Abstract

Quantum computing has revolutionized our approach to complex problems like resource allocation, scheduling, data analysis, and design optimization. With high processing power, quantum computers provide significant speed-ups for these tasks [1]. IBM has allotted public access to several quantum computers for running quantum circuits. However, one quantum computer requirement is the mapping of quantum circuits onto the computer-specific hardware architecture. IBM’s Transpiler function optimizes the mapping of a quantum circuit to a quantum computer using one of four optimization levels, 0 to 3, selecting level 1 as its default, which may be ineffective. For this reason, this research was initiated to develop a selection function that could determine the best optimization level for any quantum circuit. Using the quantum circuit complexity-based benchmarking algorithm, circuit data was collected from IBM's quantum computers. This data is used to develop the Selector Function, which maximizes accuracy and time efficiency while minimizing cost. The Selector Function decomposes any given quantum circuit into the majority of key components necessary for determining the best optimization level and then cross-references with the collected data to retrieve the key missing value. The Selector Function has achieved 91.16% accuracy compared to the control data from running the given quantum circuit on all of the optimization levels on real quantum hardware, over a breadth of 1,000 quantum circuits. Moreover, the Selector Function recorded an average runtime of only 0.08 seconds, while the control, with queue times disregarded, recorded an average runtime of 216 seconds. The project significantly improves the optimization process for mapping quantum circuits onto specific hardware architectures, allowing quantum computing to move towards more advanced and efficient solutions by directly aiding researchers.