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ACMD Seminar: QA vs. QAO vs. QAOA: Optimizing Analog Quantum Algorithms

Lucas Brady
Applied and Computational Mathematics Division, ITL, NIST

April 29, 2020, 3:00–4:00 EDT

A video of this talk is available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page.

Abstract: Analog quantum algorithms are being implemented on current devices and show some of the most promise for near-term applications of quantum computing.  Quantum Annealing (QA), Quantum Adiabatic Optimization (QAO), and the Quantum Approximate Optimization Algorithm (QAOA) all form a class of analog quantum algorithms where the system is switched between two configurations or Hamiltonians in order to steer the state of the system into a desired target.  Which algorithm is more effective has remained unclear.  We apply the framework of optimal control theory to show that generically, given a fixed amount of time, the optimal procedure has the pulsed (or "bang-bang") structure of QAOA at the beginning and end but can have a smooth annealing structure in between.  Through simulations of various transverse field Ising models, we demonstrate that bang-anneal-bang protocols are more common for that problem of experimental and theoretical interest.  The general features identified here provide guideposts for the experimental implementations of quantum optimization algorithms on current and near-term device.

Bio: Lucas Brady earned a B.S. in Physics from Harvey Mudd College in 2013.  Under the advising of Dr. Wim van Dam, he received a Ph.D in Physics from the University of California, Santa Barbara in May of 2018.  He joined the ACMD at NIST as an NRC Postdoc in September of 2018 and has been working at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland.  His research has focused on various aspects of analog quantum algorithms, such as the importance of quantum tunneling to adiabatic computation, the classical simulation of such algorithms, and their underlying physics framework.



Created April 16, 2020, Updated May 5, 2020