Efficient Implementation of Nearest Neighbor Quantum Circuits Using Clustering with Genetic Algorithm

Although quantum computing has made tremendous progress in last couple of years and technologies like NMR, Ion Trap, superconducting qubits have come out as promising platforms to implement quantum computing devices, such technologies are facing several design constraints. One such constraint is the Nearest Neighbor (NN) property, which demands the adjacency of logical qubits. Aiming to contribute to this cause, here we are proposing an improved design approach for transforming quantum circuits for NN-based architectures using genetic algorithms. In this work, our primary objective remains to form efficient NN structures by restricting the SWAP usage. In the design phase, initially, we use the k-means clustering scheme for partitioning the qubits into separate objects and, then, a genetic algorithm is applied that eventually fixes the order of qubits for each individual cluster. In the final phase, all these local solutions are combined and, again, a genetic algorithm is employed to obtain a global solution. We have tested our approach over a large spectrum of benchmarks and improvements are registered over some state-of-the-art design works.