Login [Center] Logout Join Us Guidelines  I  中文  I  CQI

Adventures in quantum optimization with noisy qubits

Speaker: Daniel Lidar USC
Time: 2018-05-14 11:00-2018-05-14 12:00
Venue: FIT 1-222


Quantum information processing holds great promise, yet large-scale, general purpose “universal" quantum computers capable of solving hard problems are not yet available despite 20+ years of immense worldwide effort. However, special purpose quantum information processors, such as the quantum simulators originally envisioned by Feynman, appear to be within reach. Another type of special purpose quantum information processor is a quantum annealer, designed to speed up the solution to classical 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. Starting with 108 qubits, two generations later the current processor at USC has 1098 qubits, and the latest generation deployed elsewhere already has close to 2048 qubits. These processors use superconducting flux qubits to try to find the ground states of Ising spin-glass problems with as many spins as qubits, an NP-hard problem with numerous applications. There has been much controversy surrounding the D-Wave processors, concerning whether they are sufficiently quantum to offer any advantage over classical computing. After introducing quantum annealing I will survey the work we have done to test the D-Wave processors for quantum effects, to test for quantum enhancements by benchmarking against highly optimized classical algorithms, and to perform error correction.


S. Boixo et al. “Evidence for Quantum Annealing with More Than One Hundred Qubits”, Nature Physics 10, 218 (2014); 

T.F. Ronnow et al., “Defining and Detecting Quantum Speedup”, Science 345, 420 (2014); 

K.P. Pudenz et al., “Error Corrected Quantum Annealing with Hundreds of Qubits”, Nature Communications 5, 3243 (2014);

S. Matsuura et al. “Mean Field Analysis of Quantum Annealing Correction”, Phys. Rev. Lett. 116, 220501 (2016);

A. Mott et al., “Solving a Higgs optimization problem with quantum annealing for machine learning”, Nature 550, 375 (2017);

T. Albash and D. A. Lidar, “Adiabatic Quantum Computation”, Rev. Mod. Phys. 90, 015002 (2018);

Short Bio:

Daniel Lidar is the Viterbi Professor of Engineering at USC, and a professor of Electrical Engineering, Chemistry, and Physics. He holds a Ph.D. in physics from the Hebrew University of
 Jerusalem. He did his postdoctoral work at UC Berkeley. Prior to joining USC in 2005 he was a faculty member at the University of Toronto. His main research interest is quantum information processing, where he works on quantum control, quantum error correction, the theory of open quantum systems, quantum algorithms, and theoretical as well as experimental adiabatic quantum computation. He is the Director of the USC Center for Quantum Information Science and Technology, and is the co-Director (Scientific Director) of the USC-Lockheed Martin Center for Quantum Computing.  Lidar is a recipient of a Sloan Research Fellowship, a Guggenheim Fellowship and is a Fellow of the AAAS, APS, and IEEE.