Strictly contractive quantum channels and physically realizable quantum computers

We study the robustness of quantum computers under the influence of errors modelled by strictly contractive channels. A channel $T$ is defined to be strictly contractive if, for any pair of density operators $\rho,\sigma$ in its domain, $\| T\rho - T\sigma \|_1 \le k \| \rho-\sigma \|_1$ for some $0 \le k < 1$ (here $\| \cdot \|_1$ denotes the trace norm). In other words, strictly contractive channels render the states of the computer less distinguishable in the sense of quantum detection theory. Starting from the premise that all experimental procedures can be carried out with finite precision, we argue that there exists a physically meaningful connection between strictly contractive channels and errors in physically realizable quantum computers. We show that, in the absence of error correction, sensitivity of quantum memories and computers to strictly contractive errors grows exponentially with storage time and computation time respectively, and depends only on the constant $k$ and the measurement precision. We prove that strict contractivity rules out the possibility of perfect error correction, and give an argument that approximate error correction, which covers previous work on fault-tolerant quantum computation as a special case, is possible.Comment: 14 pages; revtex, amsfonts, amssymb; made some changes (recommended by Phys. Rev. A), updated the reference

Active Classification for POMDPs: a Kalman-like State Estimator

The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking, estimation of sparse signals and radar scheduling.Comment: 38 pages, 6 figure

Error Detection and Recovery in Software Development

Software rarely works as intended when it is first written. Software engineering research has long been concerned with assessing why software fails and who is to blame, or why a piece of software is flawed and how to prevent such faults in the future. Errors are examined in the context of bugs, elements of source code that produce undesirable, unexpected and unintended deviations in behaviour. Though error is a prevalent, mature topic within software engineering, error detection and recovery are less well understood. This research uses rich qualitative methods to study error detection and recovery in professional software development practice. It has considered conceptual representations of error in software engineering research and trade literature. Using ethnographic principles, it has gathered accounts given by professional developers in interviews and in video-recorded paired interaction. Developers performing a range of tasks were observed, and findings were compared to theories of human error formed in psychology and safety science. Three empirical studies investigated error from the perspective of developers, recon- structing the view they hold when errors arise, to build a catalogue of active encounters with error in conceptual design, at the desk and after the fact. Analyses were structured to consider development holistically over time, rather than in terms of discrete tasks. By placing emphasis on “local rationality”, analytical focus was redirected from outcomes toward factors that influence performance. The resultant observations are assembled in an account of error handling in software development as personal and situated (in time and the developer’s environment), with implications for the changing nature of expertise

Change Acceleration and Detection

A novel sequential change detection problem is proposed, in which the change should be not only detected but also accelerated. Specifically, it is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments not only determine the pre-change and post-change distributions of the responses, but also influence when the change happens. The problem is to find a treatment assignment rule and a stopping rule that minimize the expected total number of observations subject to a user-specified bound on the false alarm probability. The optimal solution to this problem is obtained under a general Markovian change-point model. Moreover, an alternative procedure is proposed, whose applicability is not restricted to Markovian change-point models and whose design requires minimal computation. For a large class of change-point models, the proposed procedure is shown to achieve the optimal performance in an asymptotic sense. Finally, its performance is found in two simulation studies to be close to the optimal, uniformly with respect to the error probability

Technical Note: Field experiences using UV/VIS sensors for high-resolution monitoring of nitrate in groundwater

peer-reviewedTwo different in situ spectrophotometers are compared that were used in the field to determine nitrate-nitrogen (NO3-N) concentrations at two distinct spring discharge sites. One sensor was a double wavelength spectrophotometer (DWS) and the other a multiple wavelength spectrophotometer (MWS). The objective of the study was to review the hardware options, determine ease of calibration, accuracy, influence of additional substances and to assess positive and negative aspects of the two sensors as well as troubleshooting and trade-offs. Both sensors are sufficient to monitor highly time-resolved NO3-N concentrations in emergent groundwater. However, the chosen path length of the sensors had a significant influence on the sensitivity and the range of detectable NO3-N. The accuracy of the calculated NO3-N concentrations of the sensors can be affected if the content of additional substances such as turbidity, organic matter, nitrite or hydrogen carbonate significantly varies after the sensors have been calibrated to a particular water matrix. The MWS offers more possibilities for calibration and error detection but requires more expertise compared with the DWS.The authors would like to acknowledge the Teagasc Walsh Fellowship scheme for funding the study in Ireland, and the German federal Ministry of Education and Research (BMBF) for sponsoring the SMART-project (grant no. 02WM1079-1086, 02WM1211-1212) for the study in Jordan.Teagasc Walsh Fellowship Programm