– Niels Bohr Institutet - Københavns Universitet

Quantum Markov Chain Mixing and Dissipative Engineering

Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandlingForskning


Michael James Kastoryano

This thesis is the fruit of investigations on the extension of ideas of Markov chain mixing to the quantum setting, and its application to problems of dissipative engineering.

A Markov chain describes a statistical process where the probability of future events depends only on the state of the system at the present point in time, but not on the history of events. Very many important processes in nature are of this type, therefore a good understanding of their behaviour has turned out to be very fruitful for science. Markov chains always have a non-empty set of limiting distributions (stationary states). The aim of Markov chain mixing is to obtain (upper and/or lower) bounds on the number of steps it takes for the Markov chain to reach a stationary state. The natural quantum extensions of these notions are density matrices and quantum channels. We set out to develop a general mathematical framework for studying quantum Markov chain mixing.

We introduce two new distance measures into the quantum setting; the quantum $\chi^2$-divergence and Hilbert's projective metric. With the help of these distance measures, we are able to derive some basic bounds on the the mixing times of quantum channels which mirror the existing classical bounds. We introduce the notion of cutoff phenomenon to the quantum setting. The cutoff phenomenon describes the situation when a Markov chain does not converge for a potentially long time, and then at a specific point in time abruptly converges to equilibrium. Finally, we consider three independent tasks of dissipative engineering: dissipatively preparing a maximally entangled state of two atoms trapped in an optical cavity, dissipative preparation of graph states, and dissipative quantum computing construction.
Udgivelses stedQUANTOP
Antal sider125
StatusUdgivet - 2012

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk

Ingen data tilgængelig

ID: 40758454