Lecturer: Prof. Daniel Lidar
Affiliation: Department of Electrical Engineering,
Chemistry and Physics, Seaver Science Center,
University of Southern California
Abstract:
Quantum information processing holds
great promise, yet large-scale, general
purpose quantum computers capable of
solving hard problems are not yet
available despite 20+ years of immense
effort. In this talk I will describe some of
this promise and effort, as well as the
obstacles and ideas for overcoming
them using error correction techniques.
I will focus on a special purpose
quantum information processor called a
quantum annealer, designed to speed
up the solution to tough optimization
problems. In October 2011 USC and
Lockheed-Martin jointly founded a
quantum computing center housing a
commercial quantum annealer built by
the Canadian company D-Wave
Systems. These processors use
superconducting flux qubits to minimize
the energy of classical spin-glass models
with as many spins as qubits, an NP-
hard problem with numerous
applications. There has been much
controversy surrounding the D-Wave
processors, questioning whether they
offer any advantage over classical
computing. I will survey the recent work
we have done to benchmark the
processors against highly optimized
classical algorithms, to test for quantum
effects, and to perform error correction.
References:
- T. Rønnow et al., “Defining and
detecting quantum speedup” Science
345, 420 (2014).
- S. Boixo et al., “Quantum annealing
with more than one hundred qubits”,
Nature Phys. 10, 218 (2014).
- K. Pudenz et al., “Error corrected
quantum annealing with hundreds of
qubits”, Nature Commun. 5, 3243
(2014).
- I. Hen et al., “Probing for quantum
speedup in spin glass problems with
planted solutions”, arXiv:1502.01663