Wei Tang will present his FPO "Enabling Large-scale Quantum Computing via Distributed and Hybrid Architectures" on Monday, January 27, 2025 at 10am in CS 302. The members of his committee are as follows: Examiners: Margaret Martonosi (adviser), Jeff Thompson (Princeton ECE), Kai Li Readers: Fred Chong (Univ of Chicago), Amit Levy All are welcome to attend. Abstract follows below. Quantum Computing (QC) is an emerging paradigm that may o!er significant runtime advantages over classical computing by harnessing quantum physics principles to perform computations. Through years of software and hardware developments, QC has achieved notable progress. The theoretical computational advantages of QC over classical computing only manifest themselves as greater e”ciency when handling large and complex applications. Yet, QC encounters significant challenges in scaling and maintaining the precision necessary to fulfill its potential. In simpler or smaller problems, these theoretical advantages might not present a clear benefit over traditional methods. Such complex applications demand large Quantum Processing Units (QPUs). Moreover, in contrast to the exceptionally low error rates of classical computers, current QPUs are much more susceptible to errors. This vulnerability necessitates QPUs that not only support large-scale tasks but also mitigate error accumulation. The dual requirements of size and accuracy put a heavy toll on QC, hindering its adoption in practical scenarios. Conversely, classical computing is characterized by its high precision and reliability across various applications. Nonetheless, it encounters intrinsic limitations in computational complexity for solving complex, real-world challenges. In fact, the best-known classical algorithms for many important real-world problems remain intractable as the size of the problems increases. Despite decades of semiconductor technology advancements, the miniaturization of chip components to enhance their processing capacity is approaching its physical boundaries. This development poses a challenge to classical computing’s ability to keep pace with increasing computational demands, highlighting the need for innovative paradigms such as QC. This dissertation tackles the challenges of scaling up QC by distributing quantum algorithms across quantum and classical resources. The distributed hybrid approach carries two main benefits. First, it significantly reduces the quantum resource requirements compared to an exclusively quantum setup. Second, it achieves potentially faster runtimes than those attainable on purely classical platforms. This dissertation pioneers the field of Distributed Hybrid QC (DHQC) and develops the first end-to-end DHQC toolchain. By integrating with cutting-edge classical computing methods, this dissertation further enhances the scalability of DHQC systems. Additionally, this dissertation also contributes to the evolution of QC compiler design, optimizing QPU performance towards practical applications.