Distributed quantum sensing enhanced by continuous-variable error correction

A distributed sensing protocol uses a network of local sensing nodes to estimate a global feature of the network, such as a weighted average of locally detectable parameters. In the noiseless case, continuous-variable (CV) multipartite entanglement shared by the nodes can improve the precision of parameter estimation relative to the precision attainable by a network without shared entanglement; for an entangled protocol, the root mean square estimation error scales like 1/M with the number M of sensing nodes, the so-called Heisenberg scaling, while for protocols without entanglement, the error scales like 1√M. However, in the presence of loss and other noise sources, although multipartite entanglement still has some advantages for sensing displacements and phases, the scaling of the precision with M is less favorable. In this paper, we show that using CV error correction codes can enhance the robustness of sensing protocols against imperfections and reinstate Heisenberg scaling up to moderate values of M. Furthermore, while previous distributed sensing protocols could measure only a single quadrature, we construct a protocol in which both quadratures can be sensed simultaneously. Our work demonstrates the value of CV error correction codes in realistic sensing scenarios

Identification of dynamic economic models from reduced form VECM structures: an application of covariance restrictions

This analysis is a straightforward implementation of both long-run and short-run identifying or overidentifying restrictions on a vector error correction model in the "structural VAR" framework. The framework utilizes covariance restrictions, long-run multiplier restrictions, error correction coefficient restrictions, and restrictions on slope coefficients of the stimultaneous interactions in the "economic model." The framework is general enough to incorporate restrictions on impact multipliers. Two examples are provided. The first example is a dynamic M2 demand specification with a comparison to previous results that are constructed using restrictions on distributed lag coefficients to achieve identification. The second example illustrates the identification of equilibrium short-term and long-term interest responses of money demand using error-correction coefficient restrictions when the individual coefficients are underidentified in the cointegrating vectors in the presence of stationary interest rate spreads.Econometric models ; Demand for money

Autoregressive distributed lag models and cointegration

This paper considers cointegration analysis within an autoregressive distributed lag (ADL) framework. First, different reparameterizations and interpretations are reviewed. Then we show that the estimation of a cointegrating vector from an ADL specification is equivalent to that from an error-correction (EC) model. Therefore, asymptotic normality available in the ADL model under exogeneity carries over to the EC estimator. Next, we review cointegration tests based on EC regressions. Special attention is paid to the effect of linear time trends in case of regressions without detrending. Finally, the relevance of our asymptotic results in finite samples is investigated by means of computer experiments. In particular, it turns out that the conditional EC model is superior to the unconditional one. --Error-correction , asymptotically normal inference , cointegration testing

Self-Healing Cellular Automata to Correct Soft Errors in Defective Embedded Program Memories

Static Random Access Memory (SRAM) cells in ultra-low power Integrated Circuits (ICs) based on nanoscale Complementary Metal Oxide Semiconductor (CMOS) devices are likely to be the most vulnerable to large-scale soft errors. Conventional error correction circuits may not be able to handle the distributed nature of such errors and are susceptible to soft errors themselves. In this thesis, a distributed error correction circuit called Self-Healing Cellular Automata (SHCA) that can repair itself is presented. A possible way to deploy a SHCA in a system of SRAM-based embedded program memories (ePM) for one type of chip multi-processors is also discussed. The SHCA is compared with conventional error correction approaches and its strengths and limitations are analyzed