We develop entanglement swapping protocols and memory allocation methods for quantum repeater chains. Unlike most of the existing studies, the memory size of each quantum repeater in this work is a parameter that can be optimized. Based on Markov chain modeling of the entanglement distribution process, we determine the trade-off between the entanglement distribution rate and the memory size for temporal multiplexing techniques. We then propose three memory allocation methods that achieve entanglement distribution rates decaying polynomially with respect to the distance while using constant average memory slots per node. We also quantify the average number of memory slots required due to classical communication delay, as well as the delay of entanglement distribution. Our results show that a moderate memory size suffices to achieve a polynomial decay of entanglement distribution rate with respect to the distance, which is the scaling achieved by the optimal protocol even with infinite memory size at each node.
Strong light–matter coupling provides a promising path for the control of quantum matter where the latter is routinely described from first principles. However, combining the quantized nature of light with this ab initio tool set is challenging and merely developing as the coupled light–matter Hilbert space is conceptually different and computational cost quickly becomes overwhelming. In this work, we provide a nonperturbative photon-free formulation of quantum electrodynamics (QED) in the long-wavelength limit, which is formulated solely on the matter Hilbert space and can serve as an accurate starting point for such ab initio methods. The present formulation is an extension of quantum mechanics that recovers the exact results of QED for the zero- and infinite-coupling limit and the infinite-frequency as well as the homogeneous limit, and we can constructively increase its accuracy. We show how this formulation can be used to devise approximations for quantum-electrodynamical density-functional theory (QEDFT), which in turn also allows us to extend the ansatz to the full minimal-coupling problem and to nonadiabatic situations. Finally, we provide a simple local density–type functional that takes the strong coupling to the transverse photon degrees of freedom into account and includes the correct frequency and polarization dependence. This QEDFT functional accounts for the quantized nature of light while remaining computationally simple enough to allow its application to a large range of systems. All approximations allow the seamless application to periodic systems.
In the past years, large particle physics experiments have shown that muon rate variations detected in underground laboratories are sensitive to regional, middle-atmosphere temperature variations. Potential applications include tracking short-term atmosphere dynamics, such as Sudden Stratospheric Warmings. We report here that such sensitivity is not only limited to large surface detectors under high-opacity conditions. We use a portable muon detector conceived for muon tomography for geophysical applications, and we study muon rate variations observed over 1 year of measurements at the Mont Terri Underground Rock Laboratory, Switzerland (opacity of 700 meter water equivalent). We observe a direct correlation between middle-atmosphere seasonal temperature variations and muon rate. Muon rate variations are also sensitive to the abnormal atmosphere heating in January–February 2017, associated to a Sudden Stratospheric Warming. Estimates of the effective temperature coefficient for our particular case agree with theoretical models and with those calculated from large neutrino experiments under comparable conditions. Thus, portable muon detectors may be useful to (1) study seasonal and short-term middle-atmosphere dynamics, especially in locations where data are lacking such as midlatitudes, and (2) improve the calibration of the effective temperature coefficient for different opacity conditions. Furthermore, we highlight the importance of assessing the impact of temperature on muon rate variations when considering geophysical applications. Depending on latitude and opacity conditions, this effect may be large enough to hide subsurface density variations due to changes in groundwater content and should therefore be removed from the time series.
Quantum communication holds promise for absolutely secure transmission of secret messages and the faithful transfer of unknown quantum states. Photonic channels appear to be very attractive for the physical implementation of quantum communication. However, owing to losses and decoherence in the channel, the communication fidelity decreases exponentially with the channel length. Here we describe a scheme that allows the implementation of robust quantum communication over long lossy channels. The scheme involves laser manipulation of atomic ensembles, beam splitters, and single-photon detectors with moderate efficiencies, and is therefore compatible with current experimental technology. We show that the communication efficiency scales polynomially with the channel length, and hence the scheme should be operable over very long distances.
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.
The cost of enabling connectivity in noisy intermediate-scale quantum (NISQ) devices is an important factor in determining computational power. A particular architecture for trapped-ion quantum compu...
This special issue is conceived out of the proposition that recent developments in quantum theory as well as innovations in quantum technology have profound implications for international relations, especially in the field of international security. Interaction with quantum theory outside the circle of physics has been limited; our goal is to catalyse an informed debate on the virtues of quantum theory for international relations. As new scientific discoveries and technological applications suggest large-scale quantum phenomena, near-simultaneous interconnectivity creates global entanglements, and ubiquitous media produce profound observer-effects, we wish to make of quantum theory a human science. With the arrival of quantum computing, communications and artificial intelligence, we can also expect to see significant transformations in the nature, production and distribution of power and knowledge. This special issue introduces quantum approaches that can help us better understand, anticipate and perhaps even ameliorate the most pressing global issues facing us today and in the near future.