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 $1.0\,k_{\mathrm{B}}$. In the band-insulator we find local entropies as low as $0.5\,k_{\mathrm{B}}$. 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