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.
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.