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Quantum networks are advancing the information technology infrastructure of society. Simulation and emulation software tools have emerged to support the design, development, and deployment of quantum networks, however, classical simulation and emulation methods have major bottlenecks in the error, latency, and cost that they can achieve at scale. In this work, we review quantum network simulation and emulation tools, including foundational principles, state-of-the-art tools, and bottlenecks. We then discuss how quantum technologies can address these challenges, and we construct a roadmap for the adoption of quantum simulation and emulation tools, emphasizing codesign with quantum network testbeds.

Adversary emulation is an essential procedure for cybersecurity assessments such as evaluating an organization's security posture or facilitating structured training and research in dedicated environments. To allow for systematic and time-efficient assessments, several approaches from academia and industry have worked towards the automation of adversarial actions. However, they exhibit significant limitations regarding autonomy, tactics coverage, and real-world applicability. Consequently, adversary emulation remains a predominantly manual task requiring substantial human effort and security expertise - even amidst the rise of Large Language Models. In this paper, we present Bounty Hunter, an automated adversary emulation method, designed and implemented as an open-source plugin for the popular adversary emulation platform Caldera, that enables autonomous emulation of adversaries with multi-faceted behavior while providing a wide coverage of tactics. To this end, it realizes diverse adversarial behavior, such as different levels of detectability and varying attack paths across repeated emulations. By autonomously compromising a simulated enterprise network, Bounty Hunter showcases

Increasing system-on-chip (SoC) heterogeneity, deep hardware/software integration, and the proliferation of third-party intellectual property (IP) have brought security validation to the forefront of semiconductor design. While simulation and formal verification remain indispensable, they often struggle to expose vulnerabilities that emerge only under realistic execution conditions, long software-driven interactions, and adversarial stimuli. In this context, hardware emulation is emerging as an increasingly important pre-silicon verification technology because it enables higher-throughput execution of RTL designs under realistic hardware/software workloads while preserving sufficient fidelity for security-oriented analysis. This paper presents a comprehensive survey and perspective on emulation-based security verification and validation. We organize the landscape of prior work across assertion-based security checking, coverage-driven exploration, adversarial testing, information-flow tracking, fault injection, and side-channel-oriented evaluation. We provide a structured view of emulation-enabled security verification workflows, including instrumentation, stimulus generation, runti

Recent architectures integrate high-performance and power-efficient matrix engines. These engines demonstrate remarkable performance in low-precision matrix multiplication, which is crucial in deep learning. Several techniques have been proposed to emulate single- and double-precision general matrix-matrix multiplication (SGEMM and DGEMM, respectively) by leveraging such low-precision matrix engines. In this study, we present emulation methods that significantly outperforms conventional approaches. On a GH200 Grace Hopper Superchip, the proposed DGEMM emulation achieves a 1.4x speedup and a 43% improvement in power efficiency compared to native DGEMM for sufficiently large problems. The proposed SGEMM emulation achieves a 3.0x speedup and a 154% improvement in power efficiency compared to native SGEMM for sufficiently large problems. Furthermore, compared to conventional emulation methods, the proposed emulation achieves more than 2x higher performance and superior power efficiency.

The State of Brain Emulation Report 2025 provides a comprehensive reassessment of the field's progress since Sandberg and Bostrom's 2008 Whole Brain Emulation roadmap. The report is organized around three core capabilities required for brain emulation: recording brain function (Neural Dynamics), mapping brain structure (Connectomics), and emulation and embodiment (Computational Neuroscience). It also identifies ongoing challenges and outlines strategic priorities to help the field move forward.

Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.

Numerical models are widely used to simulate the earth system, but they are computationally expensive and often depend on many uncertain input parameters. Their effective use requires calibration and uncertainty quantification, which typically involve running the model across many input configurations and therefore incur substantial computational cost. Statistical emulation provides a practical alternative for efficiently exploring model behavior. We are motivated by the Arctic sea ice component of the Energy Exascale Earth System Model (MPAS-Seaice), which generates large spatiotemporal outputs at multiple spatial resolutions, with high-resolution (or high-fidelity, HF) simulations being more accurate but computationally more expensive than lower-resolution (low-fidelity, LF) simulations. Multi-fidelity (MF) emulation integrates information across resolutions to construct efficient and accurate surrogate models, yet existing approaches struggle to scale to large spatiotemporal data. We develop an MF emulator that combines tensor decomposition for dimensionality reduction, Gaussian process priors for flexible function approximation, and an additive discrepancy model to capture syst

As more applications utilize virtualization and emulation to run mission-critical tasks, the performance requirements of emulated and virtualized platforms continue to rise. Hardware virtualization is not universally available for all systems, and is incapable of emulating CPU architectures, requiring software emulation to be used. QEMU, the premier cross-architecture emulator for Linux and some BSD systems, currently uses dynamic binary translation (DBT) through intermediate representations using its Tiny Code Generator (TCG) model. While using intermediate representations of translated code allows QEMU to quickly add new host and guest architectures, it creates additional steps in the emulation pipeline which decrease performance. We construct a proof of concept emulator to demonstrate the slowdown caused by the usage of intermediate representations in TCG; this emulator performed up to 35x faster than QEMU with TCG, indicating substantial room for improvement in QEMU's design. We propose an expansion of QEMU's two-tier engine system (Linux KVM versus TCG) to include a middle tier using direct binary translation for commonly paired architectures such as RISC-V, x86, and ARM. This

This research focuses on timestamping methods for profiling network traffic in software-based environments. Accurate timestamping is crucial for evaluating network performance, particularly in Time-Sensitive Networking (TSN). We explore and compare four timestamping techniques within a TSN emulation context, though its findings extend to other network scenarios. The study leverages the Mininet emulator to model TSN networks, defining hosts, bridges, links, and traffic streams. It characterizes bridge latencies and jitter, solves the TSN scheduling problem based on measured parameters, and evaluates the correctness of a deployed schedule for a use case. Key contributions include a methodology for software-based timestamping, solutions for TSN emulation challenges in Linux and Mininet, and experimental insights for optimizing TSN emulation platforms on various system configurations, with and without Intel TCC, either on a high-end workstation or on an industrial PC.

We study in this paper two classes of experimental designs, support points and projected support points, which can provide robust and effective emulation of computer experiments with Gaussian processes. These designs have two important properties that are appealing for surrogate modeling of computer experiments. First, the proposed designs are robust: they enjoy good emulation performance over a wide class of smooth and rugged response surfaces. Second, they can be efficiently generated for large designs in high dimensions using difference-of-convex programming. In this work, we present a theoretical framework that investigates the above properties, then demonstrate their effectiveness for Gaussian process emulation in a suite of numerical experiments.

We study the optimal rates of emulation (also called interconversion) between quantum channels. When the source and the target channels are idempotent, we give a single-letter expression for the zero-error emulation capacity in terms of structural properties of the range of the two channels. This expression shows that channel emulation is not reversible for general idempotent channels. Furthermore, we establish a strong converse rate that matches with the zero-error emulation capacity when the source or the target channel is either an identity or a completely dephasing channel.

Dynamic simulators are computational models governed by differential equations that evolve over time. They are essential for scientific and engineering applications but remain challenging to emulate because of the unpredictable behavior of complex systems. To address this challenge, this paper introduces a fast and accurate Gaussian Process (GP)-based emulation method for complex dynamic simulators. By integrating linked GPs into the one-step-ahead emulation framework, the proposed algorithm provides exact and tractable computation of the posterior mean and variance, solving a problem previously considered computationally intractable and eliminating the need for expensive Monte Carlo approximations. This approach substantially reduces computation time while maintaining or improving predictive accuracy. Furthermore, the method naturally extends to systems with forcing inputs by incorporating them as additional variables within the GP framework. Numerical experiments on multiple dynamic systems demonstrate the efficiency and computational advantages of the proposed approach. An R package, dynemu, which implements the one-step-ahead emulation approach, is available on CRAN.

In-memory computing technology is used extensively in artificial intelligence devices due to lower power consumption and fast calculation of matrix-based functions. The development of such a device and its integration in a system takes a significant amount of time and requires the use of a real-time emulation environment, where various system aspects are analyzed, microcode is tested, and applications are deployed, even before the real chip is available. In this work, we present the architecture, the software development tools, and experimental results of a distributed and expandable emulation system for rapid prototyping of integrated circuits based on in-memory computing technologies. Presented experimental results demonstrate the usefulness of the proposed emulator.

Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we propose a modal emulation-based two-pass multi-modal crowd-counting framework that enables efficient modal emulation, alignment, and fusion. The framework consists of two key components: a \emph{multi-modal inference} pass and a \emph{cross-modal emulation} pass. The former utilizes a hybrid cross-modal attention module to extract global and local information and achieve efficient multi-modal fusion. The latter uses attention prompting to coordinate different modalities and enhance multi-modal alignment. We also introduce a modality alignment module that uses an efficient modal consistency loss to align the outputs of the two passes and bridge the semantic gap between modalities. Extensive experiments on both RGB-Thermal and RGB-Depth counting datasets demonstrate its superior performance compared to previous methods. Code available at https://github.com/Mr-Monday/Multi-modal-Crowd-Counting-via-Modal-Emulation.

TheaterQ is a Linux qdisc designed for dynamic network emulation, addressing the limitations of static parameters in traditional tools like NetEm. By utilizing Trace Files containing timelines with network characteristics, TheaterQ achieves high-accuracy emulation of dynamic networks without involving the userspace and allows for resolutions of characteristic updates of up to 1 microsecond. Features include synchronization across mutliple qdisc instances and handling of delays, bandwidth, packet loss, duplication, and reordering. Evaluations show TheaterQ's accuracy and its comparable performance to existing tools, offering a flexible solution for modern communication protocol development. TheaterQ is available as open-source software under the GPLv2 license.

Conflict-free replicated data types (CRDTs) are distributed data structures designed for fault tolerance and high availability. CRDTs have historically been taxonomized into state-based CRDTs, in which replicas apply updates locally and periodically broadcast their state to other replicas over the network, and operation-based (or op-based) CRDTs, in which every state-updating operation is individually broadcast. In the literature, state-based and op-based CRDTs are considered equivalent due to the existence of algorithms that let them emulate each other, and verification techniques and results that apply to one kind of CRDT are said to apply to the other thanks to this equivalence. However, what it means for state-based and op-based CRDTs to emulate each other has never been made fully precise. Emulation is nontrivial since state-based and op-based CRDTs place different requirements on the underlying network with regard to both the causal ordering of message delivery, and the granularity of the messages themselves. We specify and formalize CRDT emulation in terms of simulation by modeling CRDTs and their interactions with the network as transition systems. We show that emulation ca

Observational studies provide the only evidence on the effectiveness of interventions when randomized controlled trials (RCTs) are impractical due to cost, ethical concerns, or time constraints. While many methodologies aim to draw causal inferences from observational data, there is a growing trend to model observational study designs after RCTs, a strategy known as "target trial emulation." Despite its potential, causal inference through target trial emulation cannot fully address the confounding bias in real-world data due to the lack of randomization. In this work, we present a novel framework for target trial emulation that aims to overcome several key limitations, including confounding bias. The framework proceeds as follows: First, we apply the eligibility criteria of a specific trial to an observational cohort. We then "correct" this cohort by extracting a subset that matches both the distribution of covariates and the baseline prognosis of the control group in the target RCT. Next, we address unmeasured confounding by adjusting the prognosis estimates of the treated group to align with those observed in the trial. Following trial emulation, we go a step further by leveragin

Advanced Persistent Threats (APTs) represent the most threatening form of attack nowadays since they can stay undetected for a long time. Adversary emulation is a proactive approach for preparing against these attacks. However, adversary emulation tools lack the anti-detection abilities of APTs. We introduce Laccolith, a hypervisor-based solution for adversary emulation with anti-detection to fill this gap. We also present an experimental study to compare Laccolith with MITRE CALDERA, a state-of-the-art solution for adversary emulation, against five popular anti-virus products. We found that CALDERA cannot evade detection, limiting the realism of emulated attacks, even when combined with a state-of-the-art anti-detection framework. Our experiments show that Laccolith can hide its activities from all the tested anti-virus products, thus making it suitable for realistic emulations.

Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum of the biased tracer in real space. We employ the Kun simulation suite to construct the emulator, emulating across the space of 8 cosmological parameters including dynamic dark energy $w_0$, $w_a$, and total neutrino mass $\sum m_ν$. The sample variance due to the finite simulation box is further reduced using the Zel'dovich variance control, and it enables the precise measurement of the Lagrangian basis spectra up to the quadratic order. The emulation of basis spectra realizes 1% level accuracy, covering wavelength $ k \leq 1 \,{\rm Mpc}^{-1}h$ and redshift $0\leq z\leq 3$ up to the quadratic order field. To validate the emulator, we perform a joint fit to the halo auto power spectrum and th