Optical Gaussian Boson Sampling for Transaction Graph Anomaly Detection
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Solution Overview
Problem
Existing classical computer algorithms struggle to efficiently and accurately identify abnormal network events, such as fraudulent transactions in large and complex transaction networks, due to their inefficiency in scaling with network size and the impractical time required for detection.
Innovation Solution
Utilizing a quantum Gaussian Boson Sampling (GBS) device to map network activity into a complex graph problem, specifically identifying dense subgraphs in transaction graphs to detect anomalies like price manipulation by encoding inputs based on a transaction graph and processing outputs to identify dense subgraphs representative of potential anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical computer algorithms are used to detect abnormal network events, then detection can be performed with existing technology, but the detection time becomes impractically large and the algorithm does not scale efficiently with network size
Solution Approach 1:
The patent replaces classical computational systems with a quantum optical system (Gaussian Boson Sampling device) to perform graph analysis. The quantum device uses photonic interference patterns to naturally reveal dense subgraphs, substituting classical algorithmic processing with quantum mechanical phenomena to achieve exponential speedup in detecting abnormal network events.
2Adaptability or versatility
If the network size increases to provide more decentralized P2P transactions, then network decentralization and transaction capacity improve, but the complexity of identifying abnormal activity increases significantly
Solution Approach 1:
The quantum GBS device performs self-service analysis by naturally revealing dense subgraphs through photonic interference without requiring complex classical post-processing algorithms. The quantum system inherently performs the graph analysis function that would otherwise require sophisticated classical computational resources, enabling the system to serve itself in identifying abnormal patterns.
3Productivity
If classical algorithms compromise between efficiency and accuracy for detecting illicit activities, then some detection capability is maintained, but the trade-off results in impractically large detection times
Solution Approach 1:
The patent changes the fundamental parameter of computation from classical to quantum, utilizing quantum superposition and interference to process graph data. This parameter change enables the system to achieve both high efficiency and accuracy simultaneously, as the quantum device processes all possible subgraph configurations in parallel through quantum interference, naturally highlighting dense subgraphs without classical algorithmic trade-offs.
Data Source
AI summary
There is described a method of detecting an anomaly in a transaction network using an optical quantum-computing device. The method comprises obtaining transaction information. generating a transaction graph based on the transaction information. encoding inputs of a Gaussian Boson Sampling (GBS) device based on the transaction graph. processing outputs of the GBS device to identify one or more dense subgraphs of the transaction graph. and generating a detection output identifying one or more of the identified dense subgraphs as representative of a potential anomaly. There is also described an apparatus for detecting an anomaly. comprising a GBS device having a photon source. a linear optical interferometer and a photon detector. and a computer device configured to program inputs of the GBS device.