Quantum weak values and logic, an uneasy couple

Quantum mechanical weak values of projection operators have been used to answer which-way questions, e.g. to trace which arms in a multiple Mach-Zehnder setup a particle may have traversed from a given initial to a prescribed final state. I show that this procedure might lead to logical inconsistencies in the sense that different methods used to answer composite questions, like Has the particle traversed the way X or the way Y? , may result in different answers depending on which methods are used to find the answer. I illustrate the problem by considering some examples: the quantum pigeonhole framework of Aharonov et al, the three-box problem, and Hardys paradox. To prepare the ground for my main conclusion on the incompatibility in certain cases of weak values and logic, I study the corresponding situation for strong/projective measurements. In this case, no logical inconsistencies occur provided one is always careful in specifying exactly to which ensemble or sample space one refers. My results cast doubts on the utility of quantum weak values in treating cases like the examples mentioned

Trends and challenges in VLSI technology scaling towards 100 nm

Summary form only given. Moore's Law drives VLSI technology to continuous increases in transistor densities and higher clock frequencies. This tutorial will review the trends in VLSI technology scaling in the last few years and discuss the challenges facing process and circuit engineers in the 100nm generation and beyond. The first focus area is the process technology, including transistor scaling trends and research activities for the 100nm technology node and beyond. The transistor leakage and interconnect RC delays will continue to increase. The tutorial will review new circuit design techniques for emerging process technologies, including dual Vt transistors and silicon-on-insulator. It will also cover circuit and layout techniques to reduce clock distribution skew and jitter, model and reduce transistor leakage and improve the electrical performance of flip-chip packages. Finally, the tutorial will review the test challenges for the 100nm technology node due to increased clock frequency and power consumption (both active and passive) and present several potential solution

Organic production systems in Northern highbush blueberries

The production of highbush blueberries is increasing worldwide. Organic production of blueberries in Sweden is presently very limited but is expected to have a great potential to expand as the berries are popular and have a good shelf life. The fact that blueberries require acid soils raises several questions concerning suitable substrates in combination with mycorrhizal inoculation and fertilization in organic production systems. Field and pot experiments have been established during 2011 and 2012 with the aim of developing a sustainable production system for high quality organic blueberries. After the second experimental year, total fruit yields were similar for plants grown in a plastic tunnel and in the open field. Yields were not affected by the addition of 10% forest soil to the peat-based substrate. Inoculation with ericoid mycorrhizal fungi had little effect on shoot length in a greenhouse pot experiment. Blueberries may be particularly suitable for organic production as the need for fertilizers is low combined with a relatively low disease pressure on the blueberry crop in the Nordic countries. The Swedish blueberry production might be expected to expand in the near future. The development of a successful and resource-efficient growing system for organic blueberries may encourage new blueberry growers to chose organic production

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods---the particle Metropolis--Hastings algorithm---which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis--Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods---including particle Metropolis--Hastings---to a large group of users without requiring them to know all the underlying mathematical details.Comment: Thomas B. Sch\"on, Andreas Svensson, Lawrence Murray and Fredrik Lindsten, 2018. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. In Mechanical Systems and Signal Processing, Volume 104, pp. 866-88