01-27

Wei Tang will present his FPO "Enabling Large-scale Quantum Computing via Distributed and Hybrid Architectures"

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 computing paradigm that may offer significant runtime advantages over classical computing by harnessing the principles of quantum physics to perform computations. Through years of dedicated research and advancements in both software and hardware, QC has achieved notable progress. Yet, it encounters significant challenges in scaling and maintaining the precision necessary to fulfill its potential. The advantages of QC over classical computing become apparent when handling large-scale applications. This is because the theoretical computational advantages only manifest themselves as greater efficiency when the problems are large and complex. In simpler or smaller problems, these theoretical advantages might not present a clear benefit over traditional methods. Such large applications demand the construction of 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 to ensure the validity of the results. The dual requirements of size and accuracy put a heavy toll on QC, hindering its widespread adoption in practical scenarios. Conversely, classical computing is characterized by its high precision with low error rates and reliability across various applications. Nonetheless, it encounters intrinsic limitations in computational complexity, particularly 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 advancements in semiconductor technology, enhancing the capability for processing increasingly complex workloads, the miniaturization of chip components is approaching its physical boundaries. This development poses a challenge to classical computing’s iiiability to keep pace with increasing computational demands efficiently, highlighting the need for innovative computational paradigms such as QC. This dissertation tackles the challenges of scaling up QC by distributing quantum algorithms across both quantum and classical resources. This 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.