Optimal tracking for pairs of qubit states

In classical control theory, tracking refers to the ability to perform measurements and feedback on a classical system in order to enforce some desired dynamics. In this paper we investigate a simple version of quantum tracking, namely, we look at how to optimally transform the state of a single qubit into a given target state, when the system can be prepared in two different ways, and the target state depends on the choice of preparation. We propose a tracking strategy that is proved to be optimal for any input and target states. Applications in the context of state discrimination, state purification, state stabilization and state-dependent quantum cloning are presented, where existing optimality results are recovered and extended.Comment: 15 pages, 8 figures. Extensive revision of text, optimality results extended, other physical applications include

Partial mixing and the formation of 13C pockets in AGB stars: effects on the s-process elements

The production of the elements heavier than iron via slow neutron captures (the s process) is a main feature of the contribution of asymptotic giant branch (AGB) stars of low mass (< 5 Msun) to the chemistry of the cosmos. However, our understanding of the main neutron source, the 13C(alpha,n)16O reaction, is still incomplete. It is commonly assumed that in AGB stars mixing beyond convective borders drives the formation of 13C pockets. However, there is no agreement on the nature of such mixing and free parameters are present. By means of a parametric model we investigate the impact of different mixing functions on the final s-process abundances in low-mass AGB models. Typically, changing the shape of the mixing function or the mass extent of the region affected by the mixing produce the same results. Variations in the relative abundance distribution of the three s-process peaks (Sr, Ba, and Pb) are generally within +/-0.2 dex, similar to the observational error bars. We conclude that other stellar uncertainties - the effect of rotation and of overshoot into the C-O core - play a more important role than the details of the mixing function. The exception is at low metallicity, where the Pb abundance is significantly affected. In relation to the composition observed in stardust SiC grains from AGB stars, the models are relatively close to the data only when assuming the most extreme variation in the mixing profile.Comment: 17 pages, 8 figures, 6 tables, accepted for publications on Monthly Notices of the Royal Astronomical Societ

Projections of future air quality are uncertain. But which source of uncertainty is most important?

Understanding how air pollution events may change in the future is of key importance to decision makers. Multi-model intercomparison projects focusing on atmospheric chemistry and air quality have been performed to inform the latest IPCC assessments. Future anthropogenic emission changes have generally been the foci of such model experiments, envisaged as the dominant driver of future atmospheric composition. The latest model assessments such as AerChemMIP utilize multi-model ensembles but also have limited individual model ensembles which permit different sources of uncertainty to be characterized. The recent study by Fiore et al. (2022, https://doi.org/10.1029/2021JD035985) specifically considers a multi-model and multi-member ensemble approach. It adds to the quantification of uncertainty in future projections through delineating uncertainty due to model diversity and due to internal or natural climate variability within the climate system, for mean and high PM2.5 air pollution events over the Eastern USA in the 21st century. Exploring the separate roles of internal climate variability and model diversity adds further value to the important research issue of quantifying how future anthropogenic climate change impacts air quality. Future multi-model intercomparisons need to balance the additional knowledge gained from research into understanding multiple sources of uncertainty that can inform decision making vs. the resource costs of performing these experiments using Earth System Models with interactive chemistry

Low Gain Avalanche Detectors (LGAD) for particle physics and synchrotron applications

A new avalanche silicon detector concept is introduced with a low gain in the region of ten, known as a Low Gain Avalanche Detector, LGAD. The detector's characteristics are simulated via a full process simulation to obtain the required doping profiles which demonstrate the desired operational characteristics of high breakdown voltage (500 V) and a gain of 10 at 200 V reverse bias for X-ray detection. The first low gain avalanche detectors fabricated by Micron Semiconductor Ltd are presented. The doping profiles of the multiplication junctions were measured with SIMS and reproduced by simulating the full fabrication process which enabled further development of the manufacturing process. The detectors are 300 μm thick p-type silicon with a resistivity of 8.5 kΩcm, which fully depletes at 116 V. The current characteristics are presented and demonstrate breakdown voltages in excess of 500 V and a current density of 40 to 100 nAcm−2 before breakdown measured at 20oC. The gain of the LGAD has been measured with a red laser (660 nm) and shown to be between 9 and 12 for an external bias voltage range from 150 V to 300 V

Sensitivity optimization in quantum parameter estimation

We present a general framework for sensitivity optimization in quantum parameter estimation schemes based on continuous (indirect) observation of a dynamical system. As an illustrative example, we analyze the canonical scenario of monitoring the position of a free mass or harmonic oscillator to detect weak classical forces. We show that our framework allows the consideration of sensitivity scheduling as well as estimation strategies for non-stationary signals, leading us to propose corresponding generalizations of the Standard Quantum Limit for force detection.Comment: 15 pages, RevTe

The neutral silicon-vacancy center in diamond: spin polarization and lifetimes

We demonstrate optical spin polarization of the neutrally-charged silicon-vacancy defect in diamond ($\mathrm{SiV^{0}}$), an $S=1$ defect which emits with a zero-phonon line at 946 nm. The spin polarization is found to be most efficient under resonant excitation, but non-zero at below-resonant energies. We measure an ensemble spin coherence time $T_2>100~\mathrm{\mu s}$ at low-temperature, and a spin relaxation limit of $T_1>25~\mathrm{s}$. Optical spin state initialization around 946 nm allows independent initialization of $\mathrm{SiV^{0}}$ and $\mathrm{NV^{-}}$ within the same optically-addressed volume, and $\mathrm{SiV^{0}}$ emits within the telecoms downconversion band to 1550 nm: when combined with its high Debye-Waller factor, our initial results suggest that $\mathrm{SiV^{0}}$ is a promising candidate for a long-range quantum communication technology

Agent-Based Modeling of Intracellular Transport

We develop an agent-based model of the motion and pattern formation of vesicles. These intracellular particles can be found in four different modes of (undirected and directed) motion and can fuse with other vesicles. While the size of vesicles follows a log-normal distribution that changes over time due to fusion processes, their spatial distribution gives rise to distinct patterns. Their occurrence depends on the concentration of proteins which are synthesized based on the transcriptional activities of some genes. Hence, differences in these spatio-temporal vesicle patterns allow indirect conclusions about the (unknown) impact of these genes. By means of agent-based computer simulations we are able to reproduce such patterns on real temporal and spatial scales. Our modeling approach is based on Brownian agents with an internal degree of freedom, $\theta$, that represents the different modes of motion. Conditions inside the cell are modeled by an effective potential that differs for agents dependent on their value $\theta$. Agent's motion in this effective potential is modeled by an overdampted Langevin equation, changes of $\theta$ are modeled as stochastic transitions with values obtained from experiments, and fusion events are modeled as space-dependent stochastic transitions. Our results for the spatio-temporal vesicle patterns can be used for a statistical comparison with experiments. We also derive hypotheses of how the silencing of some genes may affect the intracellular transport, and point to generalizations of the model

Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns

First-person stories can be analyzed by means of egocentric pictures acquired throughout the whole active day with wearable cameras. This manuscript presents an egocentric dataset with more than 45,000 pictures from four people in different environments such as working or studying. All the images were manually labeled to identify three patterns of interest regarding people's lifestyle: socializing, eating and sedentary. Additionally, two different approaches are proposed to classify egocentric images into one of the 12 target categories defined to characterize these three patterns. The approaches are based on machine learning and deep learning techniques, including traditional classifiers and state-of-art convolutional neural networks. The experimental results obtained when applying these methods to the egocentric dataset demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing and Beyond, 19th International Conference on Image Analysis and Processing (ICIAP), September 201