- author = "Susan Stepney and John A. Clark",
- title = "Evolving Quantum Programs and Protocols",
- booktitle = "Handbook of Theoretical and Computational Nanotechnology, volume 3, Quantum and Molecular Computing, Quantum Simulations",
- publisher = "American Scientific Publishers",
- year = "2006",
- editor = "Michael Rieth and Wolfram Schommers",
- chapter = "3",
- pages = "113--160",
- address = "Stevenson Ranch, CA, USA",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1588830425",
-
URL = "
http://www.worldcat.org/title/handbook-of-theoretical-and-computational-nanotechnology/oclc/636117368",
-
URL = "
http://www-users.cs.york.ac.uk/susan/bib/ss/nonstd/gqprev05.pdf",
-
abstract = "Quantum computation is not yet with us in any
practical sense (the largest number of qubits currently
in a quantum computer is 7), but a significant body of
physical scientists are at work on making quantum
computing a practical reality. If history repeats
itself, we will get small computers initially that will
grow as technology improves. Since quantum computers
seem capable of achieving results unachievable by other
means, exploiting effectively even limited hardware
platforms may bring significant economic benefits.
We now have an opportunity to build the application infrastructure to run on quantum computers when they eventually come on-stream. Several researchers have developed new and important quantum algorithms over the past decade, but there are fundamentally few distinct quantum algorithms. In some ways novel application development seems to have stalled.
Why is this? The authors of this review believe that intuition about quantum phenomena and the nature of quantum computation is too limited. It is such a radically different arena, well outside the comfort zone provided by traditional computation. If our mindsets are the problem then we must seek to free ourselves, or augment our current capabilities. Nature, in the guise of quantum mechanical laws, provides us with new computational capabilities. But Nature also is good at invention; evolution is a form of continual reinvention. In this chapter, we review how automated search techniques inspired by biological systems can be used to uncover new quantum circuits and algorithms.",
- notes = "revised and updated journal version \cite{Stepney:2008:jCTNS}",
- size = "60 pages",
