Chong's keynote talk, “Quantum Computing is Getting Real: Architecture, PL, and OS roles in Closing the Gap between Quantum Algorithms and Machines,” was presented at the ASPLOS 2018 Meeting . ASPLOS (ACM International Conference on Architectural Support for Programming Languages and Operating Systems) is "the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking." Chong's talk highlighted the unique time this is for the field of quantum computing, the potential transformative power of 100-1,000 qubit machines that are coming on-line in the next few years, and the key role that the computer science community must play in closing the gap between practical quantum algorithms and these real machines.
Chong discussed how these near-term machines may "fundamentally change our concept of what is computable," and outlined specific opportunities/challenges involved in vertically integrating software and hardware in the upcoming era of noisy intermediate-scale quantum (NISQ) technology. (see ASPLOS conference summary).
ASPLOS 2018 PROGRAM LISTING:
Keynote: Fred Chong, Seymour Goodman Professor of Computer Architecture,
University of Chicago
Title: “Quantum Computing is Getting Real: Architecture, PL, and OS roles in Closing the Gap between Quantum Algorithms and Machines”
Abstract: Quantum computing is at an inflection point, where 50-qubit (quantum bit) machines have been built, 100-qubit machines are just around the corner, and even 1000-qubit machines are perhaps only a few years away. These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation.
Yet a significant resource gap remains between practical quantum algorithms and real machines. There is an urgent shortage of the necessary computer scientists to work on software and architectures to close this gap.
I will outline several grand research challenges in closing this gap, including programming language design, software and hardware verification, defining and perforating abstraction boundaries, cross-layer optimization, managing parallelism and communication, mapping and scheduling computations, reducing control complexity, machine-specific optimizations, learning error patterns, and many more. I will also describe the resources and infrastructure available for starting research in quantum computing and for tackling these challenges.