Elucidation of many chemical behaviors and properties depends on our ability to model the physics of electron-electron interactions at reasonable computational expense with correlation phenomena often being integral to predicting behaviors and properties of molecules and materials of contemporary interest. A particularly sought-after consequence of a type of correlation is superconductivity, which traditionally results from the Bose-Einstein-like condensation of Cooper (electron-electron) pairs. However, all currently-known superconductors condense at either too-low of temperatures or too-high of pressures to be commercially-viable. A possible solution to this limitation is the utilization of excitonic superfluidity arising from the condensation of particle-hole pairs (excitons) which are expected to condense at higher temperatures due to their decreased mass and which can result in the frictionless flow of excitation energy and---in bilayer systems---counterflow superconductivity. The first chapters of this text focus on the identification of the beginnings of exciton condensation in molecular-scale analogues of extended systems in an effort to contribute to rational design of molecularly-scaled exciton condensates. Then, the simulation of both Cooper pair and exciton condensates on quantum devices is described, which establishes a new avenue for the creation and characterization of condensation phenomena and is an important step toward more complex modeling of phenomena with significant quantum long-range order on quantum computers. The following chapters introduce fermion-exciton condensates (FECs)---novel quantum states that simultaneously exhibit the character of superconducting states and exciton condensates and may demonstrate hybrid properties of both. In this thesis, these FECs are computationally and theoretically predicted, described with a model Hamiltonian, and experimentally prepared on a quantum device. Finally, machine learning is used to reduce the many-electron problem to an effective two-electron problem, decreasing effective computational scaling with system size.