Qubit Allocation Strategies in Quantum Computing for Improved Computational Efficiency

This research investigates qubit allocation strategies in quantum computing, evaluating the efficiency of four algorithms: Qubit Mapping Algorithm (QMA), Graph Coloring-Based Algorithm Graph Colorings Based Qubits, Dynamic Allocation of Qubits in Catalysts and Qubit Swapping Heuristic qi based on substitution. It evaluated these algorithms using simulated quantum environments, measured by their execution time, error rates, and gate fidelities. The results show that QMA can display competitive runtimes, low error rates (0.05), and high gate fidelities of 98%. However, GCBA has consistently been good with low error rates (0.07) and acceptable gate fidelities (0.94). To exhibit adaptability, DQAA provided results with low error rates (0.04) and high gate fidelities (0.96). QSH showed promising results with effective qubit reorganization, though a slightly higher error rate (0.08) and moderate gate fidelities (91%) have been seen. These results give answers about the advantages and disadvantages of each algorithm thereby contributing to qubits allocation understanding in quantum computing.