receiving input of a number of nodes, wherein each node represents a computational sub-task, and wherein the nodes are grouped into different sets according to differing computing resources used by the nodes, wherein the differing computing resources include classical computing resources and quantum computing resources;

receiving a computational objective for a modeling application;

receiving initial data inputs and desired final outputs;

generating a directed graph network comprising the nodes and directed edges connecting the nodes;

solving an optimization problem to determine a best path through the directed graph network for deriving the desired final outputs from the initial data inputs according to the computational objective, wherein the best path comprises a subset of nodes and directed edges within the directed graph network, and wherein the subset of nodes represents an allocation of computational sub-tasks between classical computing resources and quantum computing resources;

running the optimization problem multiple times with varying parameters to determine an optimal transition between classical and quantum hardware types; and

executing a full end-to-end computational process according to the best path to simulate hardware behavior of the directed graph network.