Quantum Computation in Solid State Systems discusses experimental implementation of quantum computing for information processing devices; in particular observations of quantum behavior in several solid state systems are presented. The complementary theoretical contributions provide models of minimizing decoherence in the different systems.

This book is meant to be a practical introduction into the use of probability and statistics in experimental physics for advanced undergraduate students and for graduate students. I have attempted to write a short book. It is not intended as a comprehensive text in probability and statistics. I have tried to emphasize areas I have found to be useful when doing experimental physics. Except for the first two chapters the emphasis is on applications and understanding. I have omitted proofs of formal theorems in the interests of brevity unless I felt the proof added to one's intuition in understanding and applying the theorem. Since, however, this is a field in which there are often a number of misunderstandings, it is necessary to state some things with reasonable precision. I have tried to do this when necessary. I assume the student is familiar with partial derivatives and with ele­ mentary matrix manipulation. A computer is a needed tool for probability and statistics in experimental physics. We will introduce its use in this subject in some of the homework problems. One may interact with a computer in a batch mode or an inter­ active mode. In a batch mode, one submits FORTRAN or other language programs, the computer processes them, and returns the end results. In the interactive mode, one gives the computer an instruction, the computer processes it, indicates what it has done, and waits for the next instruction.

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines.