609 research outputs found

    Spatiotemporal Mapping of Photocurrent in a Monolayer Semiconductor Using a Diamond Quantum Sensor

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    The detection of photocurrents is central to understanding and harnessing the interaction of light with matter. Although widely used, transport-based detection averages over spatial distributions and can suffer from low photocarrier collection efficiency. Here, we introduce a contact-free method to spatially resolve local photocurrent densities using a proximal quantum magnetometer. We interface monolayer MoS2 with a near-surface ensemble of nitrogen-vacancy centers in diamond and map the generated photothermal current distribution through its magnetic field profile. By synchronizing the photoexcitation with dynamical decoupling of the sensor spin, we extend the sensor's quantum coherence and achieve sensitivities to alternating current densities as small as 20 nA per micron. Our spatiotemporal measurements reveal that the photocurrent circulates as vortices, manifesting the Nernst effect, and rises with a timescale indicative of the system's thermal properties. Our method establishes an unprecedented probe for optoelectronic phenomena, ideally suited to the emerging class of two-dimensional materials, and stimulates applications towards large-area photodetectors and stick-on sources of magnetic fields for quantum control.Comment: 19 pages, 4 figure

    Synthetic clock transitions via continuous dynamical decoupling

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    Decoherence of quantum systems due to uncontrolled fluctuations of the environment presents fundamental obstacles in quantum science. `Clock' transitions which are insensitive to such fluctuations are used to improve coherence, however, they are not present in all systems or for arbitrary system parameters. Here, we create a trio of synthetic clock transitions using continuous dynamical decoupling in a spin-1 Bose-Einstein condensate in which we observe a reduction of sensitivity to magnetic field noise of up to four orders of magnitude; this work complements the parallel work by Anderson et al. (submitted, 2017). In addition, using a concatenated scheme, we demonstrate suppression of sensitivity to fluctuations in our control fields. These field-insensitive states represent an ideal foundation for the next generation of cold atom experiments focused on fragile many-body phases relevant to quantum magnetism, artificial gauge fields, and topological matter.Comment: 8 pages, 4 figures, Supplemental material

    Two-qubit spectroscopy of spatiotemporally correlated quantum noise in superconducting qubits

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    Noise that exhibits significant temporal and spatial correlations across multiple qubits can be especially harmful to both fault-tolerant quantum computation and quantum-enhanced metrology. However, a complete spectral characterization of the noise environment of even a two-qubit system has not been reported thus far. We propose and experimentally validate a protocol for two-qubit dephasing noise spectroscopy based on continuous control modulation. By combining ideas from spin-locking relaxometry with a statistically motivated robust estimation approach, our protocol allows for the simultaneous reconstruction of all the single-qubit and two-qubit cross-correlation spectra, including access to their distinctive non-classical features. Only single-qubit control manipulations and state-tomography measurements are employed, with no need for entangled-state preparation or readout of two-qubit observables. While our experimental validation uses two superconducting qubits coupled to a shared engineered noise source, our methodology is portable to a variety of dephasing-dominated qubit architectures. By pushing quantum noise spectroscopy beyond the single-qubit setting, our work paves the way to characterizing spatiotemporal correlations in both engineered and naturally occurring noise environments.Comment: total: 22 pages, 7 figures; main: 13 pages, 6 figures, supplementary: 6 pages, 1 figure; references: 3 page

    Machine learning approach for quantum non-Markovian noise classification

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    In this paper, machine learning and artificial neural network models are proposed for quantum noise classification in stochastic quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network, models with different complexity and accuracy, to solve supervised binary classification problems. By exploiting the quantum random walk formalism, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using data sets collected in a single realisation of the quantum system evolution. In addition, we also show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations, without any external driving. Thus, neither measurements of quantum coherences nor sequences of control pulses are required. Since in principle the training of the machine learning models can be performed a-priori on synthetic data, our approach is expected to find direct application in a vast number of experimental schemes and also for the noise benchmarking of the already available noisy intermediate-scale quantum devices.Comment: 14 pages, 3 figures, 3 table

    Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

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    Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

    Harnessing optical micro-combs for microwave photonics

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    In the past decade, optical frequency combs generated by high-Q micro-resonators, or micro-combs, which feature compact device footprints, high energy efficiency, and high-repetition-rates in broad optical bandwidths, have led to a revolution in a wide range of fields including metrology, mode-locked lasers, telecommunications, RF photonics, spectroscopy, sensing, and quantum optics. Among these, an application that has attracted great interest is the use of micro-combs for RF photonics, where they offer enhanced functionalities as well as reduced size and power consumption over other approaches. This article reviews the recent advances in this emerging field. We provide an overview of the main achievements that have been obtained to date, and highlight the strong potential of micro-combs for RF photonics applications. We also discuss some of the open challenges and limitations that need to be met for practical applications.Comment: 32 Pages, 13 Figures, 172 Reference

    Spatiotemporal structures in aging and rejuvenating glasses

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    Complex spatiotemporal structures develop during the process of aging glasses after cooling and of rejuvenating glasses upon heating. The key to understanding these structures is the interplay between the activated reconfiguration events which generate mobility and the transport of mobility. These effects are both accounted for by combining the random first order transition theory of activated events with mode coupling theory in an inhomogeneous setting. The predicted modifications by mobility transport of the time course of the aging regime are modest. In contrast, the rejuvenation process is strongly affected through the propagation of fronts of enhanced mobility originating from the initial reconfiguration events. The structures in a rejuvenating glass resemble flames. An analysis along the lines of combustion theory provides an estimate of the front propagation speed. Heterogeneous rejuvenation naturally should occur for glasses with free surfaces. The analogy with combustion also provides a new way of looking at the uptake of diluents by glasses described by case II and super case II diffusion
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