Simulating multilevel quantum systems using the density matrix formalism involves solving large systems of coupled differential equations, often requiring computationally intensive methods such as Runge–Kutta, Monte Carlo, or physics-informed neural networks. These solvers demand high-performance computing resources because of the complexity and size of the underlying matrices. This study evaluates key aspects of simulation performance, such as numerical accuracy, scalability, parallel efficiency, or resource utilization, to develop efficient computational strategies. To address infrastructure overhead and improve accessibility, a Function-as-a-Service-based platform is proposed to enable elastic, on-demand execution of quantum simulation workflows. A monitoring and orchestration strategy is integrated into the platform to optimize resource use across varying problem dimensionalities. By abstracting the complexity of traditional high-performance computing environments, the proposed Function-as-a-Service-based approach facilitates efficient and scalable quantum simulations while reducing user-side infrastructure management. The results provide a foundation for designing accessible, high-fidelity simulations for complex quantum systems on modern cloud-based or hybrid high-performance computing platforms.
Arzumanyan et al. (Mon,) studied this question.