A novel quantum algorithm for ant colony optimisation

Ant colony optimisation (ACO) is a commonly used meta-heuristic to solve complex combinatorial problems like the travelling salesman problem (TSP), vehicle routing (VRP) etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead significant extent. Algorithmic speed-up can be achieved by using parallelism offered quantum computing. Existing are either quantum-inspired or hybrid quantum-classical algorithms. Since all these need intervention of computing, leveraging true potential computing on real hardware remains challenge. This study's main contribution propose fully algorithm ACO, enhancing information processing toolbox in fault-tolerant (FTQC) era. We have solved single source destination (SSSD) shortest-path our proposed adaptive circuit for representing dynamic pheromone-updating strategy IBMQ devices. Our technique further as ORACLE setup with speed up upon availability more qubits.