# jemdoc: menu{MENU}{about.html}, showsource
= About


~~~
{}{img_left}{./images/TAlbash_Photo.jpg}{70}{140}{Tameem Albash}
[talbash@unm.edu Tameem Albash], Ph.D. \n
[https://ece.unm.edu/ Department of Electrical and Computer Engineering]\n
[https://physics.unm.edu/ Department of Physics and Astronomy (joint)]\n
[https://cquic.unm.edu Center for Quantum Information and Control]\n
[https://www.unm.edu/ University of New Mexico]\n
Office: ECE Building, Room 229B \n
Email: [talbash@unm.edu] \n
Tel: (505) 277-1688
~~~


== Biography

Tameem obtained his Ph.D. in Physics studying applications of AdS/CFT to condensed matter systems at the University of Southern of California (USC) in 2010 under the supervision of Clifford V. Johnson. He continued at USC as a postdoc under Daniel A. Lidar, where he started working on open quantum system simulations, quantum annealing and more generally adiabatic quantum computing. He subsequently joined the Information Sciences Institute (ISI) at USC, where he continued his work in benchmarking quantum annealing processors and continuing the search for the still elusive physically relevant example of a quantum speedup. In 2019, Tameem joined the Electrical and Computer Engineering department at the University of New Mexico (UNM). He has a joint appointment in the Department of Physics and Astronomy and is an associate member of the Center for Quantum Information and Control (CQuIC).

Tameem's present research interests are on understanding how and whether practical quantum advantages may manifest themselves in near-term quantum information processing hardware. While quantum algorithms are known to provide computational speedups over their classical counterparts, current devices are limited both in the size and length of computations they can perform. The challenge is now to uncover computational tasks for which these and future near-term devices could provide measurable performance advantages given these constraints. Of particular interest is the question of whether there is ultimately a tradeoff between the noise-sensitivity of an algorithm and its ability to provide a genuine quantum advantage.