Quantum Slime Mould Algorithm and Application to Urgent Transportation

The Slime Mould Algorithm (SMA) is a Swarm Intelligence (SI) technique inspired by the foraging behavior of slime moulds in nature. SMA demonstrates promise as an optimization method and researchers are actively exploring ways to improve its performance, including integrating hybrid strategies. Concurrently, quantum computing (QC) has emerged as a rapidly expanding research area, leveraging quantum mechanics principles for computation. This study presents QSMA, a quantum version of the slime mould algorithm, which combines classical SMA with the quantum Grover's algorithm. We apply this algorithm to the ambulance dispatching problem, a critical challenge in emergency medical services, where the objective is to allocate ambulances to respond to emergency calls efficiently. Our implementation of QSMA using the IBM Qiskit simulator is compared with the classical approach. This evaluation is performed using real-world COVID-19 data from Chicago. The experimental analysis reveals that the proposed quantum algorithm for ambulance dispatching is highly competitive, exhibiting significant improvements and converging to the optimal solution faster than the classical algorithm.