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By employing ray-tracing techniques, we investigate the shadow images of rotating Bardeen black holes surrounded by perfect fluid dark matter. In this work, two models are considered for the background light source, namely the celestial light source model and the thin accretion disk model. Regarding the celestial light source, the investigation focuses on the impact of variations in relevant parameters and observed inclination on the contour and size of the shadow. For the thin accretion disk model, the optical appearance of a black hole is evidently contingent upon the radiative properties exhibited by the accretion disk, as well as factors such as observed inclination and relevant parameters governing spacetime. With an increasing observation inclination, the observed flux of direct and lensed images of the accretion disk gradually converge towards the lower region of the image, while an increase in the dark matter parameter $a$ significantly expands the region encompassing both direct and lensed images. Furthermore, the predominant effect is redshift at lower observation angles, whereas the blueshift effect only becomes apparent at higher observation angles. Simultaneously, the increase in the observation inclination will amplify the redshift effect, whereas an increase in the magnetic charge $\mathcal{G}$, rotation parameter $a$ and the absolute value of dark matter parameter $\alpha$ will attenuate the redshift effect observed in the image. These observations of a rotating Bardeen black hole surrounded by perfect fluid dark matter could provide a convenient way to distinguish it from other black hole models., Comment: 32 pages, 12 figures

In this paper, we study the shadow and images of the accretion disk of Kerr-Newman (KN) black hole (BH) in modified gravity (MOG) theory by using backward ray-tracing method. And, the influence of spin parameter ($a$), charge ($Q$), and MOG parameter ($\alpha$) on the observed features of BHs are carefully addressed. Interestingly, as $\alpha$ increases, the flat edge of the BH's shadow gradually becomes more rounded, the size of shadow enlarges, and the deviation rate ($\delta s$) correspondingly decreases. By tracing the photon around BH, we observe that the trajectory of photon exhibits distortion behavior, i.e., the formation of two "tails" near the Einstein ring, which elongate as $a$ increases. For the accretion disk, it shows that the inner shadow expands with $\alpha$, while decreases with $Q$. The increase of $\alpha$ exhibits an increasing effect on redshift. At the same parameter level, $\alpha$ has a more obvious effect on inner shadow and image of BH by comparing with that of $Q$. Our study implies that both $\alpha$ and $Q$ have relatively significant effects on the image of the KN-MOG BH with the thin disk accretion, but the influence of $\alpha$ is much greater. So, this indicates that $\alpha$ plays a dominant role in this spacetime., Comment: 24 pages, 9 figures

Nonlinear partial differential equations (PDEs) are crucial for modeling complex fluid dynamics and are foundational to many computational fluid dynamics (CFD) applications. However, solving these nonlinear PDEs is challenging due to the vast computational resources they demand, highlighting the pressing need for more efficient computational methods. Quantum computing offers a promising but technically challenging approach to solving nonlinear PDEs. Recently, Liao proposed a framework that leverages quantum computing to accelerate the solution of nonlinear PDEs based on the homotopy analysis method (HAM), a semi-analytical technique that transforms nonlinear PDEs into a series of linear PDEs. However, the no-cloning theorem in quantum computing poses a major limitation, where directly applying quantum simulation to each HAM step results in exponential complexity growth with the HAM truncation order. This study introduces a "secondary linearization" approach that maps the whole HAM process into a system of linear PDEs, allowing for a one-time solution using established quantum PDE solvers. Our method preserves the exponential speedup of quantum linear PDE solvers while ensuring that computational complexity increases only polynomially with the HAM truncation order. We demonstrate the efficacy of our approach by applying it to the Burgers' equation and the Korteweg-de Vries (KdV) equation. Our approach provides a novel pathway for transforming nonlinear PDEs into linear PDEs, with potential applications to fluid dynamics. This work thus lays the foundation for developing quantum algorithms capable of solving the Navier-Stokes equations, ultimately offering a promising route to accelerate their solutions using quantum computing., Comment: 22 pages, 4 figures

We investigate the perturbation of the scalar field as well as the electromagnetic field over a sort of regular black holes which are characterized by the sub-Planckian curvature and the Minkowskian core. Specifically, we compute the quasinormal modes(QNMs) by employing the pseudo-spectral method. The outburst of overtones is manifestly observed in the QNMs of these regular black holes, which can be attributed to the deviation of the Schwarzschild black hole by quantum effects of gravity. Furthermore, the QNMs under the perturbation of electromagnetic field exhibit smaller real and imaginary parts than those under scalar field perturbation. By comparing the QNMs of the regular black hole featured by Minkowskian core with those of Bardeen black hole featured by de Sitter core, we find they may be an effective tool to distinguish these BHs.

In recent years, quantum computation has been rapidly advancing, driving a technological revolution with significant potential across various sectors, particularly in finance. Despite this, the insurance industry, an essential tool for mitigating unforeseen risks and losses, has received limited attention. This paper provides an initial exploration into the realm of quantum computational insurance and actuarial science. After introducing key insurance models and challenges, we examine quantum algorithms designed to address complex insurance issues. Our study includes experimental and numerical demonstrations of quantum applications in non-life insurance, life insurance, and reinsurance. Additionally, we explore the timeline for quantum insurance, the development of quantum-enhanced insurance products, and the challenges posed by quantum computational advancements., Comment: 10 pages, 5 figures

By considering the celestial light source and the thin disk source, we employ the backward ray-tracing method to carefully study the shadow, inner shadow and observational images of the non-singular rotating black holes in loop quantum gravity. The results show that the increase of quantum parameter $\lambda$ causes the shadow to shrink, while increases the deviation from circularity. And, the shadow's angular diameter of M87* impose stronger constraints on the observed properties of the no-singulgar rotating black holes by comparing with SgrA*. For a celestial light source, the parameter $\lambda$ indeed influences the distortion of light around black hole shadow, but this effect is relatively small and only becomes noticeable when extremely close to the shadow. When a thin accretion disk around black hole, it turns out that for an observer at any position, the parameter $\lambda$ has little effect on the shape of the inner shadow. However, it decreases the size of the inner shadow, reduces the observed light intensity, and narrows the redshifted shadow images, regardless of whether the accretion disk is prograde or retrograde. Meanwhile, it is true that the thin disk images of black hole cannot effectively reflect the internal structure of black hole. Finally, we can conclude that a key observational feature of these non-singular rotating black holes is that the larger the black hole's spin parameter, the smaller the upper limit of $\lambda'$s effect. And, the parameter $\lambda$ decreases the gravitational field's strength, thereby weakens the observed images. This could provide a possible way to constraining black hole parameters, identifying quantum gravity effects, and distinguishing loop quantum gravity black holes, even if it cannot be used to distinguish the non-singular properties of black hole., Comment: 28pages,14figures,made some corrections and updated some references

Large-scale quantum computation requires to be performed in the fault-tolerant manner. One crucial issue of fault-tolerant quantum computing (FTQC) is reducing the overhead of implementing logical gates. Recently proposed correlated decoding and ``algorithmic fault tolerance" achieve fast logical gates that enables universal quantum computation. However, for circuits involving mid-circuit measurements and feedback, this approach is incompatible with window-based decoding, which is a natural requirement for handling large-scale circuits. In this letter, we propose an alternative architecture that employs delayed fixup circuits, integrating window-based correlated decoding with fast transversal gates. This design significantly reduce both the frequency and duration of correlated decoding, while maintaining support for constant-time logical gates and universality across a broad class of quantum codes. More importantly, by spatial parallelism of windows, this architecture well adapts to time-optimal FTQC, making it particularly useful for large-scale computation. Using Shor's algorithm as an example, we explore the application of our architecture and reveals the promising potential of using fast transversal gates to perform large-scale quantum computing tasks with acceptable overhead on physical systems like ion traps., Comment: 11 pages, 9 figures

Algorithm Design (AD) is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. Over the past three years, the integration of LLMs into AD (LLM4AD) has seen substantial progress, with applications spanning optimization, machine learning, mathematical reasoning, and scientific discovery. Given the rapid advancements and expanding scope of this field, a systematic review is both timely and necessary. This paper provides a systematic review of LLM4AD. First, we offer an overview and summary of existing studies. Then, we introduce a taxonomy and review the literature across four dimensions: the roles of LLMs, search methods, prompt methods, and application domains with a discussion of potential and achievements of LLMs in AD. Finally, we identify current challenges and highlight several promising directions for future research.

Strong spin-orbit coupling and relatively weak hyperfine interactions make germanium hole spin qubits a promising candidate for semiconductor quantum processors. The two-dimensional hole gas structure of strained Ge quantum wells serves as the primary material platform for spin hole qubits.A low disorder material environment is essential for this process. In this work, we fabricated a Ge/SiGe heterojunction with a 60 nm buried quantum well layer on a Si substrate using reduced pressure chemical vapor deposition technology. At a temperature of 16 mK, when the carrier density is 1.87*10^11/cm2, we obtained a mobility as high as 308.64*10^4cm2/Vs. Concurrently, double quantum dot and planar germanium coupling with microwave cavities were also successfully achieved.This fully demonstrates that this structure can be used for the preparation of higher-performance hole spin qubits.

Scaling up quantum dots to two-dimensional (2D) arrays is a crucial step for advancing semiconductor quantum computation. However, maintaining excellent tunability of quantum dot parameters, including both nearest-neighbor and next-nearest-neighbor couplings, during 2D scaling is challenging, particularly for silicon quantum dots due to their relatively small size. Here, we present a highly controllable and interconnected 2D quantum dot array in planar silicon, demonstrating independent control over electron fillings and the tunnel couplings of nearest-neighbor dots. More importantly, we also demonstrate the wide tuning of tunnel couplings between next-nearest-neighbor dots,which plays a crucial role in 2D quantum dot arrays. This excellent tunability enables us to alter the coupling configuration of the array as needed. These results open up the possibility of utilizing silicon quantum dot arrays as versatile platforms for quantum computing and quantum simulation.

Electron spin qubits in silicon are a promising platform for fault-tolerant quantum computing. Low-frequency noise, including nuclear spin fluctuations and charge noise, is a primary factor limiting gate fidelities. Suppressing this noise is crucial for high-fidelity qubit operations. Here, we report on a two-qubit quantum device in natural silicon with universal qubit control, designed to investigate the upper limits of gate fidelities in a non-purified Si/SiGe quantum dot device. By employing advanced device structures, qubit manipulation techniques, and optimization methods, we have achieved single-qubit gate fidelities exceeding 99% and a two-qubit Controlled-Z (CZ) gate fidelity of 91%. Decoupled CZ gates are used to prepare Bell states with a fidelity of 91%, typically exceeding previously reported values in natural silicon devices. These results underscore that even natural silicon has the potential to achieve high-fidelity gate operations, particularly with further optimization methods to suppress low-frequency noise.

Parity of particle number is a new degree of freedom for manipulating metasurface, while its influence on controlling non-local metasurfaces remains an unresolved and intriguing question. We propose a metasurface consisting of periodically arranged infinite-long cylinders made from multiple layers of SiO2 and WS2. The cylinder exhibits strong backward scattering due to the overlapping magnetic dipole and electric quadrupole resonances. Without non-local coupling in unit cells, the infinite-size metasurface manifests high reflection across all instances. However, parity-dependent reflectivity diverges with non-local coupling in supercells, exhibiting either increased logarithmic or decreased exponential behavior, with significant distinctions at small particle numbers. Interestingly, equal magnitude reflection and transmission reversals are achievable through alternation between adjacent odd and even particle numbers. The finite-size non-local metasurfaces behave similarly to the infinite-size counterparts, yet high reflection disappears at small particle numbers due to energy leakage. Essentially, high reflection arises from strong backward scattering and effective suppression of lateral multiple scatterings. Our work aids in the actual metasurface design and sheds new light on photonic integrated circuits and on-chip optical communication., Comment: 24 pages, 13 figures

Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control system. To address this, we propose a novel Hierarchical MicroArchitecture (HiMA) designed to facilitate qubit scaling and exploit quantum process-level parallelism. This microarchitecture is based on three core elements: (i) discrete qubit-level drive and readout, (ii) a process-based hierarchical trigger mechanism, and (iii) multiprocessing with a staggered triggering technique to enable efficient quantum process-level parallelism. We implement HiMA as a control system for a 72-qubit tunable superconducting quantum processing unit, serving a public quantum cloud computing platform, which is capable of expanding to 6144 qubits through three-layer cascading. In our benchmarking tests, HiMA achieves up to a 4.89x speedup under a 5-process parallel configuration. Consequently, to the best of our knowledge, we have achieved the highest CLOPS (Circuit Layer Operations Per Second), reaching up to 43,680, across all publicly available platforms.

In the NISQ era, achieving large-scale quantum computing demands compact circuits to mitigate decoherence and gate error accumulation. Quantum operations with diverse degrees of freedom hold promise for circuit compression, but conventional approaches encounter challenges in simultaneously adjusting multiple parameters. Here, we propose a transition composite gate (TCG) scheme grounded on state-selective transition path engineering, enabling more expressive conditional operations. We experimentally validate a controlled unitary (CU) gate as an example, with independent and continuous parameters. By adjusting the parameters of $\rm X^{12}$ gate, we obtain the CU family with a fidelity range of 95.2% to 99.0% leveraging quantum process tomography (QPT). To demonstrate the capability of circuit compression, we use TCG scheme to prepare 3-qubit Greenberger-Horne-Zeilinger (GHZ) and W states, with the fidelity of 96.77% and 95.72%. TCG can achieve the reduction in circuit depth of about 40% and 44% compared with the use of CZ gates only. Moreover, we show that short-path TCG (SPTCG) can further reduce the state-preparation circuit time cost. The TCG scheme exhibits advantages in certain quantum circuits and shows significant potential for large-scale quantum algorithms., Comment: 21 pages, 12 figures

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) represents one of the most promising applications in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we improve the trainability of variational quantum eigensolver (VQE) by utilizing convex interpolation to solve portfolio optimization. The idea is inspired by the observation that the Dicke state possesses an inherent clustering property. Consequently, the energy of a state with a larger Hamming distance from the ground state intuitively results in a greater energy gap away from the ground state energy in the overall distribution trend. Based on convex interpolation, the location of the ground state can be evaluated by learning the property of a small subset of basis states in the Hilbert space. This enlightens naturally the proposals of the strategies of close-to-solution initialization, regular cost function landscape, and recursive ansatz equilibrium partition. The successfully implementation of a $40$-qubit experiment using only $10$ superconducting qubits demonstrates the effectiveness of our proposals. Furthermore, the quantum inspiration has also spurred the development of a prototype greedy algorithm. Extensive numerical simulations indicate that the hybridization of VQE and greedy algorithms achieves a mutual complementarity, combining the advantages of both global and local optimization methods. Our proposals can be extended to improve the trainability for solving other large-scale combinatorial optimization problems that are widely used in real applications, paving the way to unleash quantum advantages of NISQ computers in the near future.

Great progress has been made in quantum computing in recent years, providing opportunities to overcome computation resource poverty in many scientific computations like computational fluid dynamics (CFD). In this work, efforts are made to exploit quantum potentialities in CFD, and a hybrid classical and quantum computing CFD framework is proposed to release the power of current quantum computing. In this framework, the traditional CFD solvers are coupled with quantum linear algebra libraries in weak form to achieve collaborative computation between classical and quantum computing. The quantum linear solver provides high-precision solutions and scalable problem sizes for linear systems and is designed to be easily callable for solving linear algebra systems similar to classical linear libraries, thus enabling seamless integration into existing CFD solvers. Some typical cases are performed to validate the feasibility of the proposed framework and the correctness of quantum linear algorithms in CFD., Comment: 18 pages, 16 figures

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures

Influence maximization problem involves selecting a subset of seed nodes within a social network to maximize information spread under a given diffusion model, so how to identify the important nodes is the problem to be considered in this paper. Due to the great differences in the reality of the network, a class of multi-attribute decision fusion methods is often used to solve this problem. Electre is mostly used to solve the problems of investment order, benefit, and risk assessment of projects in economics, which supports the decision maker to make choices by comparing the differences between a set of alternatives. In this paper, we propose a multi-attribute decision fusion method named SK-E, which construct local and global metrics for different networks, use the improved Electre to make decision fusion between local and global metrics of nodes, to get the optimal weight between local and global metrics, and then identify the important nodes. The proposed method demonstrates superior accuracy compared to other methods, as evaluated through three experiments: the SIR epidemic model, the independent cascade model, and constraint efficiency. These experiments were conducted across six different real networks selected as the experimental dataset., Comment: Due to changes in authorship and substantial updates to the content, this manuscript is no longer valid

Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge., Comment: 19 pages, 5 figures

The rapid development of quantum computers has enabled demonstrations of quantum advantages on various tasks. However, real quantum systems are always dissipative due to their inevitable interaction with the environment, and the resulting non-unitary dynamics make quantum simulation challenging with only unitary quantum gates. In this work, we present an innovative and scalable method to simulate open quantum systems using quantum computers. We define an adjoint density matrix as a counterpart of the true density matrix, which reduces to a mixed-unitary quantum channel and thus can be effectively sampled using quantum computers. This method has several benefits, including no need for auxiliary qubits and noteworthy scalability. Moreover, accurate long-time simulation can also be achieved as the adjoint density matrix and the true dissipated one converge to the same state. Finally, we present deployments of this theory in the dissipative quantum $XY$ model for the evolution of correlation and entropy with short-time dynamics and the disordered Heisenberg model for many-body localization with long-time dynamics. This work promotes the study of real-world many-body dynamics with quantum computers, highlighting the potential to demonstrate practical quantum advantages., Comment: 13 pages, 6 figures

Fault-tolerant quantum computing (FTQC) is essential for achieving large-scale practical quantum computation. Implementing arbitrary FTQC requires the execution of a universal gate set on logical qubits, which is highly challenging. Particularly, in the superconducting system, two-qubit gates on surface code logical qubits have not been realized. Here, we experimentally implement logical CNOT gate as well as arbitrary single-qubit rotation gates on distance-2 surface codes using the superconducting quantum processor \textit{Wukong}, thereby demonstrating a universal logical gate set. In the experiment, we design encoding circuits to prepare the required logical states, where the fidelities of the fault-tolerantly prepared logical states surpass those of the physical states. Furthermore, we demonstrate the transversal CNOT gate between two logical qubits and fault-tolerantly prepare four logical Bell states, all with fidelities exceeding those of the Bell states on the physical qubits. Using the logical CNOT gate and an ancilla logical state, arbitrary single-qubit rotation gate is implemented through gate teleportation. All logical gates are characterized on a complete state set and their fidelities are evaluated by logical Pauli transfer matrices. Implementation of the universal logical gate set and entangled logical states beyond physical fidelity marks a significant step towards FTQC on superconducting quantum processors., Comment: 15 pages, 12 figures

To realize large-scale quantum information processes, an ideal scheme for two-qubit operations should enable diverse operations with given hardware and physical interaction. However, for spin qubits in semiconductor quantum dots, the common two-qubit operations, including CPhase gates, SWAP gates, and CROT gates, are realized with distinct parameter regions and control waveforms, posing challenges for their simultaneous implementation. Here, taking advantage of the inherent Heisenberg interaction between spin qubits, we propose and verify a fast composite two-qubit gate scheme to extend the available two-qubit gate types as well as reduce the requirements for device properties. Apart from the formerly proposed CPhase (controlled-phase) gates and SWAP gates, theoretical results indicate that the iSWAP-family gate and Fermionic simulation (fSim) gate set are additionally available for spin qubits. Meanwhile, our gate scheme limits the parameter requirements of all essential two-qubit gates to a common J~{\Delta}E_Z region, facilitate the simultaneous realization of them. Furthermore, we present the preliminary experimental demonstration of the composite gate scheme, observing excellent match between the measured and simulated results. With this versatile composite gate scheme, broad-spectrum two-qubit operations allow us to efficiently utilize the hardware and the underlying physics resources, helping accelerate and broaden the scope of the upcoming noise intermediate-scale quantum (NISQ) computing., Comment: 23 pages, 6 figures

High-contrast refractive indexes are pivotal in dielectric metasurfaces for inducing various exotic phenomena, such as the bound state in the continuum (BIC) and electric mirror (EM). However, the limitations of high-index materials are adverse to the practical applications, thus low-contrast metasurfaces with comparable performance are highly desired. Here we present a low-contrast dielectric metasurface comprising radially anisotropic cylinders, which are SiO2 cylinders doped with a small amount of WS2. The cylinder exhibits unidirectional forward superscattering originating from the overlapping of the electric and magnetic dipole resonances. When normal illumination by a near-infrared plane wave, the metasurface consisting of the superscattering constituents manifests a polarization-insensitive EM. Conversely, when subjected to an in-plane incoming wave, the metasurface generates a symmetry-protected BIC characterized by an ultrahigh Q factor and nearly negligible out-of-plane energy radiation. Our work highlights the doping approach as an efficient strategy for designing low-contrast functional metasurfaces and sheds new light on the potential applications in photonic integrated circuits and on-chip optical communication.

In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small part of the PS and requires the solutions in this set share some similarities. This paper makes a first attempt to address this issue. We first develop a performance metric that considers both optimality and variable sharing. Then we design an algorithm for finding the model that minimizes the metric to meet the user's requirements. Experimental results illustrate that we can obtain a linear model that approximates the mapping from the preference vectors to solutions in a local area well., Comment: Preprint of EMO 2023 conference paper

The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint. This metric allows for an in-depth comparison of defense mechanisms and recognizes the trivial improvements in robustness afforded by less potent defensive strategies. Additionally, we adopt a novel training algorithm that enhances adversarial robustness uniformly across various perturbation intensities, in contrast to methods narrowly focused on optimizing adversarial accuracy. Our extensive empirical studies validate the effectiveness of the adversarial hypervolume metric, demonstrating its ability to reveal subtle differences in robustness that adversarial accuracy overlooks. This research contributes a new measure of robustness and establishes a standard for assessing and benchmarking the resilience of current and future defensive models against adversarial threats.

Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve portfolio optimization problems in finance by designing two hardware-efficient Dicke state ansatze that reach a maximum of 2n two-qubit gate depth and n^2/4 parameters, with n being the number of qubits used. Both ansatze are partitioning-friendly, allowing for the proposal of a highly scalable quantum/classical hybrid distributed computing (HDC) scheme. Combining simultaneous sampling, problem-specific measurement error mitigation, and fragment reuse techniques, we successfully implement the HDC experiments on the superconducting quantum computer Wu Kong with up to 55 qubits. The simulation and experimental results illustrate that the restricted expressibility of the ansatze, induced by the small number of parameters and limited entanglement, is advantageous for solving classical optimization problems with the cost function of the conditional value-at-risk (CVaR) for the NISQ era and beyond. Furthermore, the HDC scheme shows great potential for achieving quantum advantage in the NISQ era. We hope that the heuristic idea presented in this paper can motivate fruitful investigations in current and future quantum computing paradigms., Comment: 21 pages, 20 figures

The field of quantum deep learning presents significant opportunities for advancing computational capabilities, yet it faces a major obstacle in the form of the "information loss problem" due to the inherent limitations of the necessary quantum tomography in scaling quantum deep neural networks. This paper introduces an end-to-end Quantum Vision Transformer (QViT), which incorporates an innovative quantum residual connection technique, to overcome these challenges and therefore optimize quantum computing processes in deep learning. Our thorough complexity analysis of the QViT reveals a theoretically exponential and empirically polynomial speedup, showcasing the model's efficiency and potential in quantum computing applications. We conducted extensive numerical tests on modern, large-scale transformers and datasets, establishing the QViT as a pioneering advancement in applying quantum deep neural networks in practical scenarios. Our work provides a comprehensive quantum deep learning paradigm, which not only demonstrates the versatility of current quantum linear algebra algorithms but also promises to enhance future research and development in quantum deep learning., Comment: 24pages, 10 figures

Equivalence between Positive Partial Transpose (PPT) entanglement and bound entanglement is a long-standing open problem in quantum information theory. So far limited progress has been made, even on the seemingly simple case of Werner states bound entanglement. The primary challenge is to give a concise mathematical representation of undistillability. To this end, we propose a decomposition of the N-undistillability verification into $log(N)$ repeated steps of 1-undistillability verification. For Werner state N-undistillability verification, a bound for N-undistillability is given, which is independent of the dimensionality of Werner states. Equivalent forms of inequalities for both rank one and two matrices are presented, before transforming the two-undistillability case into a matrix analysis problem. A new perspective is also attempted by seeing it as a non-convex multi-variable function, proving its critical points and conjecturing Hessian positivity, which would make them local minimums., Comment: 12 pages

In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require feedback on the decision of a model rather than detailed probabilities or scores, are particularly insidious and difficult to defend against. This work introduces L-AutoDA (Large Language Model-based Automated Decision-based Adversarial Attacks), a novel approach leveraging the generative capabilities of Large Language Models (LLMs) to automate the design of these attacks. By iteratively interacting with LLMs in an evolutionary framework, L-AutoDA automatically designs competitive attack algorithms efficiently without much human effort. We demonstrate the efficacy of L-AutoDA on CIFAR-10 dataset, showing significant improvements over baseline methods in both success rate and computational efficiency. Our findings underscore the potential of language models as tools for adversarial attack generation and highlight new avenues for the development of robust AI systems., Comment: Camera ready version for GECCO'24 workshop

Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters. Traditional defense mechanisms, such as adversarial training, gradient masking, and input transformations, either impose substantial computational costs or compromise the test accuracy of non-adversarial inputs. To address these challenges, we propose an efficient defense mechanism, PuriDefense, that employs random patch-wise purifications with an ensemble of lightweight purification models at a low level of inference cost. These models leverage the local implicit function and rebuild the natural image manifold. Our theoretical analysis suggests that this approach slows down the convergence of query-based attacks by incorporating randomness into purifications. Extensive experiments on CIFAR-10 and ImageNet validate the effectiveness of our proposed purifier-based defense mechanism, demonstrating significant improvements in robustness against query-based attacks.

Vision Transformer (ViT) has shown high potential in video recognition, owing to its flexible design, adaptable self-attention mechanisms, and the efficacy of masked pre-training. Yet, it remains unclear how to adapt these pre-trained short-term ViTs for temporal action detection (TAD) in untrimmed videos. The existing works treat them as off-the-shelf feature extractors for each short-trimmed snippet without capturing the fine-grained relation among different snippets in a broader temporal context. To mitigate this issue, this paper focuses on designing a new mechanism for adapting these pre-trained ViT models as a unified long-form video transformer to fully unleash its modeling power in capturing inter-snippet relation, while still keeping low computation overhead and memory consumption for efficient TAD. To this end, we design effective cross-snippet propagation modules to gradually exchange short-term video information among different snippets from two levels. For inner-backbone information propagation, we introduce a cross-snippet propagation strategy to enable multi-snippet temporal feature interaction inside the backbone.For post-backbone information propagation, we propose temporal transformer layers for further clip-level modeling. With the plain ViT-B pre-trained with VideoMAE, our end-to-end temporal action detector (ViT-TAD) yields a very competitive performance to previous temporal action detectors, riching up to 69.5 average mAP on THUMOS14, 37.40 average mAP on ActivityNet-1.3 and 17.20 average mAP on FineAction., Comment: Accepted by CVPR2024