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Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation. The framing of a camera often significantly contributes to the story telling in movies, games, and mixed reality applications. Generating single camera poses or even contiguous trajectories either requires a significant amount of manual labor or requires solving highdimensional optimization problems, which can be computationally demanding and error-prone. In this paper, we introduce GAIT, a Deep Reinforcement Learning (DRL) agent, that learns to automatically control a camera to generate a sequence of aesthetically meaningful views for synthetic 3D indoor scenes. To generate sequences of frames with high aesthetic value, GAIT relies on a neural aesthetics estimator, which is trained on a crowed-sourced dataset. Additionally, we introduce regularization techniques for diversity and smoothness to generate visually interesting trajectories for a 3D environment, and to constrain agent acceleration in the reward function to generate a smooth sequence of camera frames. We validated our method by comparing it to baseline algorithms, based on a perceptual user study, and through ablation studies. The source code of our method will be released with the final version of our paper. 

Augmented Reality (AR) is widely considered the next evolution in personal devices, enabling seamless integration of the digital world into our reality. Such integration, however, often requires unfettered access to sensor data, causing significant over privilege for applications that run on these platforms. Through analysis of 17 AR systems and 45 popular AR applications, we explore existing mechanisms for access control in AR platforms, identify key trends in how AR applications use sensor data, and pinpoint unique threats users face in AR environments. Using these findings, we design and implement Erebus, an access control framework for AR platforms that enables fine-grained control over data used by AR applications. Erebus achieves the principle of least privileged through the creation of a domain-specific language (DSL) for permission control in AR platforms, allowing applications to specify data needed for their functionality. Using this DSL, Erebus further enables users to customize app permissions to apply under specific user conditions. We implement Erebus on Google’s ARCore SDK and port five existing AR applications to demonstrate the capability of Erebus to secure various classes of apps. Performance results using these applications and various microbenchmarks show that Erebus achieves its security goals while being practical, introducing negligible performance overhead to the AR system. 

Simulating the properties of many-body fermionic systems is an outstanding computational challenge relevant to material science, quantum chemistry, and particle physics.-5.4pc]Please note that the spelling of the following author names in the manuscript differs from the spelling provided in the article metadata: D. González-Cuadra, D. Bluvstein, M. Kalinowski, R. Kaubruegger, N. Maskara, P. Naldesi, T. V. Zache, A. M. Kaufman, M. D. Lukin, H. Pichler, B. Vermersch, Jun Ye, and P. Zoller. The spelling provided in the manuscript has been retained; please confirm. Although qubit-based quantum computers can potentially tackle this problem more efficiently than classical devices, encoding nonlocal fermionic statistics introduces an overhead in the required resources, limiting their applicability on near-term architectures. In this work, we present a fermionic quantum processor, where fermionic models are locally encoded in a fermionic register and simulated in a hardware-efficient manner using fermionic gates. We consider in particular fermionic atoms in programmable tweezer arrays and develop different protocols to implement nonlocal gates, guaranteeing Fermi statistics at the hardware level. We use this gate set, together with Rydberg-mediated interaction gates, to find efficient circuit decompositions for digital and variational quantum simulation algorithms, illustrated here for molecular energy estimation. Finally, we consider a combined fermion-qubit architecture, where both the motional and internal degrees of freedom of the atoms are harnessed to efficiently implement quantum phase estimation as well as to simulate lattice gauge theory dynamics. 

The generation of long-lived entanglement on an optical clock transition is a key requirement to unlocking the promise of quantum metrology. Arrays of neutral atoms constitute a capable quantum platform for accessing such physics, where Rydberg-based interactions may generate entanglement between individually controlled and resolved atoms. To this end, we leverage the programmable state preparation afforded by optical tweezers along with the efficient strong confinement of a 3d optical lattice to prepare an ensemble of strontium atom pairs in their motional ground state. We engineer global single-qubit gates on the optical clock transition and two-qubit entangling gates via adiabatic Rydberg dressing, enabling the generation of Bell states, |ψ⟩=12√(|gg⟩+i|ee⟩), with a fidelity of F=92.8(2.0)%. For use in quantum metrology, it is furthermore critical that the resulting entanglement be long lived; we find that the coherence of the Bell state has a lifetime of τbc=4.2(6) s via parity correlations and simultaneous comparisons between entangled and unentangled ensembles. Such Bell states can be useful for enhancing metrological stability and bandwidth. Further rearrangement of hundreds of atoms into arbitrary configurations using optical tweezers will enable implementation of many-qubit gates and cluster state generation, as well as explorations of the transverse field Ising model and Hubbard models with entangled or finite-range-interacting tunnellers.