Charles Yuan, Massachusetts Institute of Technology

Abstract

Bringing the promise of quantum computation into reality requires not only building a quantum computer but also correctly programming it to run a quantum algorithm. To obtain asymptotic advantage over classical algorithms, quantum algorithms rely on the ability of data in quantum superposition to exhibit phenomena such as interference and entanglement. In turn, an implementation of the algorithm as a program must correctly orchestrate these phenomena in the states of qubits. Otherwise, the algorithm would yield incorrect outputs or lose its computational advantage.

Given a quantum algorithm, what are the challenges and costs to realizing it as a program that can run on a physical quantum computer? In this talk, I answer this question by showing how basic programming abstractions upon which many quantum algorithms rely – such as data structures and control flow – can fail to work correctly or efficiently on a quantum computer. I then show how we can leverage insights from programming languages to re-invent the software stack of abstractions, libraries, and compilers to meet the demands of quantum algorithms. This approach holds out a promise of expressive and efficient tools to program a quantum computer and practically realize its computational advantage.

Speaker bio: Charles Yuan is an incoming Assistant Professor at the University of Wisconsin, Madison and currently a Ph.D. candidate at MIT CSAIL advised by Prof. Michael Carbin. His research examines the challenges of programming quantum computers and other emerging models of computation. His work has appeared in the ACM SIGPLAN POPL, OOPSLA, and PLDI conferences and has been recognized with the SIGPLAN Distinguished Artifact Award and the CQE-LPS Doc Bedard Fellowship.