System and method for dynamically analyzing and configuring nodes of a distributed network leveraging photonic quantum computing

A computer program product dynamically analyzing and configuring nodes of a distributed network leveraging photonic quantum computing, the computer program product comprising at least one non-transitory computer readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising executable portions for:

• identifying one or more nodes of a distributed register network;
• extracting metadata from the one or more nodes;
• continuously monitoring the one or more nodes to detect changes to the one or more nodes;
• parsing the metadata and the changes to a deep learning network;
• determining, via the deep learning network, anomalies associated with the one or more nodes based on the changes;
• mapping the anomalies to target metrics and factors, wherein each anomaly of the anomalies is mapped to one or more corresponding factors, wherein the factors define changes in processing capacity of the one or more nodes caused by the anomalies, wherein the target metrics are impacted by the anomaly;
• creating simulation test scenarios based on the target metrics, the factors, and the anomalies to generate a plurality of configurations for the one or more nodes, wherein creating the simulation test scenarios comprises varying the factors in at least two levels for each of the target metrics to generate the plurality of configurations for the one or more nodes;
• executing the simulation test scenarios in parallel, via a quantum computer;
• and
• determining an optimal configuration for the one or more nodes from the plurality of configurations based on executing the simulation test scenarios.

A computer-implemented method for dynamically analyzing and configuring nodes of a distributed network leveraging photonic quantum computing, the method comprising:

• identifying one or more nodes of a distributed register network;
• extracting metadata from the one or more nodes;
• continuously monitoring the one or more nodes to detect changes to the one or more nodes;
• parsing the metadata and the changes to a deep learning network;
• determining, via the deep learning network, anomalies associated with the one or more nodes based on the changes;
• mapping the anomalies to target metrics and factors, wherein each anomaly of the anomalies is mapped to one or more corresponding factors, wherein the factors define changes in processing capacity of the one or more nodes caused by the anomalies, wherein the target metrics are impacted by the anomaly;
• creating simulation test scenarios based on the target metrics, the factors, and the anomalies to generate a plurality of configurations for the one or more nodes, wherein creating the simulation test scenarios comprises varying the factors in at least two levels for each of the target metrics to generate the plurality of configurations for the one or more nodes;
• executing the simulation test scenarios in parallel, via a quantum computer;
• and
• determining an optimal configuration for the one or more nodes from the plurality of configurations based on executing the simulation test scenarios.