Hi everybody,
We will be joining this talk remotely in the room 6-310 on Wednesday.
Peter
Prakash Murali
Wed 1:30-2:30 PM
Feb 13
Title: Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale
Quantum Computers.
Prakash Murali, Jonathan M. Baker, Ali Javadi Abhari, Frederic T. Chong
and Margaret Martonosi
(to appear in ASPLOS'19)
Abstract: A massive gap exists between current quantum computing (QC)
prototypes, and the size and scale required for many proposed QC
algorithms. Current QC implementations are prone to noise and
variability which affect their reliability, and yet with less than 80
quantum bits (qubits) total, they are too resource-constrained to
implement error correction. The term Noisy Intermediate-Scale Quantum
(NISQ) refers to these current and near-term systems of 1000 qubits or
less. Given NISQ's severe resource constraints, low reliability, and
high variability in physical characteristics such as coherence time or
error rates, it is of pressing importance to map computations onto them
in ways that use resources efficiently and maximize the likelihood of
successful runs.
Our work proposes and evaluates backend compiler approaches to map and
optimize high-level QC programs to execute with high reliability on NISQ
systems with diverse hardware characteristics. Our techniques all start
from an LLVM intermediate representation of the quantum program (such
as would be generated from high-level QC languages like Scaffold) and
generate QC executables runnable on the IBM Q public QC machine. We then
use this framework to implement and evaluate several optimal and
heuristic mapping methods. These methods vary in how they account for
the availability of dynamic machine calibration data, the relative
importance of various noise parameters, the different possible routing
strategies, and the relative importance of compile-time scalability
versus runtime success. Using real-system measurements, we show that
fine grained spatial and temporal variations in hardware parameters can
be exploited to obtain an average 2.9x (and up to 18x) improvement in
program success rate over the industry standard IBM Qiskit compiler.
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