Finance

Supply Chain

Data Analysis

Clustering

Many industries can benefit from extracting useful information and insights hidden within complex networks. For example, a finance company that wants to identify suspicious transactions or anomalous documents, a healthcare insurance company that needs to detect fraudulent claims, or a retail company that wants to discover patterns of consumption. All these problems can be formulated as community detection or clustering on graphs.

Finding communities or clusters within an arbitrary network can be a computationally difficult task, where the number of clusters is normally unkown and the communities are often of unequal size and density.

Potential Benefits of quantum-inspired:

Increased quality of community structures.

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Detection of hard abnormal instances.

Faster processing for large networks.

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