Analog Processor Graph Embedding for Database Query Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently solving computational problems beyond the capabilities of Universal Turing Machines, particularly in managing qubit parameter control for scalable quantum processors and optimizing graph embeddings, and in effectively querying relational databases with traditional methods.
Innovation Solution
The use of analog processors, specifically quantum processors, to evolve into a final state representative of a clique of an association graph, allowing for efficient database query solving by embedding query and database graphs and ranking responses based on responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional digital computers are used to query relational databases, then database queries can be executed, but computational problems beyond Universal Turing Machine capabilities cannot be solved efficiently and query responsiveness is limited
Solution Approach 1:
The patent replaces traditional digital computer architecture with quantum mechanical systems. Quantum processors use quantum bits (qubits) that leverage quantum superposition and entanglement to perform computations. The system embeds database relations and queries into quantum graphs, where qubits represent entities and edges represent relationships. Quantum algorithms process these encoded problems using quantum mechanical evolution, enabling solutions to complex computational problems that are intractable for classical computers, thereby improving productivity and reducing query response time.
2Adaptability or versatility
If quantum processors are used to solve computational problems, then problems beyond UTM capabilities can be solved, but qubit parameter control complexity increases with scalability
Solution Approach 1:
The patent divides the quantum processor into modular components: qubit units representing entities, coupling devices representing relationships, and controller subsystems managing parameters. Each qubit has associated control lines for parameter adjustment, and coupling devices have independent control for interaction strength. This segmentation allows independent control and optimization of individual components, making the overall system more manageable despite increasing scalability. The controller subsystem coordinates these modular controls to embed problems into quantum graphs and execute algorithms.
Solution Approach 2:
The system dynamically adjusts qubit parameters such as energy levels, coupling strengths, and interaction times to solve different computational problems. By changing these parameters, the quantum processor can embed various database relations and query types into appropriate quantum graph structures. This parameter flexibility enables the same hardware to adapt to different computational tasks without requiring physical reconfiguration, thereby managing complexity while maintaining high adaptability.
3Productivity
If graph embedding is used to represent database queries, then query processing can be optimized, but embedding complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-defining the quantum graph structure and embedding rules before query execution. Database relations are pre-encoded into the quantum graph topology, with qubits and coupling devices arranged to represent entities and relationships. When a query arrives, the system only needs to activate the relevant portions of the pre-configured graph rather than building the entire embedding from scratch. This preliminary preparation significantly reduces the computational overhead during actual query processing while maintaining optimized performance.
Data Source
AI summary
Systems, methods and articles solve queries or database problems through the use of graphs. An association graph may be formed based on a query graph and a database graph. The association graph may be solved for a clique, providing the results to a query or problem and/or an indication of a level of responsiveness of the results. Thus, unlimited relaxation of constraint may be achieved. Analog processors such as quantum processors may be used to solve for the clique.