Teague Tomesh will present his FPO entitled "Co-designing Quantum Computer Architectures and Algorithms to Bridge the Quantum Resource Gap" on Monday, January 23, 2023 at 11am in CS 105. The members of Teague's committee are as follows: Examiners: Margaret Martonosi, Stephen Lyon, and Amit Levy Readers: Kyle Jamieson and Fred Chong (University of Chicago) A copy of his thesis will be available before the FPO upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Quantum computing is a new computational paradigm based on the laws of quantum physics that have been developed over the last century. Quantum computers (QCs) manipulate quantum states and exploit non-classical phenomena, such as superposition and entanglement, to perform computations. Given this computational model, many quantum algorithms have been developed which are theoretically capable of outperforming any classical computer for certain applications such as factoring large integers, optimization, and simulating highly entangled quantum systems. However, the quantum programs implementing these high impact applications are extremely resource demanding. Their time and space requirements outstrip the capabilities of current QC systems by many orders of magnitude. I refer to this mismatch between the resources demanded by applications and what is available on current hardware as the Quantum Resource Gap (QRG). This dissertation presents a strategy for overcoming the QRG by advocating for the design of domain-specific quantum accelerators. I discuss how this strategy may be pursued using quantum benchmarks and program profiling to identify matches between applications and architectures that are well suited to one another. Once a particular application-architecture match is found, the algorithm’s execution can be optimized with cross-layer, co-design techniques that incorporate relevant information from across the entire hardware-software stack. To demonstrate the advantages of this approach, I discuss three examples covering molecular simulation, data set clustering, and constrained combinatorial optimization applications.