a measure of available work that might be done in the process. Thus available work for an ideal gas at constant temperature and pressure is where and (see also Gibbs inequality). More generally the work available relative to some ambient is obtained by multiplying ambient temperature by relative entropy or net surprisal defined as the average value of where is the probability of a given state under ambient conditions. For instance, the work available in equilibrating a monatomic ideal gas to ambient values of and is thus , where relative entropy The resulting contours of constant relative entropy, shown at right for a mole of Argon at standard temperature and pressure, for example put limits on the conversion of hot to cold as in flame-powered air-conditioning or in the unpowered device to convert boiling-water to ice-water discussed here. Thus relative entropy measures thermodynamic availability in bits. Quantum information theory For density matrices and on a Hilbert space, the quantum relative entropy from to is defined to be In quantum information science the minimum of over all separable states can also be used as a measure of entanglement in the state . Relationship between models and reality Just as relative entropy of "actual from ambient" measures thermodynamic availability, relative entropy of "reality from a model" is also useful even if the only clues we have about reality are some experimental measurements. In the former case relative entropy describes distance to equilibrium or (when multiplied by ambient temperature) the amount of available work, while in the latter case it tells you about surprises that reality has up its sleeve or, in other words, how much the model has yet to learn. Although this tool for evaluating models against systems that are accessible experimentally may be applied in any field, its application to selecting a statistical model via Akaike information criterion are particularly well described in papers and a book by Burnham and Anderson. In a nutshell the relative entropy of reality from a model may be estimated, to within a constant additive term, by a function of the deviations observed between data and the model's predictions (like the mean squared deviation) . Estimates of such divergence for models that share the same additive term can in turn be used to select among models. When trying to fit parametrized models to data there are various estimators which attempt to minimize relative entropy, such