Heat transport through a Josephson junction

We discuss heat transport through a Josephson tunnel junction under various bias conditions. We first derive the formula for the cooling power of the junction valid for arbitrary time dependence of the Josephson phase. Combining it with the classical equation of motion for the phase, we find the time-averaged cooling power as a function of bias current or bias voltage. We also find the noise of the heat current and, more generally, the full counting statistics of the heat transport through the junction. We separately consider the metastable superconducting branch of the current-voltage characteristics allowing quantum fluctuations of the phase in this case. This regime is experimentally attractive since the junction has low power dissipation, low impedance, and therefore may be used as a sensitive detector.Peer reviewe

Photonic heat transport from weak to strong coupling

Superconducting circuits provide a favorable platform for quantum thermodynamic experiments. An important component for such experiments is a heat valve, i.e. a device which allows one to control the heat power flowing through the system. Here we theoretically study the heat valve based on a superconducting quantum interference device (SQUID) coupled to two heat baths via two resonators. The heat current in such system can be tuned by magnetic flux. We investigate how does the heat current modulation depend on the coupling strength g between the SQUID and the resonators. In the weak coupling regime the heat current modulation grows as g2, but, surprisingly, at the intermediate coupling it can be strongly suppressed. This effect is linked to the resonant nature of the heat transport at weak coupling, where the heat current dependence on the magnetic flux is a periodic set of narrow peaks. At the intermediate coupling, the peaks become broader and overlap, thus reducing the heat modulation. At very strong coupling the heat modulation grows again and finally saturates at a constant value.Comment: 8 pages, 3 figure

Exactly solvable model of calorimetric measurements

Calorimetric measurements are experimentally realizable methods to assess thermodynamics relations in quantum devices. With this motivation in mind, we consider a resonant level coupled to a Fermion reservoir. We consider a transient process in which the interaction between the level and the reservoir is initially switched on and then switched off again. We find the time dependence of the energy of the reservoir, of the energy of the level, and of the interaction energy between them at weak, intermediate, strong, and ultrastrong coupling. We also determine the statistical distributions of these energies.Peer reviewe

Universal scaling of current fluctuations in disordered graphene

We analyze the full transport statistics of graphene with smooth disorder at low dopings. First we consider the case of 1D disorder for which the transmission probability distribution is given analytically in terms of the graphene-specific mean free path. All current cumulants are shown to scale with system parameters (doping, size, disorder strength and correlation length) in an identical fashion for large enough systems. In the case of 2D disorder, numerical evidence is given for the same kind of identical scaling of all current cumulants, so that the ratio of any two such cumulants is universal. Specific universal values are given for the Fano factor, which is smaller than the pseudodiffusive value of ballistic graphene (F=1/3) both for 1D (F=0.243) and 2D (F=0.295) disorder. On the other hand, conductivity in wide samples is shown to grow without saturation as \sqrt{L} and Log L with system length L in the 1D and 2D cases respectively.Comment: 9 pages, 7 figures. Published version, includes corrected figure for Fano facto

Deep Quarantine for Suspicious Mail

In this paper, we introduce DeepQuarantine (DQ), a cloudtechnology to detect and quarantine potential spam messages. Spam at-tacks are becoming more diverse and can potentially be harmful to emailusers. Despite the high quality and performance of spam filtering sys-tems, detection of a spam campaign can take some time. Unfortunately,in this case some unwanted messages get delivered to users. To solve thisproblem, we created DQ, which detects potential spam and keeps it ina special Quarantine folder for a while. The time gained allows us todouble-check the messages to improve the reliability of the anti-spam so-lution. Due to high precision of the technology, most of the quarantinedmail is spam, which allows clients to use email without delay. Our solutionis based on applying Convolutional Neural Networks on MIME headersto extract deep features from large-scale historical data. We evaluatedthe proposed method on real-world data and showed that DQ enhancesthe quality of spam detection

Extreme reductions of entropy in an electronic double dot

We experimentally study negative fluctuations of stochastic entropy production in an electronic double dot operating in nonequilibrium steady-state conditions. We record millions of random electron tunneling events at different bias points, thus collecting extensive statistics. We show that for all bias voltages the experimental average values of the minima of stochastic entropy production lie above $-k_B$, where $k_B$ is the Boltzmann constant, in agreement with recent theoretical predictions for nonequilibrium steady states. Furthermore, we also demonstrate that the experimental cumulative distribution of the entropy production minima is bounded, at all times and for all bias voltages, by a universal expression predicted by the theory. We also extend our theory by deriving a general bound for the average value of the maximum heat absorbed by a mesoscopic system from the environment and compare this result with experimental data. Finally, we show by numerical simulations that these results are not necessarily valid under non-stationary conditions.Comment: 16 pages, 12 figure

Deep Quarantine for Suspicious Mail

In this paper, we introduce DeepQuarantine (DQ), a cloudtechnology to detect and quarantine potential spam messages. Spam at-tacks are becoming more diverse and can potentially be harmful to emailusers. Despite the high quality and performance of spam filtering sys-tems, detection of a spam campaign can take some time. Unfortunately,in this case some unwanted messages get delivered to users. To solve thisproblem, we created DQ, which detects potential spam and keeps it ina special Quarantine folder for a while. The time gained allows us todouble-check the messages to improve the reliability of the anti-spam so-lution. Due to high precision of the technology, most of the quarantinedmail is spam, which allows clients to use email without delay. Our solutionis based on applying Convolutional Neural Networks on MIME headersto extract deep features from large-scale historical data. We evaluatedthe proposed method on real-world data and showed that DQ enhancesthe quality of spam detection