454 research outputs found
Site-resolved imaging of a fermionic Mott insulator
The complexity of quantum many-body systems originates from the interplay of
strong interactions, quantum statistics, and the large number of
quantum-mechanical degrees of freedom. Probing these systems on a microscopic
level with single-site resolution offers important insights. Here we report
site-resolved imaging of two-component fermionic Mott insulators, metals, and
band insulators using ultracold atoms in a square lattice. For strong repulsive
interactions we observe two-dimensional Mott insulators containing over 400
atoms. For intermediate interactions, we observe a coexistence of phases. From
comparison to theory we find trap-averaged entropies per particle of
. In the band-insulator we find local entropies as low as
. Access to local observables will aid the understanding
of fermionic many-body systems in regimes inaccessible by modern theoretical
methods.Comment: 6+7 page
Parton theory of magnetic polarons: Mesonic resonances and signatures in dynamics
When a mobile hole is moving in an anti-ferromagnet it distorts the
surrounding Neel order and forms a magnetic polaron. Such interplay between
hole motion and anti-ferromagnetism is believed to be at the heart of high-Tc
superconductivity in cuprates. We study a single hole described by the t-Jz
model with Ising interactions between the spins in 2D. This situation can be
experimentally realized in quantum gas microscopes. When the hole hopping is
much larger than couplings between the spins, we find strong evidence that
magnetic polarons can be understood as bound states of two partons, a spinon
and a holon carrying spin and charge quantum numbers respectively. We introduce
a microscopic parton description which is benchmarked by comparison with
results from advanced numerical simulations. Using this parton theory, we
predict a series of excited states that are invisible in the spectral function
and correspond to rotational excitations of the spinon-holon pair. This is
reminiscent of mesonic resonances observed in high-energy physics, which can be
understood as rotating quark antiquark pairs. We also apply the strong coupling
parton theory to study far-from equilibrium dynamics of magnetic polarons
observable in current experiments with ultracold atoms. Our work supports
earlier ideas that partons in a confining phase of matter represent a useful
paradigm in condensed-matter physics and in the context of high-Tc
superconductivity. While direct observations of spinons and holons in real
space are impossible in traditional solid-state experiments, quantum gas
microscopes provide a new experimental toolbox. We show that, using this
platform, direct observations of partons in and out-of equilibrium are
possible. Extensions of our approach to the t-J model are also discussed. Our
predictions in this case are relevant to current experiments with quantum gas
microscopes for ultracold atoms.Comment: 30 pages, 4 appendices, 26 figure
String patterns in the doped Hubbard model
Understanding strongly correlated quantum many-body states is one of the most
difficult challenges in modern physics. For example, there remain fundamental
open questions on the phase diagram of the Hubbard model, which describes
strongly correlated electrons in solids. In this work we realize the Hubbard
Hamiltonian and search for specific patterns within the individual images of
many realizations of strongly correlated ultracold fermions in an optical
lattice. Upon doping a cold-atom antiferromagnet we find consistency with
geometric strings, entities that may explain the relationship between hole
motion and spin order, in both pattern-based and conventional observables. Our
results demonstrate the potential for pattern recognition to provide key
insights into cold-atom quantum many-body systems.Comment: 8+28 pages, 5+10 figure
Method for the design of broad energy range focusing reflectrons
A novel method for the design of reflections capable of focusing large kinetic energy ranges is presented. The design method itself is a numerical approach that provides a geometrically flexible alternative to traditional analytical design solutions. This design method has been used to produce a reflectron that provides unit mass resolution for product spectra in a tandem reflectron time-of-flight (TOF) mass spectrometer despite a kinetic energy range of 1950–2700 eV. In this application, the systematic progression of reflectron design results in a practical, nonlinear field reflectron with the use of only two grids. Design improvements are proposed for more flexible systems, although geometric constraints in the current instrument limit their experimental evaluation
Towards Robots that Influence Humans over Long-Term Interaction
When humans interact with robots influence is inevitable. Consider an
autonomous car driving near a human: the speed and steering of the autonomous
car will affect how the human drives. Prior works have developed frameworks
that enable robots to influence humans towards desired behaviors. But while
these approaches are effective in the short-term (i.e., the first few
human-robot interactions), here we explore long-term influence (i.e., repeated
interactions between the same human and robot). Our central insight is that
humans are dynamic: people adapt to robots, and behaviors which are influential
now may fall short once the human learns to anticipate the robot's actions.
With this insight, we experimentally demonstrate that a prevalent
game-theoretic formalism for generating influential robot behaviors becomes
less effective over repeated interactions. Next, we propose three modifications
to Stackelberg games that make the robot's policy both influential and
unpredictable. We finally test these modifications across simulations and user
studies: our results suggest that robots which purposely make their actions
harder to anticipate are better able to maintain influence over long-term
interaction. See videos here: https://youtu.be/ydO83cgjZ2
Classifying Snapshots of the Doped Hubbard Model with Machine Learning
Quantum gas microscopes for ultracold atoms can provide high-resolution
real-space snapshots of complex many-body systems. We implement machine
learning to analyze and classify such snapshots of ultracold atoms.
Specifically, we compare the data from an experimental realization of the
two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped
quantum spin liquid state of resonating valence bond type, and the geometric
string theory, describing a state with hidden spin order. This approach
considers all available information without a potential bias towards one
particular theory by the choice of an observable and can therefore select the
theory which is more predictive in general. Up to intermediate doping values,
our algorithm tends to classify experimental snapshots as
geometric-string-like, as compared to the doped spin liquid. Our results
demonstrate the potential for machine learning in processing the wealth of data
obtained through quantum gas microscopy for new physical insights.Comment: 4 pages, 3 figures + 4 pages, 5 figure
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing data
from quantum simulators, in which results of experiments are sets of snapshots
of many-body states. Recently, they have been successfully applied to
distinguish between snapshots that can not be identified using traditional one
and two point correlation functions. Thus far, the complexity of these models
has inhibited new physical insights from this approach. Here, using a novel set
of nonlinearities we develop a network architecture that discovers features in
the data which are directly interpretable in terms of physical observables. In
particular, our network can be understood as uncovering high-order correlators
which significantly differ between the data studied. We demonstrate this new
architecture on sets of simulated snapshots produced by two candidate theories
approximating the doped Fermi-Hubbard model, which is realized in state-of-the
art quantum gas microscopy experiments. From the trained networks, we uncover
that the key distinguishing features are fourth-order spin-charge correlators,
providing a means to compare experimental data to theoretical predictions. Our
approach lends itself well to the construction of simple, end-to-end
interpretable architectures and is applicable to arbitrary lattice data, thus
paving the way for new physical insights from machine learning studies of
experimental as well as numerical data.Comment: 7 pages, 4 figures + 13 pages of supplemental materia
Electrospun Polyaniline Fibers as Highly Sensitive Room Temperature Chemiresistive Sensors for Ammonia and Nitrogen Dioxide Gases
Electrospun polyaniline (PAni) fibers doped with different levels of (+)-camphor-10-sulfonic acid (HCSA) are fabricated and evaluated as chemiresistive gas sensors. The experimental results, based on both sensitivity and response time, show that doped PAni fibers are excellent ammonia sensors and that undoped PAni fibers are excellent nitrogen dioxide sensors. The fibers exhibit changes in measured resistances up to 60-fold for ammonia sensing, and more than five orders of magnitude for nitrogen dioxide sensing, with characteristic response times on the order of one minute in both cases. A time-dependent reaction-diffusion model is used to extract physical parameters from fitting experimental sensor data. The model is then used to illustrate the selection of optimal material design parameters for gas sensing by nanofibers.Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract ARO W911NF-07-D-0004
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