A Novel Hybrid Quantum Architecture for Path Planning in Quantum-Enabled Autonomous Mobile Robots

Autonomous mobile robots are being used increasingly as consumer devices around the globe, such as for fetching items and cleaning purposes, to name a few, in households and industries. Such robots are being employed to traverse a set of target locations and provide the necessary services. In a 2-dimensional environment, these robots are required to traverse following a Hamiltonian path to reduce energy consumption and time requirements. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, upon urgent requirements, these robots must traverse in real-time, demanding speedy path planning from the TSP instance. Among the well-known optimization techniques for solving the TSP problem, Ant Colony Optimization has a good stronghold in providing good approximate solutions. Moreover, ACO not only provides near-optimal solutions for TSP instances but can also output optimal or near-optimal solutions for many other demanding hard optimization problems. However, most of the implementations of Ant Colony Optimization on quantum or hybrid quantum architecture proposed in the literature require conversion of classical data to qubits before being fed to the algorithm, and cannot be automated. But quantum-enabled mobile robots require automated path formation after receiving the commands from the environment. The novelties of the proposed work are many-fold. Firstly, the proposed work allows ACO to be applied as its classical counterparts, allowing automation in path formation in quantum-enabled mobile robots. Secondly, this allows a new way of incorporating quantum processing unit in the research of quantum-enabled mobile robots. Researchers around the globe have been trying to incorporate quantum computing in autonomous mobile robots, and true to the best of authors' knowledge, no work in path planning for multiple targets in quantum-enabled mobile robots have been found in literature. Thirdly, quantum processing unit has been applied at exactly that point where it will be most useful, as in NISQ era quantum computer is not reliable for arithmetic processing. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results with average error percentage from optimum results of only 6.985%. Hence, it is expected that the proposed work to be important in future research of fusing the two rising domains of quantum computing and autonomous mobile robots.