Quantum Circuit Complexity-Based-Optimization.pdf

 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 [5]. 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.