A frequentist definition of sensitivity of a search for new phenomena is
discussed, that has several useful properties. It is based on completely
standard concepts, is generally applicable, and has a very clear
interpretation. It is particularly suitable for optimization, being independent
of a-priori expectations about the presence of a signal, thus allowing the
determination of a single set of cuts that is optimal both for setting limits
and for making a discovery. Simple approximate formulas are given for the
common problem of Poisson counts with background.

本篇论文讨论了对新现象搜索的敏感性的经典定义，其具有标准概念、广泛适用性和非常清晰的解释，尤其适合于优化；其中涉及泊松计数和背景等问题。

寻找新信号的敏感度及其优化

Sensitivity of searches for new signals and its optimization

In standard quantum computation, the initial state is pure and the answer is
determined by making a measurement of some of the bits in the computational
basis. What can be accomplished if the initial state is a highly mixed state
and the answer is determined by measuring the expectation of $\sigma_z$ on the
first bit with bounded sensitivity? This is the situation in high temperature
ensemble quantum computation. We show that in this model it is possible to
perform interesting physics simulations which have no known efficient classical
algorithms, even though the model is less powerful then standard quantum
computing in the presence of oracles.

高温集合量子计算可以实现非常有趣的物理模拟，而且没有已知的高效经典算法，即使该模型在存在预言机时比标准量子计算弱。