Strategizing distributed systems using quantum simulation

A method comprising;

• at a computing device comprising at least one processor, a communication interface, and memory:
• training a quantum simulation model (QSM) based on a plurality of historical execution scenarios and a plurality of historical optimization scores corresponding to the plurality of historical execution scenarios, wherein training the QSM configures the QSM to output optimization scores based on input of source code matrices and input of configuration matrices;
• generating a digital twin of a distributed software system (DSS), wherein the digital twin comprises a virtual representation of the DSS;
• generating, by parsing source code corresponding to the DSS, a directed acyclic graph (DAG) of the source code;
• generating, using the DAG, a plurality of encoded vectors of the source code and a plurality of distribution assessments, wherein each distribution assessment comprises a potential organization of a plurality of associated encoded vectors, of the plurality of encoded vectors, for performing a function;
• generating, based on the plurality of distribution assessments, a source code matrix comprising information of the plurality of distribution assessments;
• generating, based on the digital twin, a plurality of change vectors, wherein each change vector comprises configuration information corresponding to the digital twin, and a configuration matrix comprising information of the plurality of change vectors;
• generating, based on inputting the source code matrix and the configuration matrix into the QSM, a plurality of execution scenarios for executing the source code and a plurality of optimization scores, wherein each optimization score corresponds to a different execution scenario of the plurality of execution scenarios;
• identifying an optimal execution scenario;
• causing, based on the optimal execution scenario, one or more updates to the DSS;
• and
• updating, based on the optimal execution scenario, the QSM.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train a quantum simulation model (QSM) based on a plurality of historical execution scenarios and a plurality of historical optimization scores corresponding to the plurality of historical execution scenarios, wherein training the QSM configures the QSM to output optimization scores based on input of source code matrices and input of configuration matrices;
• generate a digital twin of a distributed software system (DSS), wherein the digital twin comprises a virtual representation of the DSS;
• generate, by parsing source code corresponding to the DSS, a directed acyclic graph (DAG) of the source code;
• generate, using the DAG, a plurality of encoded vectors of the source code and a plurality of distribution assessments, wherein each distribution assessment comprises a potential organization of a plurality of associated encoded vectors, of the plurality of encoded vectors, for performing a function;
• generate, based on the plurality of distribution assessments, a source code matrix comprising information of the plurality of distribution assessments;
• generate, based on the digital twin, a plurality of change vectors, wherein each change vector comprises configuration information corresponding to the digital twin, and a configuration matrix comprising information of the plurality of change vectors;
• generate, based on inputting the source code matrix and the configuration matrix into the QSM, a plurality of execution scenarios for executing the source code and a plurality of optimization scores, wherein each optimization score corresponds to a different execution scenario of the plurality of execution scenarios;
• identify an optimal execution scenario;
• cause, based on the optimal execution scenario, one or more updates to the DSS;
• and
• update, based on the optimal execution scenario, the QSM.