609 research outputs found
Spatiotemporal Mapping of Photocurrent in a Monolayer Semiconductor Using a Diamond Quantum Sensor
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
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
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
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
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Optical imaging of motor cortical hemodynamic response to directional arm movements using near-infrared spectroscopy
Article on optical imaging of motor cortical hemodynamic response to directional arm movements using near-infrared spectroscopy
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach
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
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
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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