This paper numerically studies the time evolution of s-wave scalar probe field in a black hole of which the event horizon is surrounded by matter. As a toy model, it encodes the effects of matter into deformations of Regge-Wheeler potential. It considers three different types of local deformations in the vicinity of the event horizon, the negative static bump potential, the stochastic potential and bump potential modulated by time function in low frequency limit. Our numerical results show that infinitesimal local deformations on Regge-Wheeler potential near the horizon can overturn stability of an arbitrarily strongly stable black hole, implying that late-time behavior of a stable black hole is extremely sensitive to geometry near horizon. Specially, certain deformations that stabilize systems in flat backgrounds can destabilize otherwise stable black holes. It also shows that horizon-induced redshift transforms near-horizon quantum fluctuations into classical-scale stochastic deformations capable of triggering instability, implying that even an isolated black hole cannot keep stable in extended timescales. less

Quantum reservoir computing (QRC) offers a promising framework for online quantum-enhanced machine learning tailored to temporal tasks, yet practical implementations with native memory capabilities remain limited. Here, we demonstrate an optical QRC platform based on deterministically generated multimode squeezed states, exploiting both spectral and temporal multiplexing in a fully continuous-variable (CV) setting, and enabling controlled fading memory. Data is encoded via programmable phase shaping of the pump in an optical parametric process and retrieved through mode-selective homodyne detection. Real-time memory is achieved through feedback using electro-optic phase modulation, while long-term dependencies are achieved via spatial multiplexing. This architecture with minimal post-processing performs nonlinear temporal tasks, including parity checking and chaotic signal forecasting, with results corroborated by a high-fidelity Digital Twin. We show that leveraging the entangled multimode structure significantly enhances the expressivity and memory capacity of the quantum reservoir. This work establishes a scalable photonic platform for quantum machine learning, operating in CV encoding and supporting practical quantum-enhanced information processing. less

We experimentally demonstrate that the bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm, implemented on IonQ's fully connected trapped-ion quantum processors, offers an efficient approach to solving dense higher-order unconstrained binary optimization (HUBO) problems. Specifically, we tackle protein folding on a tetrahedral lattice for up to 12 amino acids, representing the largest quantum hardware implementations of protein folding problems reported to date. Additionally, we address MAX 4-SAT instances at the computational phase transition and fully connected spin-glass problems using all 36 available qubits. Across all considered cases, our method consistently achieves optimal solutions, highlighting the powerful synergy between non-variational quantum optimization approaches and the intrinsic all-to-all connectivity of trapped-ion architectures. Given the expected scalability of trapped-ion quantum systems, BF-DCQO represents a promising pathway toward practical quantum advantage for dense HUBO problems with significant industrial and scientific relevance. less

Recovering both amplitude and phase information from a system is a fundamental goal of optical imaging. At the same time, it is crucial to use a low photon dose to avoid altering the system, particularly when dealing with biological samples. Quantum imaging offers a powerful approach for extracting more information per photon than classical techniques, which are ultimately limited by shot-noise. However, the trade-off between quantum noise reduction and spatial resolution has been considered a major drawback to the application of quantum techniques to small cellular and sub-cellular structures, where they could offer the most significant benefits. In this work, we overcome this limitation by demonstrating a resolution-independent quantum advantage. We achieve high-resolution phase imaging limited only by the numerical aperture, while simultaneously attaining quantum noise reduction. This enables, for the first time, sub-shot-noise quantitative phase imaging of biological cells. Unlike other quantum imaging approaches, our method operates in a quasi-single-shot wide-field mode, retrieves both phase and amplitude information, and does not rely on interferometric measurements, making it intrinsically fast and stable. These results pave the way for the immediate application of sub-shot-noise imaging in biology. less

We construct a new rotating solution of Einstein's theory in vacuum by exploiting the Lie point symmetries of the field equations in the complex potential formalism of Ernst. In particular, we perform a discrete symmetry transformation, known as inversion, of the gravitational potential associated with the Kerr metric. The resulting metric describes a rotating generalization of the Schwarzschild--Levi-Civita spacetime, and we refer to it as the Kerr--Levi-Civita metric. We study the key geometric features of this novel spacetime, which turns out to be free of curvature singularities, topological defects, and closed timelike curves. These attractive properties are also common to the extremal black hole and the super-spinning case. The solution is algebraically general (Petrov-type I), and its horizon structure is exactly that of the Kerr spacetime. The ergoregions, however, are strongly influenced by the Levi-Civita-like asymptotic structure, producing an effect akin to the magnetized Kerr--Newman and swirling solutions. Interestingly, while its static counterpart permits a Kerr--Schild representation, the Kerr--Levi-Civita metric does not admit such a formulation. less

We compute the quasi-normal mode (QNM) frequencies of slowly rotating black holes in dynamical Chern-Simons (dCS) gravity, including corrections up to second order in the black hole's dimensionless spin parameter ($\chi$) and second order in the dCS coupling parameter ($\alpha$). Due to the complexities of constructing a Newman-Penrose tetrad at this order, we employ a metric perturbation approach. We derive a system of coupled ordinary differential equations for the primary axial $l$-mode and the polar $l\pm 1$ modes, which is then solved numerically with appropriate ingoing and outgoing wave boundary conditions. Our numerical framework is validated in the General Relativistic limit against known Schwarzschild QNMs and highly accurate Kerr QNM results for $\chi \leq 0.15$. For the fundamental $n=0, l=m=2$ axial mode, we present detailed numerical results illustrating the dependence of QNM frequencies on both $\chi$ and $\alpha$. We observe that while rotation generally increases the damping time, increasing the dCS coupling parameter significantly reduces the damping time of the axial mode. This finding contrasts with previous analytical work on polar modes, which suggested an increase in damping time due to dCS effects, highlighting a crucial parity-dependent difference in how dCS gravity impacts black hole ringdowns. Furthermore, we provide an analytical fitting formula for this mode. These results, incorporating coupled spin and dCS effects at second order, provide more accurate theoretical predictions for testing dCS gravity with gravitational wave observations of black hole ringdowns. The refined QNM calculations are particularly relevant for lower-mass black hole merger events, such as GW230529, where dCS corrections may be more prominent and their distinct damping signatures could be observable. [Abridged Version] less

Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier--Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97\% for single-qubit gates and 99.76\% for two-qubit gates, we successfully reproduce natural vortex interactions. This method bridges classical fluid dynamics and quantum computing, offering a novel computational platform for studying vortex dynamics. Our results demonstrate the potential of quantum computing to tackle longstanding challenges in fluid dynamics and broaden applications across both natural and engineering systems. less

The erasure of information is fundamentally an irreversible logical operation, carrying profound consequences for the energetics of computation and information processing. In this work, we investigate the thermodynamic costs associated with erasing (and preparing) quantum processes. Specifically, we analyze an arbitrary bipartite unitary gate acting on logical and ancillary input-output systems, where the ancillary input is always initialized in the ground state. We focus on the adversarial erasure cost of the reduced dynamics~\textemdash~that is, the minimal thermodynamic work required to erase the logical output of the gate for any logical input, assuming full access to the ancilla but no access to any purifying reference of the logical input state. We determine that this adversarial erasure cost is directly proportional to the negative min-entropy of the reduced dynamics, thereby giving the dynamical min-entropy a clear operational meaning. A key foundation of this result is the quantum process decoupling theorem, which quantitatively relates the decoupling ability of a process with its min-entropy. This insight bridges thermodynamics, information theory, and the fundamental limits of quantum computation. less