Quantum Computing Day 2: Image Recognition with an Adiabatic Quantum Computer
Google Tech Talks December, 13 2007 ABSTRACT This tech talk series explores the enormous opportunities afforded by the emerging field of quantum computing. The exploitation of quantum phenomena not only offers tremendous speed-ups for important algorithms but may also prove key to achieving genuine synthetic intelligence. We argue that understanding higher brain function requires references to quantum mechanics as well. These talks look at the topic of quantum computing from mathematical, engineering and neurobiological perspectives, and we attempt to present the material so that the base concepts can be understood by listeners with no background in quantum physics. In this second talk, we make the case that machine learning and pattern recognition are problem domains well-suited to be handled by quantum routines. We introduce the adiabatic model of quantum computing and discuss how it deals more favorably with decoherence than the gate model. Adiabatic quantum computing can be understood as an annealing process that outperforms classical approaches to optimization by taking advantage of quantum tunneling.
