There is now strong evidence that the superconformal index of holographic gauge theories admits a giant graviton expansion capturing the worldvolume dynamics of spherical D3-branes moving in internal space. While the giant graviton indices may be written as suitable contour integrals, care must be taken in their evaluation, as there can be an ambiguity in choice of integration contour or, equivalently, "pole selection". We resolve this ambiguity by using Murthy's recent expansion of the superconformal index to provide a rigorous underpinning of the evaluation of the supergravity giant graviton indices. In doing so, we directly relate Murthy's giant graviton expansion to the supergravity D3-brane expansion.

There is a long-standing discussion in the astrophysical/astrochemical community as to the structure and morphology of dust grains in various astrophysical environments (e.g., interstellar clouds, protostellar envelopes, protoplanetary and debris disks, and the atmospheres of exoplanets). Typical grain models assume a compact dust core which becomes covered in a thick ice mantle in cold dense environments. In contrast, less compact cores are likely to exhibit porosity, leading to a pronounced increase in surface area with concomitant much thinner ice films and higher accessibility to the bare grain surface. Several laboratory experimental and theoretical studies have shown that this type of dust structure can have a marked effect on several physico-chemical processes, including adsorption, desorption, mobility, and reactivity of chemical species. Porous grains are thus thought to likely play a particularly important and wide-ranging astrochemical role. Herein, we clarify what is meant by porosity in relation to grains and grain agglomerates, assess the likely astrochemical effects of porosity and ask whether a fractal/porous structural/morphological description of dust grains is appropriate from an astronomical perspective. We provide evidence for high porosity from laboratory experiments and computational simulations of grains and their growth in various astrophysical environments. Finally, we assess the observational constraints and perspectives on cosmic dust porosity. Overall, our paper discusses the effects of including porosity in dust models and the need to use such models for future astrophysical, astrochemical and astrobiological studies involving surface or solid-state processes.

Non-Hermitian parity-time ($\mathcal{PT}$) and anti-parity-time ($\mathcal{APT}$)-symmetric systems exhibit novel quantum properties and have attracted increasing interest. Although many counterintuitive phenomena in $\mathcal{PT}$- and $\mathcal{APT}$-symmetric systems were previously studied, coherence flow has been rarely investigated. Here, we experimentally demonstrate single-qubit coherence flow in $\mathcal{PT}$- and $\mathcal{APT}$-symmetric systems using an optical setup. In the symmetry unbroken regime, we observe different periodic oscillations of coherence. Particularly, we observe two complete coherence backflows in one period in the $\mathcal{PT}$-symmetric system, while only one backflow in the $\mathcal{APT}$-symmetric system. Moreover, in the symmetry broken regime, we observe the phenomenon of stable value of coherence flow. We derive the analytic proofs of these phenomena and show that most experimental data agree with theoretical results within one standard deviation. This work opens avenues for future study on the dynamics of coherence in $\mathcal{PT}$- and $\mathcal{APT}$-symmetric systems.

To achieve a fault-tolerant quantum computer, it is crucial to increase the coherence time of quantum bits. In this work, we theoretically investigate a system consisting of a series of superconducting qubits that alternate between XX and YY ultrastrong interactions. By considering the two-lowest energy eigenstates of this system as a {\it logical} qubit, we demonstrate that its coherence is significantly enhanced: both its pure dephasing and relaxation times are extended beyond those of individual {\it physical} qubits. Specifically, we show that by increasing either the interaction strength or the number of physical qubits in the chain, the logical qubit's pure dephasing rate is suppressed to zero, and its relaxation rate is reduced to half the relaxation rate of a single physical qubit. Single qubit and two-qubit gates can be performed with a high fidelity.

We investigate microwave interference from a spin ensemble and its mirror image in a one-dimensional waveguide. Away from the mirror, the resonance frequencies of the Kittel mode (KM) inside a ferrimagnetic spin ensemble have sinusoidal shifts as the normalized distance between the spin ensemble and the mirror increases compared to the setup without the mirror. These shifts are a consequence of the KM's interaction with its own image. Furthermore, the variation of the magnon radiative decay into the waveguide shows a cosine squared oscillation and is enhanced twofold when the KM sits at the magnetic antinode of the corresponding eigenmode. We can finely tune the KM to achieve the maximum adsorption of the input photons at the critical coupling point. Moreover, by placing the KM in proximity to the node of the resonance field, its lifetime is extended to more than eight times compared to its positioning near the antinode.

We present QuantumToolbox.jl, an open-source Julia package for simulating open quantum systems. Designed with a syntax familiar to users of QuTiP (Quantum Toolbox in Python), it harnesses Julia's high-performance ecosystem to deliver fast and scalable simulations. The package includes a suite of time-evolution solvers supporting distributed computing and GPU acceleration, enabling efficient simulation of large-scale quantum systems. We also show how QuantumToolbox.jl can integrate with automatic differentiation tools, making it well-suited for gradient-based optimization tasks such as quantum optimal control. Benchmark comparisons demonstrate substantial performance gains over existing frameworks. With its flexible design and computational efficiency, QuantumToolbox.jl serves as a powerful tool for both theoretical studies and practical applications in quantum science.

In quantum-optics experiments with both natural and artificial atoms, the atoms are usually small enough that they can be approximated as point-like compared to the wavelength of the electromagnetic radiation they interact with. However, superconducting qubits coupled to a meandering transmission line, or to surface acoustic waves, can realize "giant artificial atoms" that couple to a bosonic field at several points which are wavelengths apart. Here, we study setups with multiple giant atoms coupled at multiple points to a one-dimensional (1D) waveguide. We show that the giant atoms can be protected from decohering through the waveguide, but still have exchange interactions mediated by the waveguide. Unlike in decoherence-free subspaces, here the entire multi-atom Hilbert space is protected from decoherence. This is not possible with "small" atoms. We further show how this decoherence-free interaction can be designed in setups with multiple atoms to implement, e.g., a 1D chain of atoms with nearest-neighbor couplings or a collection of atoms with all-to-all connectivity. This may have important applications in quantum simulation and quantum computing.

Strongly correlated photons play a crucial role in modern quantum technologies. Here, we investigate the probability of generating strongly correlated photons in a chain of N qubits coupled to a one-dimensional (1D) waveguide. We found that disorder in the transition frequencies can induce photon antibunching, and especially nearly perfect photon blockade events in the transmission and reflection outputs. As a comparison, in ordered chains, strongly correlated photons cannot be generated in the transmission output, and only weakly antibunched photons are found in the reflection output. The occurrence of nearly perfect photon blockade events stems from the disorder-induced near completely destructive interference of photon scattering paths. Our work highlights the impact of disorder on photon correlation generation and suggests that disorder can enhance the potential for achieving strongly correlated photon.

Variability is a fundamental signature for active galactic nuclei (AGN) activity, and serve as an unbiased indicator for rapid instability happened near the center supermassive black hole (BH). Previous studies showed that AGN variability does not have strong redshift evolution, and scales with their bolometric luminosity and BH mass, making it a powerful probe to identify low-mass, low-luminosity AGNs at high redshift. JWST has discovered a new population of high-redshift galaxies likely hosting moderate accreting BHs ($10^6\,M_\odot$) -- the little red dots (LRDs, $z=4-10$). In this paper, we study the variability of a sample of 21 LRDs with V-shaped SEDs in three JWST deep fields that also have reliable HST observations in closely paired filters at 1-2 um (rest-frame UV), with the time difference between 6 and 11 years. This LRD sample covers a redshift range of $3

Very Faint X-ray Transients (VFXTs) are a class of X-ray binary systems that exhibit occasional outbursts with peak X-ray luminosities (L_X< 1e36 erg s^-1) much lower than typical X-ray transients. On 22nd February 2024, during its daily Galactic center monitoring, Swift-XRT detected a VFXT, 7 arcmin from Sgr A* dubbing it Swift J174610--290018. We aim to characterize the outburst that occurred in 2024, and a second, distinct outburst in 2025, to understand the nature and accretion flow properties of this new VFXT. Swift-XRT light curves are used to constrain the duration of the two events. We carried out X-ray spectral analysis exploiting XMM and NuSTAR data. We used Chandra and XMM observations of the last 25 years to constrain the quiescent luminosity of the source. During the 2024 outburst, which lasted about 50 days, the source reached a luminosity in the 2-10 keV band of L_X = 1.2e35 erg s^-1 (assuming it is located at the Galactic center). The 2025 outburst is shorter (about 5 days), and reached L_X = 9e34 erg s^-1. The spectral features of the source include an excess at 6.5-7 keV, which can be associated either with a single reflection line or with the ionized Fe XXV and XXVI lines. The same source was identified in both the XMM and Chandra catalogs of point sources (known as 4XMM J174610.7--290020). During previous detections, the source displayed luminosity levels ranging from L_X= 2e32 to L_X = 3e34 erg s^-1 between 2000 and 2010. Moreover, it exhibited a potential type I X-ray burst in 2004. The analysis of the outbursts and the potential type I burst strongly suggests the neutron star low mass X-ray binary (NS-LMXB) nature of the VFXT. The source can be described by an accretion disk corona (as has been recently proposed by the XRISM/Xtend analysis). This scenario explains the overall low luminosity of this transient and the peculiar iron lines in the spectrum.

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases.

Certifying nonclassical correlations typically requires access to all subsystems, presenting a major challenge in open quantum systems coupled to inaccessible environments. Recent works have shown that, in autonomous pure dephasing scenarios, quantum discord with the environment can be certified from system-only dynamics via the Hamiltonian ensemble formulation. However, this approach leaves open whether stronger correlations, such as entanglement, can be certified. Moreover, its reliance on Fourier analysis requires full-time dynamics, which is experimentally resource-intensive and provides limited information about when such correlations are established during evolution. In this work, we present a method that enables the certification of system-environment quantum entanglement solely from the reduced dynamics of the system. The method is based on the theory of mixed-unitary channels and applies to general non-autonomous pure dephasing scenarios. Crucially, it relaxes the need for full-time dynamics, offering a resource-efficient approach that also reveals the precise timing of entanglement generation. We experimentally validate this method on a Quantinuum trapped-ion quantum processor with a controlled-dephasing model. Finally, we highlight its potential as a tool for certifying gravitationally induced entanglement.

The control of individual quantum systems is now a reality in a variety of physical settings. Feedback control is an important class of control methods because of its ability to reduce the effects of noise. In this review we give an introductory overview of the various ways in which feedback may be implemented in quantum systems, the theoretical methods that are currently used to treat it, the experiments in which it has been demonstrated to-date, and its applications. In the last few years there has been rapid experimental progress in the ability to realize quantum measurement and control of mesoscopic systems. We expect that the next few years will see further rapid advances in the precision and sophistication of feedback control protocols realized in the laboratory.

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.

We consider a fundamental string in a bubbling geometry of arbitrary genus dual to a half-supersymmetric Wilson loop in a general large representation $\mathbf{R}$ of the $SU(N)$ gauge group in ${\cal N}=4$ Supersymmetric Yang-Mills. We demonstrate, under some mild conditions, that the minimum value of the string classical action for a bubbling geometry of arbitrary genus precisely matches the correlator of a Wilson loop in the fundamental representation and one in a general large representation. We work out the case in which the large representation is given by a rectangular Young Tableau, corresponding to a genus one bubbling geometry, explicitly. We also present explicit results in the field theory for a correlator of two Wilson loops: a large one in an arbitrary representation and a "small" one in the fundamental, totally symmetric or totally antisymmetric representation.

Critical evaluation and understanding of ship responses in the ocean is important for not only the design and engineering of future platforms but also the operation and safety of those that are currently deployed. Simulations or experiments are typically performed in nominal sea conditions during ship design or prior to deployment and the results may not be reflective of the instantaneous state of the vessel and the ocean environment while deployed. Short-term temporal predictions of ship responses given the current wave environment and ship state would enable enhanced decision-making onboard for both manned and unmanned vessels. However, the current state-of-the-art in numerical hydrodynamic simulation tools are too computationally expensive to be employed for real-time ship motion forecasting and the computationally efficient tools are too low fidelity to provide accurate responses. A methodology is developed with long short-term memory (LSTM) neural networks to represent the motions of a free running David Taylor Model Basin (DTMB) 5415 destroyer operating at 20 knots in Sea State 7 stern-quartering irregular seas. Case studies are performed for both course-keeping and turning circle scenarios. An estimate of the vessel's encounter frame is made with the trajectories observed in the training dataset. Wave elevation time histories are given by artificial wave probes that travel with the estimated encounter frame and serve as input into the neural network, while the output is the 6-DOF temporal ship motion response. Overall, the neural network is able to predict the temporal response of the ship due to unseen waves accurately, which makes this methodology suitable for system identification and real-time ship motion forecasting. The methodology, the dependence of model accuracy on wave probe and training data quantity and the estimated encounter frame are all detailed.

This thesis explores topics related to the study of quantum gravity, with a focus on precision holography and higher-derivative supergravity. First, we study subleading corrections to the free energy of a particular 3D N=3 Chern-Simons-matter theory found by Gaiotto and Tomasiello, which is given by a matrix model after supersymmetric localization. This theory is dual to massive IIA supergravity on AdS4, and consequently, the structure of subleading corrections to the field theory naturally elucidates the higher-derivative corrections to the gravity dual. We extract the first order of corrections to the free energy using resolvent methods, and our results imply that particular terms in the supergravity action should vanish on-shell. Next, we consider the unreasonable effectiveness of five-dimensional minimal gauged supergravity. There are three independent supersymmetric four-derivative terms that one can add to the action; nevertheless, after going on-shell (or, equivalently, after a field redefinition that pushes the off-shell discrepancies to six-derivative order), there is a unique supersymmetric invariant. Third, we consider the effect of higher-derivative corrections in holographic renormalization group flows across dimensions. In particular, we construct a local holographic c-function out of metric functions and show its monotonicity via the Null Energy Condition. We also construct a c-function from the entanglement entropy for flows with a CFT2 IR fixed point, and we show that such flows are monotonic. Finally, we consider consistent truncations of four-derivative heterotic supergravity. In particular, we show that reducing both on a torus $T^n$ or on $S^3$ and truncating the vector multiplets is indeed a consistent truncation at the four-derivative level. Moreover, we find examples of two-derivative consistent truncations which fail to extend to four-derivative ones.

Frustration, that is, the impossibility to satisfy the energetic preferences between all spin pairs simultaneously, underlies the complexity of many fundamental properties in spin systems, including the computational hardness to determine their ground states. Coherent Ising machines (CIM) have been proposed as a promising analog computational approach to efficiently find different degenerate ground states of large and complex Ising models. However, CIMs also face challenges in solving frustrated Ising models: Frustration not only reduces the probability to find good solutions, but it also prohibits to leverage quantum effects in doing so. To circumvent these detrimental effects of frustration, we show how frustrated Ising models can be mapped to frustration-free CIM configurations by including ancillary modes and modifying the coupling protocol used in current CIM designs. In our proposal, degenerate optical parametric oscillator (DOPO) modes encode the ground state candidates of the studied Ising model, while the ancillary modes enable the autonomous transformation to a frustration-free Ising model that preserves the ground states encoded in the DOPO modes. Such frustration elimination may empower current CIMs to improve precision and to benefit from quantum effects in dealing with frustrated Ising models.

Reinforcement learning (RL) based investment strategies have been widely adopted in portfolio management (PM) in recent years. Nevertheless, most RL-based approaches may often emphasize on pursuing returns while ignoring the risks of the underlying trading strategies that may potentially lead to great losses especially under high market volatility. Therefore, a risk-manageable PM investment framework integrating both RL and barrier functions (BF) is proposed to carefully balance the needs for high returns and acceptable risk exposure in PM applications. Up to our understanding, this work represents the first attempt to combine BF and RL for financial applications. While the involved RL approach may aggressively search for more profitable trading strategies, the BF-based risk controller will continuously monitor the market states to dynamically adjust the investment portfolio as a controllable measure for avoiding potential losses particularly in downtrend markets. Additionally, two adaptive mechanisms are provided to dynamically adjust the impact of risk controllers such that the proposed framework can be flexibly adapted to uptrend and downtrend markets. The empirical results of our proposed framework clearly reveal such advantages against most well-known RL-based approaches on real-world data sets. More importantly, our proposed framework shed lights on many possible directions for future investigation.

Highly efficient transfer of quantum resources including quantum excitations, states, and information on quantum networks is an important task in quantum information science. Here, we propose a bipartite-graph framework for studying quantum excitation transfer in bosonic networks by diagonalizing the intermediate sub-network between the sender and the receiver to construct a bipartite-graph configuration. We examine the statistical properties of the bosonic networks in both the original and bipartite-graph representations. In particular, we investigate quantum excitation transfer in both the finite and infinite intermediate-normal-mode cases, and show the dependence of the transfer efficiency on the network configurations and system parameters. We find the bound of maximally transferred excitations for various network configurations and reveal the underlying physical mechanisms. We also find that the dark-mode effect will degrade the excitation transfer efficiency. Our findings provide a new insight for the design and optimization of quantum networks.