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Short intense laser pulses are routinely used to induce rotational wave packet dynamics of molecules. Ro-vibrational wave packet dynamics has been explored comparatively infrequently, focusing predominantly on extremely light and rigid molecules such as H$_2^+$, H$_2$, and D$_2$. This work presents quantum mechanical calculations that account for the rotational {\em{and}} the vibrational degrees of freedom for a heavier and rather floppy diatomic molecule, namely the neon dimer. For pumping by a strong and short non-resonant pump pulse, we identify several phenomena that depend critically on the vibrational (i.e., radial) degree of freedom. Our calculations show (i) fingerprints of the radial dynamics in the alignment signal; (ii) laser-kick induced dissociative dynamics on very short time scales (ejection of highly structured "jets"); and (iii) tunneling dynamics that signifies the existence of resonance states, which are supported by the effective potential curves for selected finite relative angular momenta. Our theory predictions can be explored by existing state-of-the-art experiments., Comment: 7 figures

We investigate the influence of the equation-of-state (EoS) of nuclear matter on collective observables, the directed ($v_1$) and the elliptic flow ($v_2$) of nucleons and light clusters in heavy-ion collisions at GeV beam energies employing the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) microscopic transport approach. Here the clusters are formed dynamically during the entire heavy-ion collision by potential interaction between nucleons, including additionally deuteron production by hadronic kinetic reactions. We employ three different EoS - realized via potential interactions: two static EoS, dubbed 'soft' and 'hard', which differ in the compressibility modulus, as well as a soft momentum dependent EoS, adjusted to pA elastic scattering data. We find that the momentum dependent potential has different consequences for rapidity and transverse momentum spectra than for flow coefficients. We obtain the best description of the HADES and FOPI data on the directed and elliptic flow coefficients of protons and light clusters applying a momentum dependent EoS. Moreover, we observe a scaling behavior of $v_2$ versus $p_T$ with atomic number $A$. Finally we demonstrate that flow observables can help to identify the cluster production mechanisms.

We investigate light cluster and anti-cluster production in heavy-ion collisions from SIS to RHIC energies within the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) microscopic transport approach which propagates (anti-)baryons using n-body QMD dynamics. In PHQMD the clusters are formed dynamically by potential interactions between baryons - and recognized by the Minimum Spanning Tree (MST) algorithm - as well as by kinetic reactions in case of deuterons. We present the novel PHQMD results for different observables such as excitation functions of the multiplicity of deuterons, anti-deuterons and tritons, as well as their transverse momentum spectra. Moreover, we investigate the system size dependence of proton and deuteron production in p+A collisions and show the PHQMD results for p+A collisions (A = Be, Al, Cu, Au) at 14 AGeV/c, as well as for asymmetric Au+A collisions (A = Al, Cu, Pb) at a bombarding energy of about 10 AGeV., Comment: Contribution to: QNP2024

High-fidelity mid-circuit measurements, which read out the state of specific qubits in a multiqubit processor without destroying them or disrupting their neighbors, are a critical component for useful quantum computing. They enable fault-tolerant quantum error correction, dynamic circuits, and other paths to solving classically intractable problems. But there are almost no methods to assess their performance comprehensively. We address this gap by introducing the first randomized benchmarking protocol that measures the rate at which mid-circuit measurements induce errors in many-qubit circuits. Using this protocol, we detect and eliminate previously undetected measurement-induced crosstalk in a 20-qubit trapped-ion quantum computer. Then, we use the same protocol to measure the rate of measurement-induced crosstalk error on a 27-qubit IBM Q processor, and quantify how much of that error is eliminated by dynamical decoupling., Comment: 7 pages, 4 figures, plus supplemental notes

Spin precession is a textbook example of dynamics of a quantum system that exactly mimics its classical counterpart. Here we challenge this view by certifying the quantumness of exotic states of a nuclear spin through its uniform precession. The key to this result is measuring the positivity, instead of the expectation value, of the $x$-projection of the precessing spin, and using a spin > 1/2 qudit, that is not restricted to semi-classical spin coherent states. The experiment is performed on a single spin-7/2 $^{123}$Sb nucleus, implanted in a silicon nanoelectronic device, amenable to high-fidelity preparation, control, and projective single-shot readout. Using Schr\"odinger cat states and other bespoke states of the nucleus, we violate the classical bound by 19 standard deviations, proving that no classical probability distribution can explain the statistic of this spin precession, and highlighting our ability to prepare quantum resource states with high fidelity in a single atomic-scale qudit., Comment: Main text: 11 pages, 5 figures. Supplementary information: 13 pages, 11 figures

In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image-weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 48.7% mAP improvement on ODinW and 59.1% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.

Facial expression perception in humans inherently relies on prior knowledge and contextual cues, contributing to efficient and flexible processing. For instance, multi-modal emotional context (such as voice color, affective text, body pose, etc.) can prompt people to perceive emotional expressions in objectively neutral faces. Drawing inspiration from this, we introduce a novel approach for facial expression classification that goes beyond simple classification tasks. Our model accurately classifies a perceived face and synthesizes the corresponding mental representation perceived by a human when observing a face in context. With this, our model offers visual insights into its internal decision-making process. We achieve this by learning two independent representations of content and context using a VAE-GAN architecture. Subsequently, we propose a novel attention mechanism for context-dependent feature adaptation. The adapted representation is used for classification and to generate a context-augmented expression. We evaluate synthesized expressions in a human study, showing that our model effectively produces approximations of human mental representations. We achieve State-of-the-Art classification accuracies of 81.01% on the RAVDESS dataset and 79.34% on the MEAD dataset. We make our code publicly available., Comment: GCPR 2024

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this Tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV's core models and concepts -- quantum states, measurements, and processes -- and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This Tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

A key capability in managing patent applications or a patent portfolio is comparing claims to other text, e.g. a patent specification. Because the language of claims is different from language used elsewhere in the patent application or in non-patent text, this has been challenging for computer based natural language processing. We test two new LLM-based approaches and find that both provide substantially better performance than previously published values. The ability to match dense information from one domain against much more distributed information expressed in a different vocabulary may also be useful beyond the intellectual property space., Comment: 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2024) at ACM SIGIR

The rapid pace of development in quantum computing technology has sparked a proliferation of benchmarks for assessing the performance of quantum computing hardware and software. Good benchmarks empower scientists, engineers, programmers, and users to understand a computing system's power, but bad benchmarks can misdirect research and inhibit progress. In this Perspective, we survey the science of quantum computer benchmarking. We discuss the role of benchmarks and benchmarking, and how good benchmarks can drive and measure progress towards the long-term goal of useful quantum computations, i.e., "quantum utility". We explain how different kinds of benchmark quantify the performance of different parts of a quantum computer, we survey existing benchmarks, critically discuss recent trends in benchmarking, and highlight important open research questions in this field.

Dynamic polarizabilities of cesium Rydberg states, explicitly $nS_{1/2}$, $nP_{1/2}$, $nP_{3/2}$, $nD_{3/2}$, and $nD_{5/2}$, where the principal quantum number $n$ is $40$ to $70$, are presented for linearly polarized light. The dynamic polarizability is calculated using the sum-over-states approach. We identify double magic wavelengths in the range of $1,000-2,000$~nm for simultaneous trapping of the ground state and a Rydberg state, which are, respectively, red-detuned and blue-detuned with respect to a low-lying excited auxiliary state. Based on calculations of the radiative lifetime, blackbody radiation induced transitions, and population transfer out of the Rydberg and auxiliary states (estimated within two-state as well as master equation models), we conclude that magic wavelength trapping is particularly promising experimentally for the $nD_{J,|M_J|}$ Rydberg series with angular momentum $J=3/2$ and projection quantum numbers $M_J=\pm 1/2$ (auxiliary state $8P_{1/2}$) and $M_J=\pm 3/2$ (auxiliary state $8P_{3/2}$), using trap depths as large as $10$~$\mu$K. Moreover, by tuning the angle between the quantization axis and the polarization vector of the light, we identify triple and quadruple magic wavelengths, for which the polarizabilities of the ground state, a Rydberg state, and, respectively, one and two low-lying excited states are equal. Our comprehensive theoretical study provides much needed guidance for on-going experimental efforts on cesium Rydberg-state based quantum simulations that operate on time scales up to several $\mu$s., Comment: 21 pages and 14 cpationed figures

While the thermodynamics for bosonic systems with weak $s$-wave interactions has been known for decades, a general and systematic extension to higher partial waves has not yet been reported. We provide closed-form expressions for the equations of state for weakly interacting systems with arbitrary partial waves in the normal phase. Thermodynamics, including contact, loss rate, and compressibility, are derived over the entire temperature regime. Our results offer an improved thermometer for ultracold atoms and molecules with weak high-partial wave interactions., Comment: 15 pages, 3 figures

High-dimensional quantum systems are a valuable resource for quantum information processing. They can be used to encode error-correctable logical qubits, for instance in continuous-variable states of oscillators such as microwave cavities or the motional modes of trapped ions. Powerful encodings include 'Schr\"odinger cat' states, superpositions of widely displaced coherent states, which also embody the challenge of quantum effects at the large scale. Alternatively, recent proposals suggest encoding logical qubits in high-spin atomic nuclei, which can host hardware-efficient versions of continuous-variable codes on a finite-dimensional system. Here we demonstrate the creation and manipulation of Schr\"odinger cat states using the spin-7/2 nucleus of a single antimony ($^{123}$Sb) atom, embedded and operated within a silicon nanoelectronic device. We use a coherent multi-frequency control scheme to produce spin rotations that preserve the SU(2) symmetry of the qudit, and constitute logical Pauli operations for logical qubits encoded in the Schr\"odinger cat states. The Wigner function of the cat states exhibits parity oscillations with a contrast up to 0.982(5), and state fidelities up to 0.913(2). These results demonstrate high-fidelity preparation of nonclassical resource states and logical control in a single atomic-scale object, opening up applications in quantum information processing and quantum error correction within a scalable, manufacturable semiconductor platform., Comment: 40 pages including main and supplementary information

Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that capture human facial expressions, such systems should utilize raw unlabeled videos without requiring expensive annotations. Therefore, in this work, we employ a multitask multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our model combines three self-supervised objective functions: First, a multi-modal contrastive loss, that pulls diverse data modalities of the same video together in the representation space. Second, a multi-modal clustering loss that preserves the semantic structure of input data in the representation space. Finally, a multi-modal data reconstruction loss. We conduct a comprehensive study on this multimodal multi-task self-supervised learning method on three facial expression recognition benchmarks. To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task. Our model ConCluGen outperforms several multi-modal self-supervised and fully supervised baselines on the CMU-MOSEI dataset. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks such as facial expression recognition, while also reducing the amount of manual annotations required. We release our pre-trained models as well as source code publicly, Comment: The paper will appear in the CVPR 2024 workshops proceedings

We investigate whether region-based representations are effective for recognition. Regions were once a mainstay in recognition approaches, but pixel and patch-based features are now used almost exclusively. We show that recent class-agnostic segmenters like SAM can be effectively combined with strong unsupervised representations like DINOv2 and used for a wide variety of tasks, including semantic segmentation, object-based image retrieval, and multi-image analysis. Once the masks and features are extracted, these representations, even with linear decoders, enable competitive performance, making them well suited to applications that require custom queries. The compactness of the representation also makes it well-suited to video analysis and other problems requiring inference across many images., Comment: CVPR 2024 Camera Ready; website: https://regionreps.web.illinois.edu/

Driven classical self-sustained oscillators have been studied extensively in the context of synchronization. Using the master equation, this work considers the classically driven generalized quantum Rayleigh-van der Pol oscillator, which is characterized by linear dissipative gain and loss terms as well as three non-linear dissipative terms. Since two of the non-linear terms break the rotational phase space symmetry, the Wigner distribution of the quantum mechanical limit cycle state of the undriven system is, in general, not rotationally symmetric. The impact of the symmetry-breaking dissipators on the long-time dynamics of the driven system are analyzed as functions of the drive strength and detuning, covering the deep quantum to near-classical regimes. Phase localization and frequency entrainment, which are required for synchronization, are discussed in detail. We identify a large parameter space where the oscillators exhibit appreciable phase localization but only weak or no entrainment, indicating the absence of synchronization. Several observables are found to exhibit the analog of the celebrated classical Arnold tongue; in some cases, the Arnold tongue is found to be asymmetric with respect to vanishing detuning between the external drive and the natural oscillator frequency., Comment: 9 figures (several sub-figures), 17+ pages

Recent observations of Rossby waves and other more exotic forms of inertial oscillations in the Sun's convection zone have kindled the hope that such waves might be used as a seismic probe of the Sun's interior. Here we present a 3D numerical simulation in spherical geometry that models the Sun's convection zone and upper radiative interior. This model features a wide variety of inertial oscillations, including both sectoral and tesseral equatorial Rossby waves, retrograde mixed inertial modes, prograde thermal Rossby waves, the recently observed high-frequency retrograde (HFR) vorticity modes, and what may be latitudinal overtones of these HFR modes. With this model, we demonstrate that sectoral and tesseral Rossby waves are ubiquitous within the radiative interior as well as within the convection zone. We suggest that there are two different Rossby-wave families in this simulation that live in different wave cavities: one in the radiative interior and one in the convection zone. Finally, we suggest that many of the retrograde inertial waves that appear in the convection zone, including the HFR modes, are in fact all related, being latitudinal overtones that are mixed modes with the prograde thermal Rossby waves., Comment: 23 pages, 14 figures, submitted to The Astrophysical Journal

Sensing plays a crucial role in autonomous and assisted vehicular driving, as well as in the operation of autonomous drones. The traditional segregation of communication and onboard sensing systems in mobility applications is due to be merged using Joint Communication and Sensing (JCAS) in the development of the 6G mobile radio standard. The integration of JCAS functions into the future road traffic landscape introduces novel challenges for the design of the 6G system architecture. Special emphasis will be placed on facilitating direct communication between road users and aerial drones. In various mobility scenarios, diverse levels of integration will be explored, ranging from leveraging communication capabilities to coordinate different radars to achieving deep integration through a unified waveform. In this paper, we have identified use cases and derive five higher-level Tech Cases (TCs). Technical and functional requirements for the 6G system architecture for a device-oriented JCAS approach will be extracted from the TCs and used to conceptualize the architectural views., Comment: 6 pages, 3 figures, 4th IEEE Symposium on Joint Communication and Sensing