Quantum controlled-NOT gate with 'hot' trapped ions

We present a novel method of performing quantum logic gates in trapped ion quantum computers which does not require the ions to be cooled down to the ground state of their vibrational modes, thereby avoiding one of the principal experimental difficulties encountered in realizing this technology. Our scheme employs adiabatic passages and a phase shift conditional on the phonon number state.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

An analysis of why telehealth systems in Australia have not always succeeded

Telehealth programmes are rather similar to humans in the way that they are planned, develop, grow and ultimately die or disappear. To achieve good life expectancy for a telehealth programme there appear to be three major needs: nurturing, which includes the provision of money, ideas, education, training and innovation; experience, which involves an integrated management process, the achievement of long and wide patterns of usage, the development of updated policies and procedures and the involvement of multiple disciplines; success, which involves evidence of outcomes, evaluation and research, and, most important, the sharing of information through scientific and popular press publications, and conferences and collaborations with internal and external groups. The future of telehealth in Australia is at a watershed. There are now a substantial number of programmes, and there has been a large amount of financial and human investment in telehealth around the nation. There is, however, no forum for national leadership, no national association and little support at federal government level.

Who does not gain weight? Prevalence and predictors of weight maintenance in young women

OBJECTIVE: To investigate the prevalence and predictors of weight maintenance over time in a large sample of young Australian women. DESIGN: This population study examined baseline and 4y follow-up data from the cohort of young women participating in the Australian Longitudinal Study on Women's Health. SUBJECTS: A total of 8726 young women aged 18 - 23y at baseline. MEASURES: Height, weight and body mass index (BMI); physical activity; time spent sitting; selected eating behaviours (eg dieting, disordered eating, takeaway food consumption); cigarette smoking, alcohol consumption; parity; and socio-demographic characteristics. RESULTS: Only 44% of the women reported their BMI at follow-up to be within 5% of their baseline BMI (maintainers); 41% had gained weight and 15% had lost weight. Weight maintainers were more likely to be in managerial or professional occupations; to have never married; to be currently studying; and not to be mothers. Controlling for sociodemographic factors, weight maintainers were more likely to be in a healthy weight range at baseline, and to report that they spent less time sitting, and consumed less takeaway food, than women who gained weight. CONCLUSIONS: Fewer than half the young women in this community sample maintained their weight over this 4y period in their early twenties. Findings of widespread weight gain, particularly among those already overweight, suggest that early adulthood, which is a time of significant life changes for many women, may be an important time for implementing strategies to promote maintenance of healthy weight. Strategies which encourage decreased sitting time and less takeaway food consumption may be effective for encouraging weight maintenance at this life stage.