Abstract: Despite its relative infancy, there are a number of emerging quantum

technologies for quantum computation, and it is unclear which will be the

clear winner. Evaluation of these technologies at the architectural level,

far beyond the small-scale prototypes of 1 to 2 qubits, is critical to

producing viable systems capable of executing both near and long-term

applications effectively. At a high level, we are tasked with asking and

answering important sets of questions with each new technology developed.

In this talk, I discuss two case studies involving emerging technologies:

use of multivalued logic for quantum computation and use of 2.5D quantum

architectures with bounded local “memory.” In the first part, we explore

the use of a variety of optimization techniques for specialized and

general-use of *intermediate* qudits, temporary occupancy of higher order

states, to reduce circuit runtimes and reduce physical device requirements

which directly translates into improved output quality. In the second part,

we introduce a scalable 2.5D architecture composed of resonant cavities and

evaluate its ability to support quantum error correction codes. In

particular, we design an architecture which directly matches the

requirements of known error correction codes to reduce physical device

requirements while accelerating key logical operations. I will conclude

with some current and future directions in this area.

 

Short Bio: Jonathan Baker is a final-year Ph.D. student in the Department of Computer
Science at the University of Chicago, advised by Prof. Fred Chong. His
research is focused on interdisciplinary, full-stack optimization and the
evaluation of emerging quantum systems. Prior to the University of Chicago,
he received degrees in Computer Science, Chemistry, and Mathematics from
the University of Notre Dame.