Three-way noiseless signal splitting in a parametric amplifier with quantum correlation

We demonstrate that a phase-insensitive parametric amplifier, coupled to a quantum correlated source, can be used as a quantum information tap for noiseless three-way signal splitting. We find that the output signals are amplified noiselessly in two of the three output ports while the other can more or less keep its original input size without adding noise. This scheme is able to cascade and scales up for efficient information distribution in an optical network. Furthermore, we find this scheme satisfies the criteria for a non-ideal quantum non-demolition (QND) measurement and thus can serve as a QND measurement device. With two readouts correlated to the input, we find this scheme also satisfies the criterion for sequential QND measurement

Energy-pollution-socioeconomic assessment from production- and consumption-based accounting approach

Rapid urbanization and industrialization in developing countries have stimulated energy consumption and resulted in environmental degradation. One of the global challenges today is to sustain socioeconomic development under the constraints of limited resources and without compromise in environmental wellness, climate resilience or function. Sustainable production and consumption is a promising way out of this grand challenge. A fundamental shift towards sustainable production and consumption patterns relies on a detailed characterization of material and emission flows between producers, consumers and environmental receptors. Such information, however, is greatly lacking in developing countries for both national and subnational levels. This study presents an integrated assessment of the interlinkages between energy, pollution and socioeconomic demands in China and its provinces with the thread of production- and consumption-based emissions. The double-digit growth of China’s economy before 2011 and its slow-down in the “new normal” period since then, rapid urbanization and rise of middle income class, and recession in export growth have resulted in dramatic changes in socioeconomic dimensions. It is important to understand how the socioeconomic drivers have evolved and fuelled the energy consumption and air pollution formation. Production- and consumption-based accounting approaches provide two distinct yet complementary angles to understand the nexus of socioeconomic demands, energy and pollution. This study develops an integrated assessment framework to depict material and emission flows between producers, consumers and environmental receptors. A four-stage research framework is proposed. It starts from the compilation of a primary energy consumption matrix, followed by the establishment of production-based inventories of greenhouse gases and air pollutants. Energy and emission accounts are then connected to socioeconomic accounts through environmentally-extended input-output (EEIO) analysis and decomposition techniques. Socioeconomic drivers that are responsible for energy consumption or emissions can be revealed, including entities such as intermediate sectors and final consumers and macroeconomic factors such as population growth, economic growth, industrial structure, energy intensity and energy mix. Meanwhile, production-based emissions marked by different socioeconomic drivers are fed into environmental modelling tools such as an air quality model. Through environmental models, a vast variety of environmental end-points can be evaluated, including but not limited to the ambient air pollutant concentration, air quality attainment rate, pollution formation regimes and death toll. With the corresponding relationship between production- and consumption-based emissions, socioeconomic demands and environmental consequences can be connected in an explicit and quantitative way. The proposed framework has been demonstrated at the provincial and national levels in China to advance the understanding of causes and effects of environmental issues in a socioeconomic context. Recognizing the central role of energy consumption in climate and air pollution problems, the production-based patterns of energy consumption in 30 provinces in China and their socioeconomic drivers are first investigated. Energy elasticity (the percentage change in energy consumption to achieve a 1% change in national GDP) in China have decreased continuously from 2003 to 2016. Starting at a level of 1.11 from 2003 to 2007, the energy elasticity dropped to 0.58 from 2007 to 2011, followed by an even lower value of 0.42 from 2011 to 2016. The reduction in the growth of energy consumption is even more prominent at the provincial level. Eight of the provinces saw declines in their total primary consumption from 2011 to 2016. They differed from their counterparts since 2011, when the decreasing effect of energy intensity was enhanced and, for the first time, surpassed or approximated the increasing effect of economic growth. The catching-up was more associated with the significant reduction of energy intensity rather than the slowdown of economic growth. New decreasing factors such as the share of coal and industrial structure change were also emerging to curb the growth. In addition, six provinces have levelled off their total primary consumption and decreased the combined consumption of coal and petroleum. Their driver mechanisms were similar but the share of cleaner fuels, e.g., natural gas and non-fossil fuels, increased significantly. Nevertheless, such declines were demonstrated to be initial rather than structural changes. To secure the trend or fasten transition, one path is to sustain the strong decreasing effect mainly from energy intensity, which is applicable to Hebei, Liaoning, Jilin, Henan, Hubei and Yunnan, whose energy intensities are still high (3.0~5.8 tce/104 $USD in 2016). The other path is to complement energy intensity with new decreasing drivers, which better suits the other provinces which have reached relatively low levels of energy intensity and have less potential for further reduction. Another two case studies at province levels are conducted. One is to investigate the demands behind air pollutant emissions in a fast developing region in China. Guangdong is a typical fast-developing region with annual GDP growth around 11% and China’s export industry hub. It is beset with air pollution problems featured by fine particulate matter (PM2.5) and ground-level ozone (O3). This study reveals that the varying trends of air pollutants from 2007 to 2012 were associated with production-based control measures and changes in economic structure and trading patterns. From the consumption perspective, due to the stringent control of SO2 in power plants and key industries, SO2 emissions saw substantial declines, while the less controlled PM10, PM2.5, non-methane volatile organic compounds (NMVOCs) and CO emissions continued to grow. The contributions of the cleaner service sectors to all seven pollutants increased. This increase could be a consequence of the expansion of the service sector, which grew by 41% in terms of its contributions to Guangdong’s GDP in 5 years. Meanwhile, exports accounted for more than 50% of the emissions, but their share had started to decrease for most pollutants except NMVOCs and CO. It suggests that Guangdong is moving towards a cleaner production and consumption pathway. The transformation of the industrial structure and increase in urban demand should help to further reduce emissions while maintaining economic development. The other case study focuses on CO2 emission in a less developed region in China. The production- and consumption-based characteristics of Tibet's CO2 emissions and its linkages with other regions in China are studied. Results show that the consumption-based CO2 emissions in Tibet (18.8 Mt, similar to Guinea's emissions in 2015) were three times as high as the production-based estimate (6.2 Mt). Tibet displays unique emission patterns with the highest ratio of consumption- to production-based emissions in China, which are more similar with the east developed provinces rather than its counterparts in west China. More than half of Tibet's consumption-based emissions are supported by Qinghai, Hebei, Sichuan, and others, enabled by the Qinghai-Tibet railway that connected Tibet to China's national railway system. High carbon footprint but low life expectancy is found in Tibet, suggesting the emerging need of a more sustainable consumption pathway under the intensifying interregional connections by Belt and Road Initiative. This study also presents a national study on the nexus of demand-emission-pollution-health. While China has made enormous progress in combatting PM2.5 pollution, its O3 exposure metrics increased by more than 50% from 2013 to 2017. This study investigates the socioeconomic drivers behind the O3 precursor emissions (NMVOCs, NOx and CO) and their effects on O3 formation chemistry, ambient O3 level and mortality. As the world’s factory, goods produced in China for foreign markets lead to an increase of domestic non-methane volatile organic compounds (NMVOCs) emissions by 3.5 million tons in 2013; about 13% of the national total or, equivalent to half of emissions from European Union (EU). Export demand driven emissions have mixed impacts on China’s ozone (O3) formation, but they generally contribute about 6~15% of peak O3 levels (6~10 μg/m3) caused by human activities in the coastal area resulting in an estimated 4615 (1514 ~ 7600) premature deaths. By benchmarking emission intensity in China to EU, the export footprint and NMVOCs emissions from the whole production capacity can be reduced by nearly 60% at moderate costs (at an annualized cost equivalent to 0.05% to 0.30% of industrial output). Such efforts will slow down the upward trend of O3 with notable health benefits. For a substantial attenuation of O3 pollution in China, however, concerted actions addressing domestic demands from urban and rural household are in great need. This PhD study presents an integrated assessment framework and captures how socioeconomic demands in China evolved and acted as driving forces of national and regional energy consumption, air pollutant emissions and pollution formation. In addition to end-of-pipe treatments, the roots of environmental problems need to be understood in socioeconomic context. The booming socioeconomic demands are responsible for the rise of energy consumption and poor air quality, but China as a whole and some of its more developed regions have been under a crucial transition towards sustainable production and consumption while maintaining the prosperity of individual and society. Experiences in China can be mirrored to other developing countries to foster sustainable production and consumption patterns

Joint measurement of multiple noncommuting parameters

Although quantum metrology allows us to make precision measurements beyond the standard quantum limit, it mostly works on the measurement of only one observable due to the Heisenberg uncertainty relation on the measurement precision of noncommuting observables for one system. In this paper, we study the schemes of joint measurement of multiple observables which do not commute with each other using the quantum entanglement between two systems. We focus on analyzing the performance of a SU(1,1) nonlinear interferometer on fulfilling the task of joint measurement. The results show that the information encoded in multiple noncommuting observables on an optical field can be simultaneously measured with a signal-to-noise ratio higher than the standard quantum limit, and the ultimate limit of each observable is still the Heisenberg limit. Moreover, we find a resource conservation rule for the joint measurement

Hidden dependence of spreading vulnerability on topological complexity

Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time -- commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its `spreading vulnerability', is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the `entropy of temporal entanglement' as a novel and insightful quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological and engineered systems.Comment: 15 pages, 9 figures, to appear in Phys. Rev.

Interference between two independent multi-temporal-mode thermal fields

We construct a general theoretical model for analyzing the intensity correlation of the field formed by mixing two independent multi-temporal-mode thermal fields. In the model, we use the intensity correlation function g(2) to characterize the mode property of the mixed thermal field. We find that g(2) of the mixed field is always less than that of the individual thermal field with less average mode number unless the two thermal fields are identical in mode property. The amount of drop in g(2) of the interference field depends on the relative overlap between the mode structures of two thermal fields and their relative strength. We successfully derive the analytical expressions of the upper bound and lower limit for g(2) of the interference field. Moreover, we verify the theoretical analysis by performing a series of experiments when the mode structures of two independent thermal fields are identical, orthogonal, and partially overlapped, respectively. The experimental results agree with theoretical predictions. Our investigation is useful for analyzing the signals carried by the intensity correlation of thermal fields

Quantum enhanced joint measurement of two conjugate observables with an SU(1, 1) interferometer

We jointly measure the phase and amplitude modulation of an optical field with the newly developed SU(1,1) interferometer. We simultaneously achieve a signal-to-noise ratio improvement of 1.1 and 1 dB over the standard quantum limit in amplitude and phase measurement

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol

Optimum quantum resource distribution for phase measurement and quantum information tapping in a dual-beam SU(1,1) interferometer

Quantum entanglement is a resource in quantum metrology that can be distributed to two conjugate physical quantities for the enhancement of their measurement sensitivity. This is demonstrated in the joint measurement of phase and amplitude modulation signals in quantum dense metrology schemes. We can also devote all the quantum resource to phase measurement only, leading to the optimum sensitivity enhancement. In this paper, we experimentally implement a dual-beam sensing scheme in an SU(1,1) interferometer for the optimum quantum enhancement of phase measurement sensitivity. We demonstrate a 3.9-dB improvement in signal-to-noise ratio over the optimum classical method, and this is 3-dB better than the traditional single-beam scheme. Furthermore, such as cheme also realizes a quantum optical tap of quantum entangled fields and has the full advantages of an SU(1,1) interferometer, such as detection loss tolerance, making it more suitable for practical applications in quantum metrology and quantum information

Versatile and precise quantum state engineering by using nonlinear interferometers

The availability of photon states with well-defined temporal modes is crucial for photonic quantum technologies. Ever since the inception of generating photonic quantum states through pulse pumped spontaneous parametric processes, many exquisite efforts have been put on improving the modal purity of the photon states to achieve single-mode operation. However, because the nonlinear interaction and linear dispersion are often mixed in parametric processes, limited successes have been achieved so far only at some specific wavelengths with sophisticated design. In this paper, we resort to a different approach by exploiting an active filtering mechanism originated from interference fringe of nonlinear interferometer. The nonlinear interferometer is realized in a sequential array of nonlinear medium, with a gap in between made of a linear dispersive medium, in which the precise modal control is realized without influencing the phase matching of the parametric process. As a proof-of-principle demonstration of the capability, we present a photon pairs source using a two-stage nonlinear interferometer formed by two identical nonlinear fibers with a standard single mode fiber in between. The results show that spectrally correlated two-photon state via four wave mixing in a single piece nonlinear fiber is modified into factorable state and heralded single-photons with high modal purity and high heralding efficiency are achievable. This novel quantum interferometric method, which can improve the quality of the photon states in almost all the aspects such as modal purity, heralding efficiency, and flexibility in wavelength selection, is proved to be effective and easy to realize

Quantifying agent impacts on contact sequences in social interactions

Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions - since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time - analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called 'contact sequence centrality', which quantifies the impact of an individual on the contact sequences, reflecting the individual's behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential 'behavioral super-spreaders'. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions