Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Machine generated contents note: Introduction and summary; 1. Operator algebras and topologies; 2. Quantum probability; 3. Quantum stochastic calculus; 4. Quantum stochastic differential equations; 5. Quantum Markov semigroups; 6. Minimal QDS; 7. Quantum Markov processes; 8. Strong quantum Markov processes; 9. Invariant normal states; 10. Recurrence and transience; 11. Ergodic theory.

The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigroups and processes, and large-time asymptotic behavior of quantum Markov semigroups.