Quantum repeaters with imperfect memories: cost and scalability

Memory dephasing and its impact on the rate of entanglement generation in quantum repeaters is addressed. For systems that rely on probabilistic schemes for entanglement distribution and connection, we estimate the maximum achievable rate per employed memory for our optimized partial nesting protocol. We show that, for any given distance $L$, the polynomial scaling of rate with distance can only be achieved if quantum memories with coherence times on the order of $L/c$ or longer, with $c$ being the speed of light in the channel, are available. The above rate degrades as a power of $\exp[-\sqrt{(L/c)/ \tau_c}]$ with distance when the coherence time $\tau_c \ll L/c$.Comment: Extended version with 5 figure

A review on the medical effects of Capparis spinosa L.

Background and aims: Plants are a valuable source of wide range of secondary metabolites. Caper (Capparis spinosa L.) belongs to the Capparaceae family. It has a lot of medical uses especially in medical fields. The aim of this study is to review the medical uses of this plant in nobel studies. Methods: In order to conduct this review study, INLM and Google scholar and Science direct databases were searched for English published articles during 2000-2015. Results: This plant has a lot of traditional and medical use. The whole plant was used for rheumatism. Roots were used as diuretic, astringent, and tonic. Bark root, which has a bitter taste, was used as appetizer, astringent, tonic, ant diarrheic and to treat hemorrhoids and spleen disease. Bark was also used for gout and rheumatism, as expectorant, and for chest diseases. Infusion of stems and root bark were used as anti-diarrheic and febrifuge. Fresh fruits were used in sciatica, and dropsy. Dried and powdered fruit combined with honey was used in colds, rheumatism, gout, sciatica and backache. Seeds were used in feminine sterility and dysmenorrheal and to relieve toothache. Crushed seeds were used for ulcers, scrofula, and ganglions. Conclusion: The paper reviewed was promising medicinal plant with wide range of pharmacological activities which could be utilized in several medical applications because of its effectiveness and safety

Memory-assisted quantum key distribution resilient against multiple-excitation effects

Memory-assisted measurement-device-independent quantum key distribution (MA-MDI-QKD) has recently been proposed as a technique to improve the rate-versus-distance behavior of QKD systems by using existing, or nearly-achievable, quantum technologies. The promise is that MA-MDI-QKD would require less demanding quantum memories than the ones needed for probabilistic quantum repeaters. Nevertheless, early investigations suggest that, in order to beat the conventional memory-less QKD schemes, the quantum memories used in the MA-MDI-QKD protocols must have high bandwidth-storage products and short interaction times. Among different types of quantum memories, ensemble-based memories offer some of the required specifications, but they typically suffer from multiple excitation effects. To avoid the latter issue, in this paper, we propose two new variants of MA-MDI-QKD both relying on single-photon sources for entangling purposes. One is based on known techniques for entanglement distribution in quantum repeaters. This scheme turns out to offer no advantage even if one uses ideal single-photon sources. By finding the root cause of the problem, we then propose another setup, which can outperform single memory-less setups even if we allow for some imperfections in our single-photon sources. For such a scheme, we compare the key rate for different types of ensemble-based memories and show that certain classes of atomic ensembles can improve the rate-versus-distance behavior

Memory-Assised Quantum Key Distribution Immune to Multiple-Excitation Effects

Memory-assisted quantum key distribution aims to use existing quantum-device technologies to offer rate-versus-distance enhancements. Here, a variant of such systems, relying on single-photon sources, is proposed that counters the multiple-excitation effects in ensemble-based memories

Primary cutaneous CD4-positive small/medium-sized pleomorphic T-cell lymphoma following heart transplantation

Post-transplantation cutaneous lymphoproliferative diseases (PTCLD) are rare, with 29 cases have so far been reported in the literature—only 4 cases underwent cardiac transplantation. Herein, we report on, to the best of our knowledge, the first case in the English literature of primary cutaneous CD4-positive small/medium-sized pleomorphic T-cell lymphoma in a cardiac transplant recipient

Using the Discrete Dipole Approximation and Holographic Microscopy to Measure Rotational Dynamics of Non-spherical Colloidal Particles

We present a new, high-speed technique to track the three-dimensional translation and rotation of non-spherical colloidal particles. We capture digital holograms of micrometer-scale silica rods and sub-micrometer-scale Janus particles freely diffusing in water, and then fit numerical scattering models based on the discrete dipole approximation to the measured holograms. This inverse-scattering approach allows us to extract the the position and orientation of the particles as a function of time, along with static parameters including the size, shape, and refractive index. The best-fit sizes and refractive indices of both particles agree well with expected values. The technique is able to track the center of mass of the rod to a precision of 35 nm and its orientation to a precision of 1.5$^\circ$, comparable to or better than the precision of other 3D diffusion measurements on non-spherical particles. Furthermore, the measured translational and rotational diffusion coefficients for the silica rods agree with hydrodynamic predictions for a spherocylinder to within 0.3%. We also show that although the Janus particles have only weak optical asymmetry, the technique can track their 2D translation and azimuthal rotation over a depth of field of several micrometers, yielding independent measurements of the effective hydrodynamic radius that agree to within 0.2%. The internal and external consistency of these measurements validate the technique. Because the discrete dipole approximation can model scattering from arbitrarily shaped particles, our technique could be used in a range of applications, including particle tracking, microrheology, and fundamental studies of colloidal self-assembly or microbial motion.Comment: 11 pages, 9 figures, 2 table

Observation of large-scale multi-agent based simulations

The computational cost of large-scale multi-agent based simulations (MABS) can be extremely important, especially if simulations have to be monitored for validation purposes. In this paper, two methods, based on self-observation and statistical survey theory, are introduced in order to optimize the computation of observations in MABS. An empirical comparison of the computational cost of these methods is performed on a toy problem

Architectural Considerations in Hybrid Quantum-Classical Networks

Three network architectures, compatible with passive optical networks, for future hybrid quantum-classical networks are proposed and compared. These setups rely on three different schemes for quantum key distribution (QKD): BB84, entanglement-based QKD, and measurement-device-independent QKD (MDI-QKD). It turns out that, while for small-to-moderatesize networks BB84 supports the highest secret key generation rate, it may fail to support large numbers of users. Its cost implications are also expected to be higher than other setups. For large networks, MDI-QKD offers the highest key rate if fast single-photon detectors are employed. Entanglement-based networks offer the longest security distance among the three setups. MDI-QKD is, however, the only architecture resilient to detection loopholes and possibly the most favorable with its less demanding end-user technology. Entanglement-based and MDI-QKD setups can both be combined with quantum repeater systems to allow for long-distance QKD with no trust constraints on the service provider

Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing

Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer's personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attri