Quantum Atom Array for Scalable Graph Similarity Measurement
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Solution Overview
Problem
Existing methods for measuring similarity between graphs, especially those with a large number of nodes, are inefficient due to exponential computation time and limited adaptability to varying graph sizes, and existing quantum processing methods are not scalable for graphs with more than 10 nodes.
Innovation Solution
A method using an array of atoms modeled via optical trapping techniques to compute feature vectors for graphs, where atoms represent nodes and interactions represent edges, allowing for iterative measurement and evolution to determine a quantum state observable, enabling the encoding of graph structure and similarity measurement adaptable to graphs with various numbers of nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact methods (brute force) are used to compute graph isomorphism, then measurement precision is improved, but computation time increases exponentially
Solution Approach 1:
The patent replaces classical computational methods with quantum mechanical systems. Quantum circuits are used to compute graph similarity through quantum algorithms that leverage quantum superposition and interference, transforming the computational approach from classical brute-force methods to quantum-based approaches that achieve polynomial time complexity
Solution Approach 2:
The patent changes the fundamental parameter of computation from classical bits to quantum states. By encoding graph information into quantum states and using quantum operations to compute similarity, the system achieves exponential speedup compared to classical methods while maintaining measurement precision
2Loss of time
If existing quantum processing methods are used, then computation time is reduced, but adaptability to varying graph sizes is worsened
Solution Approach 1:
The patent designs a universal quantum circuit framework that can handle graphs of varying sizes. The quantum circuit incorporates adjustable parameters and scalable structures that allow it to process graphs with different numbers of nodes and edges, making the system adaptable to various graph sizes while maintaining efficient computation time
Solution Approach 2:
The patent implements dynamic adjustment capabilities in the quantum processing system. The quantum circuit can adapt its configuration based on the input graph characteristics, allowing flexible handling of graphs with varying sizes and complexities through dynamic parameter adjustment and scalable quantum operations
3Loss of time
If graph kernels are used to estimate similarity, then computation time is reduced, but measurement precision is worsened
Solution Approach 1:
The patent replaces classical graph kernel methods with quantum mechanical computations. By using quantum circuits to compute graph similarity, the system achieves both reduced computation time and maintained or improved measurement precision through quantum algorithms that provide more accurate similarity estimates while scaling efficiently
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more accurate and scalable method for measuring graph similarity, achieving better expressivity and accuracy in classification tasks, particularly for graphs with up to 10,000 nodes, with computation time increasing polynomially with the number of nodes.
Implementation Method 1
providing an array of atoms arranged to model the graph using optical trapping techniques
Implementation Method 2
sending, using an electromagnetic source, at least a first electromagnetic pulse with a first energy to the array during a first excitation time, in order to excite at least one atom from said at least first reference state to said at least first excited state
Data Source
AI summary
The present disclosure relates to a method for measuring a similarity between two graphs comprising: determining a feature vector for each of the two graphs; and calculating an estimated value of the similarity between the two graphs by comparing the two feature vectors, wherein, for each of the two graphs, determining a feature vector comprises: providing an array (120) of atoms arranged to model the graph (G1), wherein each atom of the array has a plurality of energy levels corresponding to at least a first reference state (a0) and at least a first excited state (a1); preparing the atoms of the array in the reference state (a0); applying an interaction sequence (137) to the array of atoms: detecting atoms of the array that are in the excited state (a1) to compute a value of an observable (O1) of the array of atoms; and determining a feature vector (f) based on a distribution of the plurality of values of said observable (O1_i).