upper limit: C=10 (bit/s)Hz. Clues: (a) Assume a binary input signal Xi E X = {x1,x2} taking the values X1 = a and x2 = - a, and a continuous output yey, (b) Define the mutual information as (c) Define the conditional probability p(y/xi) as: para) where o2 is the channel noise variance. (d) Show that the channel capacity is a function of the "bit SNR" parameter S2 = a2/(2o2) and numerically determine the limit for increasing S2. Reversible computation This chapter makes us walk a few preliminary, but decisive, steps towards quantum information theory (QIT), which will be the focus of the rest of this book. Here, we shall remain in the classical world, yet getting a hint that it is possible to think of a different world where computations may be reversible, namely, without any loss of information. One key realization through this paradigm shift is that "information is physical." As we shall see, such a nonintuitive and striking conclusion actually results from the age-long paradox of Maxwell's demon in thermodynamics, which eventually found an elegant conclusion in Landauer's principle. This principle states that the erasure of a single bit of information requires one to provide an energy that is proportional to log 2, which, as we know from Shannon's theory, is the measure of information and also the entropy of a two-level system with a uniformly distributed source. This consideration brings up the issue of irreversible computation. Logic gates, used at the heart of the CPU in modern computers, are based on such computation irreversibility. I shall then describe the computers' von Newman's architecture, the intimate workings of the ALU processing network, and the elementary logic gates on which the ALU is based. This will also provide some basics of Boolean logic, expanding on Chapter 1, which is the key to the following logic-gate concepts. As I shall describe, a novel concept for logic gates (and, hence, their associated circuits or networks), can be designed to provide truly reversible computation. Maxwell's d