학술논문

Qubit Allocation Strategies in Quantum Computing for Improved Computational Efficiency
Document Type
Conference
Source
2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM) Innovative Practices in Technology and Management (ICIPTM), 2024 4th International Conference on. :1-6 Feb, 2024
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Runtime
Quantum algorithm
Error analysis
Heuristic algorithms
Qubit
Signal processing algorithms
Logic gates
Quantum Computing
Qubit Allocation
Algorithm Evaluation
Error Mitigation
Gate Fidelity
Language
Abstract
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.