Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Department of Electrical and Computer Engineering
First Advisor
Laurence D. Merkle, PhD
Abstract
The potential of quantum computing to revolutionize critical military applications has led the US Department of Defense to recognize it as a keen interest. However, the practical implementation of these theoretical applications on physical quantum devices is currently limited by inherent reliability and accuracy issues in quantum hardware. To mitigate errors stemming from these limitations, the incorporation of software-based solutions is imperative. Quantum circuit optimization stands out as a primary method of increasing the accuracy of quantum computations. One of the key components of this approach is circuit reduction, whereby circuits are condensed to realize the same computation using fewer operations. State-of-the-art reduction schemes include the use of template matching to identify and reduce portions of a circuit. This thesis presents an algorithmic approach to quantum circuit reduction that uses layer transposition to achieve greater reductions than state-of-the-art methods. Specifically, it focuses on those transpositions of a single layer with either the preceding or the following pair of layers within a subcircuit that preserve the effect of the subcircuit. Upon executing the transposition operation and revealing a new, equivalent circuit arrangement, conventional optimization techniques are employed to identify reductions that were previously undetected. Following extensive testing methodologies, the proposed algorithm for implementing layer transposition consistently outperforms traditional optimization methods, demonstrating a statistically significant advantage in quantum cost reductions for exhaustively generated circuits and a statistically insignificant yet observable advantage for randomly generated circuits. However, these enhanced reduction capabilities come with trade-offs in terms of runtime and scalability, as the algorithm’s execution time exhibits exponential growth with increasing input size.
AFIT Designator
AFIT-ENG-MS-24-M-013
Recommended Citation
Grauberger, Christian L., "Quantum Circuit Reduction Using Three Layer Transposition" (2024). Theses and Dissertations. 7698.
https://scholar.afit.edu/etd/7698
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.