Hybrid Quantum-Classical Search for Secure Data Retrieval
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Solution Overview
Problem
Organizations face challenges in accessing and interpreting confidential data elements, logic, and derivative formulae used in reports due to regulatory restrictions, requiring a method for selective and secure data retrieval without direct access to sensitive databases.
Innovation Solution
A hybrid computing system combining classical and quantum computing enables access-less data retrieval through multi-thread processing, using Grover's search processes and smart contracts within blockchain ledgers, allowing for parallel execution and real-time data interpretation without revealing data storage locations or systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical computing is used for data retrieval, then the system is simple to operate, but it cannot perform simultaneous and dynamic computing with multiple parallel searches in real-time
Solution Approach 1:
The patent introduces a hybrid computing system that acts as an intermediary between classical computing simplicity and quantum computing power. The quantum processing unit is embedded within a classical computing architecture, allowing the system to leverage quantum parallel search capabilities while maintaining classical system interfaces and operational simplicity. This mediator approach enables multiple parallel searches to execute simultaneously without requiring the end user to directly manage quantum system complexity.
2Ease of operation
If users are granted direct access to confidential databases, then data retrieval is straightforward, but regulatory restrictions prevent access to confidential data elements
Solution Approach 1:
The patent extracts only the necessary data elements from confidential databases through quantum surgical search, rather than providing users with broad database access. The quantum processing unit identifies and retrieves specific data points matching user queries without exposing the underlying database structure, confidential data elements, or storage locations. This extraction approach maintains regulatory compliance by preventing access to confidential information while still delivering the required data retrieval functionality.
3Loss of information
If quantum computing is used for surgical search, then access-less data retrieval is achieved, but the computing system becomes more complex
Solution Approach 1:
The patent segments the computing system into distinct functional components: a classical computing portion that handles user interfaces, data storage, and system management, and a quantum processing unit that performs specialized surgical search operations. This segmentation allows each component to be optimized independently while maintaining clear boundaries that manage overall system complexity. The quantum processing unit is isolated to specific search functions, preventing quantum complexity from propagating throughout the entire system architecture.
4Productivity
If multiple Grover's search processes are executed in parallel, then search efficiency increases, but the computational resources required increase
Solution Approach 1:
The patent implements a controlled parallel execution strategy where multiple Grover's search processes are launched simultaneously but with varying degrees of resource allocation. The system dynamically adjusts the number and intensity of parallel search threads based on query complexity and available quantum resources. For simple queries, fewer parallel processes are used, while complex queries requiring broader search spaces receive more parallel processing power. This partial action approach ensures high search efficiency is achieved when needed without consistently consuming maximum quantum computational resources.
Data Source
AI summary
Systems and methods for confidential database surgical search and access-less retrieval using a hybrid-computing-powered system with multi-thread processing are provided. The systems and methods may include a quantum processor and a classical processor. The systems and methods may include requesting data elements pertinent to reports. The systems and methods may include authenticating requests and creating classical request strings via a classical processor. The systems and methods may include interfacing classical request strings with a quantum processor. The systems and methods may include running Grover's searches in parallel over the request strings. The systems and methods may include fetching data from smart contracts within ledgers and sub-ledgers within a blockchain. The systems and methods may include pulling dynamic market data through a legacy transformation platform including dynamically derived data values and a machine learning model (MLM).