summary data serves its purpose quickly and can be replaced by new summaries.
Data Source
AI summary
A computer-implemented method for executing a quantum computing task includes providing a quantum computing system. The computer-implemented method includes receiving a request, from a quantum computing client, to execute a quantum computing task via the quantum computing system. The computer-implemented method includes executing the requested quantum computing task via the quantum computing system. The executing the requested quantum computing task includes trapping a set of ions. The computer-implemented method includes returning a response related to the executed quantum computing task to the quantum computing client.