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Why Study Quantum Circuits and What They Are Good For

 Igor Markov
 University of Michigan

 As transistor dimensions approach atomic scale,  quantum-mechanical effects such as tunneling and spin  become important ingredients in accurate performance models  of integrated circuits. Theoretical work in terms of such  models suggests that power-density constraints may eventually  require a departure from common practices of representing  logic 0s and 1s by charges, voltages or currents.
 Instead, nuclear and electron spins are proposed as  primary careers of stationary information, e.g.,  in the well-publicized demonstration by IBM in 2000,  and photon polarizations can transport quantum information  over great distances, acting as quantum bits.
 However, the algebra of quantum bits is radically different  from the Boolean algebra that describes modern digital  electronics, while such states are susceptible to frequent  and unusual types of errors. On the positive side, quantum  communication promises an unparalleled level of security  and some quantum algorithms solve otherwise intractable  problems in polynomial time. Despite many potential  applications and several active start-ups in the field,  the main obstacle to further progress in quantum information  processing is complexity. This is where design automation  can lend a helping hand.

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Igor L. Markov is an associate professor of Electrical Engineering and Computer Science at the University of Michigan. He is serving on the Executive Board of ACM SIGDA and editorial boards of the ACM Transactions on Design Automation of Electronic Systems (TODAES) and IEEE Transactions on Computers (TC). He has co-authored more than 120 refereed publications, some of which were honored by the best-paper award at the Design Automation and Test in Europe Conference (DATE) and the IEEE CAS Donald O. Pederson award for best paper in IEEE Transactions on Computer-Aided Design.
Additionally, Prof. Markov is the recipient of a DAC Fellowship, an ACM SIGDA Outstanding New Faculty award, an ACM SIGDA Technical Leadership Award, an NSF CAREER award, and an IBM Partnership Award.
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