  In the current era of noisy intermediate-scale quantum (NISQ) technology,
quantum devices present new avenues for addressing complex, real-world
challenges including potentially NP-hard optimization problems. This work aims
to showcase how the inherent noise in NISQ devices can be leveraged to solve
such real-world problems effectively. Utilizing a D-Wave quantum annealer and
IonQ's gate-based NISQ computers, we generate and analyze solutions for
managing train traffic under stochastic disturbances. Our case study focuses on
the Baltimore Light RailLink, which embodies the characteristics of both
tramway and railway networks. We explore the feasibility of using NISQ
technology to model the stochastic nature of disruptions in these
transportation systems. Our research marks the inaugural application of both
quantum computing paradigms to tramway and railway rescheduling, highlighting
the potential of quantum noise as a beneficial resource in complex optimization
scenarios.
