The quantum Rabi model for two qubits

We study the two-qubit Rabi model in the most general case where the qubits are different from each other. The spectrum of the system in the ultrastrong-coupling regime is shown to converge to two forced oscillator chains by perturbation theory. An even and odd decomposition of the Hilbert space allows us to calculate the spectra in any given parameter regime; the cases studied confirm our perturbation theory prediction in the ultrastrong-coupling regime and point to crossings in the spectra within each parity subspace in the moderate-coupling regime. The normal modes of the system are calculated by two different methods, the first a linear algebra approach via the parity bases that delivers a four-term recurrence relation for the amplitudes of the proper states and, the second, via Bargmann representation for the field that delivers five-term recurrence relations. Finally, we show some examples of the time evolution of the mean photon number, population inversion, von Neuman entropy and Wootters concurrence under the two-qubit quantum Rabi Hamiltonian by taking advantage of the parity decomposition.Comment: 14 pages, 3 figure

Searching for structure beyond parity in the two-qubit Dicke model

We try to classify the spectrum of the two-qubit Dicke model by calculating two quantum information measures of its eigenstates: the Wooters concurrence and the mutual quantum information. We are able to detect four spectral sets in each parity subspace of the model: one set is regular and given by the product of a Fock state of the field times the singlet Bell state of the qubits; the rest are fairly regular and related to the triplet states of the Bell basis. The singlet states become trapping states when we couple the Dicke model to an environment of harmonic oscillators, making them candidates for generating maximally entangled states in experimental realizations of ion trap quantum electrodynamics (QED) and circuit QED. Furthermore, they are robust and survive the inclusion of driving and dipole-dipole interactions, pointing to their use for storing quantum correlations, and it is straightforward to provide a generalization of these trapping states to the Dicke model with even number of qubits.Comment: 10 pages, 2 figures, 1 tabl

Multidimensional analysis of data obtained in experiments with X-ray emulsion chambers and extensive air showers

Nonparametric statistical methods are used to carry out the quantitative comparison of the model and the experimental data. The same methods enable one to select the events initiated by the heavy nuclei and to calculate the portion of the corresponding events. For this purpose it is necessary to have the data on artificial events describing the experiment sufficiently well established. At present, the model with the small scaling violation in the fragmentation region is the closest to the experiments. Therefore, the treatment of gamma families obtained in the Pamir' experiment is being carried out at present with the application of these models

Cosmic Ray Navigation System (CRoNS) for Autonomous Navigation in GPS-Denied Environments

In an era where Position, Navigation, and Timing (PNT) systems are integral to our technological infrastructure, the increasing prevalence of severe space weather events and the advent of deliberate disruptions such as GPS jamming and spoofing pose significant risks. These challenges are underscored by recent military operations in Ukraine, highlighting the vulnerability of Global Navigation Satellite Systems (GNSS). In response, we introduce the Cosmic Ray Navigation System (CRoNS). This innovative and resilient alternative utilizes cosmic muon showers for precise location pinpointing, especially in environments where GNSS is compromised or unavailable. CRoNS capitalizes on an economical, distributed network of compact muon sensors deployed across urban landscapes and potentially integrated into mobile devices. These sensors are tasked with continuously monitoring muon flux resulting from extensive air showers (EASs) triggered by the consistent high-energy particle flux entering Earth's atmosphere. A central AI unit synthesizes the collected data, determining EAS parameters to establish a dynamic reference coordinate system that could span cities and even nations. A notable advantage of CRoNS lies in its capability for reliable operation beneath the Earth's surface and in aquatic environments

Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN.

We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9})  eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation

Commissioning of the vacuum system of the KATRIN Main Spectrometer

The KATRIN experiment will probe the neutrino mass by measuring the beta-electron energy spectrum near the endpoint of tritium beta-decay. An integral energy analysis will be performed by an electro-static spectrometer (Main Spectrometer), an ultra-high vacuum vessel with a length of 23.2 m, a volume of 1240 m^3, and a complex inner electrode system with about 120000 individual parts. The strong magnetic field that guides the beta-electrons is provided by super-conducting solenoids at both ends of the spectrometer. Its influence on turbo-molecular pumps and vacuum gauges had to be considered. A system consisting of 6 turbo-molecular pumps and 3 km of non-evaporable getter strips has been deployed and was tested during the commissioning of the spectrometer. In this paper the configuration, the commissioning with bake-out at 300{\deg}C, and the performance of this system are presented in detail. The vacuum system has to maintain a pressure in the 10^{-11} mbar range. It is demonstrated that the performance of the system is already close to these stringent functional requirements for the KATRIN experiment, which will start at the end of 2016.Comment: submitted for publication in JINST, 39 pages, 15 figure

Reviewing GPU architectures to build efficient back projection for parallel geometries

Back-Projection is the major algorithm in Computed Tomography to reconstruct images from a set of recorded projections. It is used for both fast analytical methods and high-quality iterative techniques. X-ray imaging facilities rely on Back-Projection to reconstruct internal structures in material samples and living organisms with high spatial and temporal resolution. Fast image reconstruction is also essential to track and control processes under study in real-time. In this article, we present efficient implementations of the Back-Projection algorithm for parallel hardware. We survey a range of parallel architectures presented by the major hardware vendors during the last 10 years. Similarities and differences between these architectures are analyzed and we highlight how specific features can be used to enhance the reconstruction performance. In particular, we build a performance model to find hardware hotspots and propose several optimizations to balance the load between texture engine, computational and special function units, as well as different types of memory maximizing the utilization of all GPU subsystems in parallel. We further show that targeting architecture-specific features allows one to boost the performance 2–7 times compared to the current state-of-the-art algorithms used in standard reconstructions codes. The suggested load-balancing approach is not limited to the back-projection but can be used as a general optimization strategy for implementing parallel algorithms