Detecting entanglement by pure bosonic extension

Detecting and quantifying quantum entanglement is a central task in quantum information theory. Relative entropy of entanglement (REE) is one of the most famous quantities for measuring entanglement and has various applications in many other fields. One well-studied and efficient approach for calculating the lower bound of REE is the positive partial transpose (PPT) criterion. But it fails in the bound entangled area. In this work, we use a method called pure bosonic extension to significantly improve the feasibility of $k$-symmetric/bosonic extensions which characterize the separable set from outside by a hierarchy structure. Based on this method, we can efficiently approximate the boundaries of $k$-bosonic extendible sets and obtain the desired lower bound of REE. Compared to the Semi-Definite Programming method, for example, the symmetric extension function in QETLAB, our algorithm can support much larger single particle dimensions and much larger $k$.Comment: 11 pages, 10 figure

Experimental Realization of a Quantum Refrigerator Driven by Indefinite Causal Orders

Indefinite causal order (ICO) is playing a key role in recent quantum technologies. Here, we experimentally study quantum thermodynamics driven by ICO on nuclear spins using the nuclear magnetic resonance system. We realize the ICO of two thermalizing channels to exhibit how the mechanism works, and show that the working substance can be non-classically cooled or heated albeit it undergoes thermal contacts with reservoirs of the same temperature. Moreover, we construct a single cycle of the ICO refrigerator, and evaluate its efficiency by measuring the work consumption and the heat energy extracted from the low-temperature reservoir. Unlike classical refrigerators in which the efficiency is perversely higher the closer the temperature of the high-temperature and low-temperature reservoirs are to each other, the ICO refrigerator's efficiency of performance is always bounded to small values due to the non-unit success probability in projecting the ancillary qubit to the preferable subspace. Our experiment demonstrates that the ICO process may offer a new resource with non-classical heat exchange, and paves the way towards construction of quantum refrigerators on a quantum system.Comment: 5 pages, 4 figure

Preparation of Polymer Solution for Profile Control and Displacement Using Wastewater with High Ca2+/Mg2+ and Fe2+ Concentrations

In the present study, we used Kalamkas, which is a typical Kazakhstani oilfield, which produces wastewater with high Ca2+/Mg2+ and Fe2+ concentrations, as a case study. We investigated a method for preparing Fe2+ polymer solutions without oxygen isolation under the conditions of salinity >110 × 103 mg/L, Ca2+/Mg2+ concentration >7000 mg/L, and Fe2+ concentration >30 mg/L. Fe2+-resistant groups were grafted onto the molecular chains of a hydrophobically associating polymer prepared using existing synthesis technology to overcome the decrease in apparent viscosity of the polymer solution due to the oxidation of Fe2+ during solution preparation. The experiments showed that PAM-IR with iron-resistant groups can be completely dissolved in the wastewater within 180 min, and can tolerate an NaCl concentration of up to 0.23 × 106 mg/L, a Ca2+ concentration of up to 10 × 103 mg/L, an Mg2+ concentration of up to 9 × 103 mg/L, and a Fe2+ concentration of up to 90 mg/L, with favorable thickening performance and resistances to NaCl, Ca2+, Mg2+, and Fe2+. PAM-IR has good injection performance and can establish a high resistance factor (FR) and residual resistance factor (FRR) to increase the sweep efficiency. Therefore, it is potentially useful for enhancing oil recovery

Experimental Study on Profile Control of Polymer and Weak Gel Molecules in Porous Media

Weak gel is a gel system formed by the mixing and crosslinking of a low-concentration polymer and a slow-release crosslinker. It can be used for profile control in deep reservoir, but its effect is greatly affected by mechanical shearing. Currently, the shearing effect on weak gel is mainly studied by way of mechanical stirring, while the effect of porous media shear on weak gel molecules and properties has been rarely discussed. In this paper, polymer solution, aluminum gel and phenolic gel were prepared. The molecular coil size, viscoelastic modulus and microscopic aggregation morphology in water solution of three systems before and after core shearing were investigated, and the injection performance of the three systems in cores with different permeabilities was tested by physical simulation experiments. The study results show that at equivalent permeability, the system with a larger equivalent sphere diameter of molecular coil is more seriously sheared and suffers greater viscosity loss. In the core with permeability of 1.0 D, polymer solution remains as the aggregation, while phenolic gel and aluminum gel cannot form network aggregations and they are inferior to polymer solution in migration capacity in the mid-deep part of the core. In the core with permeability of 1–5.8 D, the polymer solution remains as a Newtonian fluid, while phenolic gel and aluminum gel become purely viscous non-Newtonian fluids. The elastic modulus of aluminum gel and phenolic gel is four times more than that of a polymer. In the core with permeability higher than 8.5 D, aluminum gel and phenolic gel migrate with less effect by core shearing, and their profile control capacity in deep reservoir is higher than that of the polymer. In the core with permeability lower than 8.5 D, because the monomolecular activity of weak gels becomes poor, they migrate in porous media with more effect by core shearing, and their profile control and oil displacement capacity in deep reservoir is lower than that of the polymer

Tapping into Permutation Symmetry for Improved Detection of k-Symmetric Extensions

Symmetric extensions are essential in quantum mechanics, providing a lens to investigate the correlations of entangled quantum systems and to address challenges like the quantum marginal problem. Though semi-definite programming (SDP) is a recognized method for handling symmetric extensions, it grapples with computational constraints, especially due to the large real parameters in generalized qudit systems. In this study, we introduce an approach that adeptly leverages permutation symmetry. By fine-tuning the SDP problem for detecting $k$-symmetric extensions, our method markedly diminishes the searching space dimensionality and trims the number of parameters essential for positive definiteness tests. This leads to an algorithmic enhancement, reducing the complexity from $O(d^{2k})$ to $O(k^{d^2})$ in the qudit $k$-symmetric extension scenario. Additionally, our approach streamlines the process of verifying the positive definiteness of the results. These advancements pave the way for deeper insights into quantum correlations, highlighting potential avenues for refined research and innovations in quantum information theory.Comment: 8 pages, comments welcome

A Prediction Method for Development Indexes of Waterflooding Reservoirs Based on Modified Capacitance–Resistance Models

Capacitance–resistance models (CRMs) are semi-analytical methods to estimate the production rate of either an individual producer or a group of producers based on historical observed production and injection rates using material balance and signal correlations between injectors and producers. Waterflood performance methods are applied to evaluate the waterflooding performance effect and to forecast the development index on the basis of Buckley–Leverett displacement theory and oil–water permeability curve. In this case study, we propose an approach that combines a capacitance–resistance model (CRM) modified by increasing the influence radius on the constraints and a waterflood performance equation between oil cut and oil accumulative production to improve liquid and oil production prediction ability. By applying the method, we can understand the waterflood performance, inter-well connectivities between injectors and producer, and production rate fluctuation better, in order to re-just the water injection and optimize the producers’ working parameters to maximize gain from the reservoir. The new approach provides an effective way to estimate the conductivities between wells and production rates of a single well or well groups in CRMs. The application results in Kalamkas oilfield show that the estimated data can be in good agreement with the actual observation data with small fitting errors, indicating a good development index forecasting capability

Preparation of Polymer Solution for Profile Control and Displacement Using Wastewater with High Ca²⁺/Mg²⁺ and Fe²⁺ Concentrations

In the present study, we used Kalamkas, which is a typical Kazakhstani oilfield, which produces wastewater with high Ca2+/Mg2+ and Fe2+ concentrations, as a case study. We investigated a method for preparing Fe2+ polymer solutions without oxygen isolation under the conditions of salinity >110 × 103 mg/L, Ca2+/Mg2+ concentration >7000 mg/L, and Fe2+ concentration >30 mg/L. Fe2+-resistant groups were grafted onto the molecular chains of a hydrophobically associating polymer prepared using existing synthesis technology to overcome the decrease in apparent viscosity of the polymer solution due to the oxidation of Fe2+ during solution preparation. The experiments showed that PAM-IR with iron-resistant groups can be completely dissolved in the wastewater within 180 min, and can tolerate an NaCl concentration of up to 0.23 × 106 mg/L, a Ca2+ concentration of up to 10 × 103 mg/L, an Mg2+ concentration of up to 9 × 103 mg/L, and a Fe2+ concentration of up to 90 mg/L, with favorable thickening performance and resistances to NaCl, Ca2+, Mg2+, and Fe2+. PAM-IR has good injection performance and can establish a high resistance factor (FR) and residual resistance factor (FRR) to increase the sweep efficiency. Therefore, it is potentially useful for enhancing oil recovery

Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China

Snow plays an important role in the water cycle and global climate change, and the accurate monitoring of changes in snow depth is an important task. However, monitoring snow properties is still challenging and unclear, particularly in the Tibetan Plateau, which has rough land and uneven terrain. The traditional monitoring methods have some limitations in monitoring snow depth changes, and the Global Navigation Satellite System-Reflectometry (GNSS-R) provides a new opportunity for snow monitoring. This paper employed data from the Cyclone Global Navigation Satellite System (CYGNSS) to discover the effect of snow properties. Firstly, the observations of CYGNSS were used to find the sensitive to snow properties, and the relationships between signal to noise ratio (SNR), leading edge slope (LES), surface reflectivity (SR), and snow depth were studied and analyzed, respectively. It is found that the correlation between the first two parameters and snow depth is poor, while SR can indicate the changes in snow depth, and is proposed as an indicator of SR change, namely, surface reflectivity–difference ratio factor (SR–DR factor). Furthermore, the long-time series data in the Tibetan Plateau (2018–2019) are used to analyze its effects on the time series of the SR–DR factor, while the influences of the soil freeze/thaw (F/T) process and soil moisture are excluded during the analysis. The results indicate that the SR–DR factor can be a good indicator and discriminator for snow depth. Our work shows that space-borne GNSS-R has the potential for the monitoring of snow properties