On the Baltimore Light RailLink into the quantum future.

Area of Science:

• Quantum Computing
• Operations Research
• Transportation Systems

Background:

• Noisy Intermediate-Scale Quantum (NISQ) devices offer potential for complex optimization problems, including NP-hard challenges.
• Current quantum methods often underperform classical solvers, necessitating innovative approaches.
• Leveraging inherent quantum noise is an underexplored strategy for enhancing computational power.

Purpose of the Study:

• To demonstrate the use of quantum noise as a computational resource for optimization problems.
• To showcase the effective application of NISQ devices for real-world problem-solving.
• To explore the feasibility of quantum computing for modeling stochastic disruptions in transportation networks.

Main Methods:

• Utilized a D-Wave quantum annealer and IonQ's gate-based NISQ computers.
• Generated and analyzed solutions for train traffic management under stochastic disturbances.
• Focused on the Baltimore Light RailLink as a case study for tramway and railway networks.

Main Results:

• Successfully applied both quantum computing paradigms (annealing and gate-based) to a real-world transportation rescheduling problem.
• Demonstrated the potential of leveraging quantum noise for effective optimization.
• Showcased the feasibility of modeling stochastic disruptions using NISQ technology.

Conclusions:

• Quantum noise can be effectively leveraged as a computational resource in NISQ devices for optimization.
• This research represents the first application of both quantum annealing and gate-based NISQ computing to tramway and railway rescheduling.
• NISQ technology holds promise for addressing complex, real-world optimization challenges in transportation and beyond.