Probing site-resolved correlations in a spin system of ultracold molecules

Synthetic quantum systems with interacting constituents play an important role in quantum information processing and in elucidating fundamental phenomena in many-body physics. Following impressive advances in cooling and trapping techniques, ensembles of ultracold polar molecules have emerged as a promising synthetic system that combines several advantageous properties. These include a large set of internal states for encoding quantum information, long nuclear and rotational coherence times and long-range, anisotropic interactions. The latter are expected to allow the exploration of intriguing phases of correlated quantum matter, such as topological superfluids, quantum spin liquids, fractional Chern insulators and quantum magnets. Probing correlations in these phases is crucial to understand their microscopic properties, necessitating the development of new experimental techniques. Here we use quantum gas microscopy to measure the site-resolved dynamics of quantum correlations in a gas of polar molecules in a two-dimensional optical lattice. Using two rotational states of the molecules, we realize a spin-1/2 system where the particles are coupled via dipolar interactions, producing a quantum spin-exchange model. Starting with the synthetic spin system prepared far from equilibrium, we study the evolution of correlations during the thermalization process for both spatially isotropic and anisotropic interactions. Furthermore, we study the correlation dynamics in a spin-anisotropic Heisenberg model engineered from the native spin-exchange model using Floquet techniques. These experiments push the frontier of probing and controlling interacting systems of ultracold molecules, with prospects for exploring new regimes of quantum matter and characterizing entangled states useful for quantum computation and metrology

A two-dimensional programmable tweezer array of fermions

We prepare high-filling two-component arrays of up to fifty fermionic atoms in optical tweezers, with the atoms in the ground motional state of each tweezer. Using a stroboscopic technique, we configure the arrays in various two-dimensional geometries with negligible Floquet heating. Full spin- and density-resolved readout of individual sites allows us to post-select near-zero entropy initial states for fermionic quantum simulation. We prepare a correlated state in a two-by-two tunnel-coupled Hubbard plaquette, demonstrating all the building blocks for realizing a programmable fermionic quantum simulator

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Programmable Quantum Simulation with Fermionic Atoms and Polar Molecules

In the first part of this thesis, we describe the development of a programmable Fermi-Hubbard tweezer array using Fermi gases of lithium-6. Using a stroboscopic technique, we demonstrate a two-dimensional tweezer array which can realize lattices of arbitrary geometries including triangular, Lieb, and octagonal ring lattices. Fermions loaded into the array tunnel between different tweezers and experience strong on-site interactions. Full spin- and charge-resolved readout of the system using bilayer imaging enables post-selection of near-zero entropy initial states for quantum simulation. We demonstrate a two-by-two Fermi-Hubbard plaquette, which provides a building block for a 2D Fermi-Hubbard quantum simulator with software-defined geometry. In the second part of this thesis, we describe our theoretical contributions to an experiment studying non-equilibrium spin dynamics using a 2D polar molecule array with dipole-dipole interactions using ultracold NaRb molecules. The experiment prepares rovibrational ground state molecules from Feshbach molecules in an optical lattice. The polar molecules realize a site-diluted 2D quantum XY model with long-range interactions. Using a novel molecular quantum gas microscope, molecules in one of the spin states are detected with single-site resolution. We compare the experimental measurements of the time-evolution of the spin correlation function following a quench with exact diagonalization simulations. We find good agreement of the simulations with the experiments in spin systems with isotropic or anisotropic interactions

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.Comment: Project website: https://droid-dataset.github.io

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist"X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io.</p