Distributed quantum computing (DQC) provides a promising route toward scalable quantum computation, where entanglement-assisted LOCC and circuit knitting represent two complementary approaches. The former deterministically realizes nonlocal operations but demands extensive entanglement resources, whereas the latter requires no entanglement yet suffers from exponential sampling overhead. Here, we propose a hybrid framework called entanglement-assisted circuit knitting that integrates these two paradigms by performing circuit knitting assisted with a limited amount of entanglement. We establish a general theoretical framework for entanglement-assisted circuit knitting. Optimal sampling overhead is achieved for Choi-stretchable unitaries with general entanglement resources, while for general unitaries we derive both lower and upper bounds for one-Bell-pair-assisted circuit knitting. We further extend the framework to the black-box setting, which can be treated as a class of quantum combs. This extension releases the need for explicit knowledge of the global unitary of a whole quantum circuit, enables a more flexible embedding structure, and broadens its applicability. Within this fram
In large-scale reconfigurable intelligent surface (RIS) communication systems, the precise acquisition of channel state information (CSI) is challenging. Consider a practical RIS configuration where only a few reflective elements serve as anchors to estimate CSI, which are termed partial CSI. To improve the robustness against partial CSI and the scalability of RIS networks, this paper proposes an unsupervised learning-based rate-splitting multiple access (RSMA) scheme for RIS-assisted multi-user systems. Specifically, RISnet, a neural network architecture designed to infer full CSI from partial observations, is employed and integrated with a low-complexity RSMA precoder. Effective channel features are constituted from partial CSI, and the original elements with channel information contribute to new anchors after expansion in RISnet. Numerical results demonstrate that the proposed scheme approximates the performance with a full CSI of RIS under deterministic raytracing channel conditions. When channel uncertainty increases during training, RSMA has been shown to enhance RISnet robustness, significantly mitigating performance loss.
Edge caching is an emerging technology that empowers caching units at edge nodes, allowing users to fetch contents of interest that have been pre-cached at the edge nodes. The key to pre-caching is to maximize the cache hit percentage for cached content without compromising users' privacy. In this letter, we propose a federated learning (FL) assisted edge caching scheme based on lightweight architecture denoising diffusion probabilistic model (LDPM). Our simulation results verify that our proposed scheme achieves a higher cache hit percentage compared to existing FL-based methods and baseline methods.
Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this paper, we propose a personalized federated distillation assisted vehicle edge caching strategy. The simulation results demonstrate that the proposed vehicle edge caching strategy has good robustness to variations in vehicle speed, significantly reducing communication overhead.
We investigate the issue of assisted coherence distillation in the asymptotic limit (considering infinite copies of the resource states), by coordinately performing the identical local operations on the auxiliary systems of each copy. When we further restrict the coordinate operations to projective measurements, the distillation process is branched into many sub-processes. Finally, a simple formula is given that the assisted distillable coherence should be the maximal average coherence of the residual states. The formula makes the experimental research of assisted coherence distillation possible and convenient, especially for the case that the system and its auxiliary are in mixed states. By using the formula,\ we for the first time study the assisted coherence distillation in multipartite systems. Monogamy-like inequalities are given to constrain the distribution of the assisted distillable coherence in the subsystems. Taking three-qubit system for example, we experimentally prepare two types of tripartite correlated states, i.e., the $W$-type and GHZ-type states in a linear optical setup, and experimentally explore the assisted coherence distillation. Theoretical and experimental
Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less extensive training as they deliver care. To this end, we developed the AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response suggestion feature, which supports providers in delivering protocolized therapies empathetically. We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features. Upon evaluation, the AI-assisted system significantly decreased response times by 29.34% (p=0.002), tripled empathic response accuracy (p=0.0001), and increased goal recommendation accuracy by 66.67% (p=0.001) across both user groups compared to the control. Both groups rated the system as having excellent usability.
Atrial fibrillation (AF) increases the risk of thromboembolic events due to impaired function of the left atrial appendage (LAA). Left atrial appendage closure (LAAC) is a minimally invasive intervention designed to reduce stroke risk by sealing the LAA with an expandable occluder device. Current deployment relies on manual catheter control and imaging modalities like fluoroscopy and transesophageal echocardiography, which carry limitations including radiation exposure and limited positioning precision. In this study, we leverage a previously developed force-sensing delivery sheath integrating fiber Bragg gratings (FBGs) at the interface between the catheter and the occluder. Combined with electromagnetic (EM) tracking, this setup enables real-time measurement of interaction forces and catheter tip position during robot-assisted LAAC deployment in an anatomical phantom. We present a novel force-displacement profiling method that characterizes occluder deployment dynamics and identifies key procedural steps without relying on ionizing radiation. The force profiles reveal low-magnitude interaction forces, suggesting minimal mechanical stress on the surrounding anatomy. This approach
This paper explores a novel research direction where a digital twin is leveraged to assist the beamforming design for an integrated sensing and communication (ISAC) system. In this setup, a base station designs joint communication and sensing beamforming to serve the communication user and detect the sensing target concurrently. Utilizing the electromagnetic (EM) 3D model of the environment and ray tracing, the digital twin can provide various information, e.g., propagation path parameters and wireless channels, to aid communication and sensing systems. More specifically, our digital twin-based beamforming design first leverages the environment EM 3D model and ray tracing to (i) predict the directions of the line-of-sight (LoS) and non-line-of-sight (NLoS) sensing channel paths and (ii) identify the dominant one among these sensing channel paths. Then, to optimize the joint sensing and communication beam, we maximize the sensing signal-to-noise ratio (SNR) on the dominant sensing channel component while satisfying a minimum communication signal-to-interference-plus-noise ratio (SINR) requirement. Simulation results show that the proposed digital twin-assisted beamforming design ach
In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom--built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderl
We presented assisted common information as a generalization of Gács-Körner (GK) common information at ISIT 2010. The motivation for our formulation was to improve upperbounds on the efficiency of protocols for secure two-party sampling (which is a form of secure multi-party computation). Our upperbound was based on a monotonicity property of a rate-region (called the assisted residual information region) associated with the assisted common information formulation. In this note we present further results. We explore the connection of assisted common information with the Gray-Wyner system. We show that the assisted residual information region and the Gray-Wyner region are connected by a simple relationship: the assisted residual information region is the increasing hull of the Gray-Wyner region under an affine map. Several known relationships between GK common information and Gray-Wyner system fall out as consequences of this. Quantities which arise in other source coding contexts acquire new interpretations. In previous work we showed that assisted common information can be used to derive upperbounds on the rate at which a pair of parties can {\em securely sample} correlated random
Generative AI tools can help users with many tasks. One such task is data analysis, which is notoriously challenging for non-expert end-users due to its expertise requirements, and where AI holds much potential, such as finding relevant data sources, proposing analysis strategies, and writing analysis code. To understand how data analysis workflows can be assisted or impaired by generative AI, we conducted a study (n=15) using Bing Chat via participatory prompting. Participatory prompting is a recently developed methodology in which users and researchers reflect together on tasks through co-engagement with generative AI. In this paper we demonstrate the value of the participatory prompting method. We found that generative AI benefits the information foraging and sensemaking loops of data analysis in specific ways, but also introduces its own barriers and challenges, arising from the difficulties of query formulation, specifying context, and verifying results.
We consider conditions necessary for a successful implementation of so-called assisted inflation. We generalize the applicability of assisted inflation beyond exponential potentials as originally proposed to include standard chaotic (λφ^4 or m^2 φ^2) models as well. We also demonstrate that in a purely 4-dimensional theory, unless the assisted sector is in fact decoupled, the additional fields of the assisted sector actually impede inflation. As a specific example of an assisted sector, we consider a 5-dimensional KK model for which the extra dimension may be somewhat or much larger than the inverse Planck scale. In this case, the assisted sector (coming from a KK compactification) eliminates the need for a fine-tuned quartic coupling to drive chaotic inflation. This is a general result of models with one or more "large" extra dimensions.
Bennett et al. showed that allowing shared entanglement between a sender and receiver before communication begins dramatically simplifies the theory of quantum channels, and these results suggest that it would be worthwhile to study other scenarios for entanglement-assisted classical communication. In this vein, the present paper makes several contributions to the theory of entanglement-assisted classical communication. First, we rephrase the Giovannetti-Lloyd-Maccone sequential decoding argument as a more general "packing lemma" and show that it gives an alternate way of achieving the entanglement-assisted classical capacity. Next, we show that a similar sequential decoder can achieve the Hsieh-Devetak-Winter region for entanglement-assisted classical communication over a multiple access channel. Third, we prove the existence of a quantum simultaneous decoder for entanglement-assisted classical communication over a multiple access channel with two senders. This result implies a solution of the quantum simultaneous decoding conjecture for unassisted classical communication over quantum multiple access channels with two senders, but the three-sender case still remains open (Sen rece
Plasma Assisted Combustion (PAC) is a promising technology to enhance the combustion of lean mixtures prone to instabilities and flame blow-off. Although many PAC experiments demonstrated combustion enhancement, several studies report an increase in NOx emissions. The aim of this study is to determine the kinetic pathways leading to NOx formation in the second stage of a sequential combustor assisted by Nanosecond Repetitively Pulsed Discharges (NRPDs). For this purpose, Large Eddy Simulation (LES) associated with an accurate description of the combustion/NOx chemistry and a phenomenological model of the plasma kinetics is used. Detailed kinetics 0-Dimensional reactors complement the study. First, the LES setup is validated by comparison with experiments. Then, the NOx chemistry is analyzed. For the conditions of operation studied, it is shown that the production of atomic nitrogen in the plasma by direct electron impact on nitrogen molecules increases the formation of NO. Then, the NO molecules are transported through the turbulent flame without being strongly affected. This study illustrates the need to limit the diatomic nitrogen dissociation process in order to mitigate harmful
This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperativ
We explore the dynamics of assisted quintessence, where more than one scalar field is present with the same potential. For potentials with tracking solutions, the fields naturally approach the same values; in the context of inflation this leads to the assisted inflation phenomenon where several fields can cooperate to drive a period of inflation though none is able to individually. For exponential potentials, we study the fixed points and their stability confirming results already in the literature, and then carry out a numerical analysis to show how assisted quintessence is realized. For inverse power-law potentials, we find by contrast that there is no assisted behaviour; indeed those are the unique (monotonic) potentials where several fields together behave just as a single field in the same potential. More generally, we provide an algorithm for generating a single-field potential giving equivalent dynamics to multi-field assisted quintessence.
We characterize the operational task of environment-assisted distillation of quantum coherence under different sets of free operations when only a finite supply of copies of a given state is available. We first evaluate the one-shot assisted distillable coherence exactly, and introduce a semidefinite programming bound on it in terms of a smooth entropic quantity. We prove the bound to be tight for all systems in dimensions 2 and 3, which allows us to obtain computable expressions for the one-shot rate of distillation, establish an analytical expression for the best achievable fidelity of assisted distillation for any finite number of copies, and fully solve the problem of asymptotic zero-error assisted distillation for qubit and qutrit systems. Our characterization shows that all relevant sets of free operations in the resource theory of coherence have exactly the same power in the task of one-shot assisted coherence distillation, and furthermore resolves a conjecture regarding the additivity of coherence of assistance in dimension 3.
Coherence and entanglement are fundamental concepts in resource theory. The coherence (entanglement) of assistance is the coherence (entanglement) that can be extracted assisted by another party with local measurement and classical communication. We introduce and study the general coherence of assistance. First, in terms of real symmetric concave functions on the probability simplex, the coherence of assistance and the entanglement of assistance are shown to be in one-to-one correspondence. We then introduce two classes of quantum states: the assisted maximally coherent states and the assisted maximally entangled states. They can be transformed into maximally coherent or entangled pure states with the help of another party using local measurement and classical communication. We give necessary conditions for states to be assisted maximally coherent or assisted maximally entangled. Based on these, a unified framework between coherence and entanglement including coherence (entanglement) measures, coherence (entanglement) of assistance, coherence (entanglement) resources is proposed. Then we show that the coherence of assistance as well as entanglement of assistance are strictly larger
We study the photo-assisted noise generated by time-dependent or random sources and transmission amplitudes. We show that it obeys a perturbative non-equilibrium fluctuation relation that fully extends the lateral-band transmission picture in terms of many-body correlated states. This relation holds in non-equilibrium strongly correlated systems such as the integer or fractional quantum Hall regime as well as in quantum circuits formed by a normal or Josephson junctions strongly coupled to an electromagnetic environment, with a possible temperature bias. We then show that the photo-assisted noise is universally super-poissonian, giving an alternative to a theorem by L. Levitov {\it et al} which states that an ac voltage increases the noise. Restricted to a linear dc current, we show that the latter does not apply to a non-linear superconducting junction. Then we characterize minimal excitations in non-linear conductors by ensuring a poissonian photo-assisted noise, and show that these can carry a non-trivial charge value in the fractional quantum Hall regime. We also propose methods for shot noise spectroscopy and for a robust determination of the fractional charge which is more ad
In this correspondence, we investigate an intelligent reflective surface (IRS) assisted downlink ultra-reliable and low-latency communication (URLLC) system, where an access point (AP) sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access (FDMA) and time division multiple access (TDMA) is conducted for the considered system, from the perspective of average age of information (AoI). Aiming to minimize the maximum average AoI among all devices by jointly optimizing the resource allocation and passive beamforming. However, the formulated problem is difficult to solve due to the non-convex objective function and coupled variables. Thus, we propose an alternating optimization based algorithm by dividing the original problem into two sub-problems which can be efficiently solved. Simulation results show that TDMA can achieve lower AoI by exploiting the time-selective passive beamforming of IRS for maximizing the signal to noise ratio (SNR) of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwid