Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.
We investigate the feasibility of extracting infinite volume scattering phase shift on quantum computers in a simple one-dimensional quantum mechanical model, using the formalism established in Ref.~\cite{Guo:2023ecc} that relates the integrated correlation functions (ICF) for a trapped system to the infinite volume scattering phase shifts through a weighted integral. The system is first discretized in a finite box with periodic boundary conditions, and the formalism in real time is verified by employing a contact interaction potential with exact solutions. Quantum circuits are then designed and constructed to implement the formalism on current quantum computing architectures. To overcome the fast oscillatory behavior of the integrated correlation functions in real-time simulation, different methods of post-data analysis are proposed and discussed. Test results on IBM hardware show that good agreement can be achieved with two qubits, but complete failure ensues with three qubits due to two-qubit gate operation errors and thermal relaxation errors.
The paper presents a variational quantum algorithm to solve initial-boundary value problems described by second-order partial differential equations. The approach uses hybrid classical/quantum hardware that is well suited for quantum computers of the current noisy intermediate-scale quantum era. The partial differential equation is initially translated into an optimal control problem with a modular control-to-state operator (ansatz). The objective function and its derivatives required by the optimizer can efficiently be evaluated on a quantum computer by measuring an ancilla qubit, while the optimization procedure employs classical hardware. The focal aspect of the study is the treatment of boundary conditions, which is tailored to the properties of the quantum hardware using a correction technique. For this purpose, the boundary conditions and the discretized terms of the partial differential equation are decomposed into a sequence of unitary operations and subsequently compiled into quantum gates. The accuracy and gate complexity of the approach are assessed for second-order partial differential equations by classically emulating the quantum hardware. The examples include steady
Random numbers form an intrinsic part of modern day computing with applications in a wide variety of fields. But due to their limitations, the use of pseudo random number generators (PRNGs) is certainly not desirable for sensitive applications. Quantum systems due to their intrinsic randomness form a suitable candidate for generation of true random numbers that can also be certified. In this work, the violation of CHSH inequality has been used to propose a scheme by which one can generate device independent quantum random numbers by use of IBM quantum computers that are available on the cloud. The generated random numbers have been tested for their source of origin through experiments based on the testing of CHSH inequality through available IBM quantum computers. The performance of each quantum computer against the CHSH test has been plotted and characterized. Further, efforts have been made to close as many loopholes as possible to produce device independent quantum random number generators. This study will provide new directions for the development of self-testing and semi-self-testing random number generators using quantum computers.
Air-gapped systems are disconnected from the Internet and other networks because they contain or process sensitive data. However, it is known that attackers can use computer speakers to leak data via sound to circumvent the air-gap defense. To cope with this threat, when highly sensitive data is involved, the prohibition of loudspeakers or audio hardware might be enforced. This measure is known as an `audio gap'. In this paper, we present PIXHELL, a new type of covert channel attack allowing hackers to leak information via noise generated by the pixels on the screen. No audio hardware or loudspeakers is required. Malware in the air-gap and audio-gap computers generates crafted pixel patterns that produce noise in the frequency range of 0 - 22 kHz. The malicious code exploits the sound generated by coils and capacitors to control the frequencies emanating from the screen. Acoustic signals can encode and transmit sensitive information. We present the adversarial attack model, cover related work, and provide technical background. We discuss bitmap generation and correlated acoustic signals and provide implementation details on the modulation and demodulation process. We evaluated the
Quantum computing is a new emerging computer technology. Current quantum computing devices are at a development stage where they are gradually becoming suitable for small real-world applications. This lecture is devoted to the practical aspects of programming such quantum computing devices. The first part of these lecture notes focuses on programming gate-based quantum computers, and the second part shows how to program quantum annealers.
Boson sampling is considered as a strong candidate to demonstrate the quantum computational supremacy over classical computers. However, previous proof-of-principle experiments suffered from small photon number and low sampling rates owing to the inefficiencies of the single-photon sources and multi-port optical interferometers. Here, we develop two central components for high-performance boson sampling: robust multi-photon interferometers with 0.99 transmission rate, and actively demultiplexed single-photon sources from a quantum-dot-micropillar with simultaneously high efficiency, purity and indistinguishability. We implement and validate 3-, 4-, and 5-photon boson sampling, and achieve sampling rates of 4.96 kHz, 151 Hz, and 4 Hz, respectively, which are over 24,000 times faster than the previous experiments, and over 220 times faster than obtaining one sample through calculating the matrices permanent using the first electronic computer (ENIAC) and transistorized computer (TRADIC) in the human history. Our architecture is feasible to be scaled up to larger number of photons and with higher rate to race against classical computers, and might provide experimental evidence against
"Natural Language," whether spoken and attended to by humans, or processed and generated by computers, requires networked structures that reflect creative processes in semantic, syntactic, phonetic, linguistic, social, emotional, and cultural modules. Being able to produce novel and useful behavior following repeated practice gets to the root of both artificial intelligence and human language. This paper investigates the modalities involved in language-like applications that computers -- and programmers -- engage with, and aims to fine tune the questions we ask to better account for context, self-awareness, and embodiment.
Trust is the core building block of secure systems, and it is enforced through methods to ensure that a specific system is properly configured and works as expected. In this context, a Root of Trust (RoT) establishes a trusted environment, where both data and code are authenticated via a digital signature based on asymmetric cryptography, which is vulnerable to the threat posed by Quantum Computers (QCs). Firmware, being the first layer of trusted software, faces unique risks due to its longevity and difficult update. The transition of firmware protection to Post-Quantum Cryptography (PQC) is urgent, since it reduces the risk derived from exposing all computing and network devices to quantum-based attacks. This paper offers an analysis of the most common trust techniques and their roadmap towards a Post-Quantum (PQ) world, by investigating the current status of PQC and the challenges posed by such algorithms in existing Trusted Computing (TC) solutions from an integration perspective. Furthermore, this paper proposes an architecture for TC techniques enhanced with PEC, addressing the imperative for immediate adoption of quantum-resistant algorithms.
Many studies have found active learning, either in the form of in-class exercises or projects, to be superior to traditional lectures. However, these forms of hands-on learning do not always lead students to reach the higher order thinking skills associated with the highest levels of Bloom's Taxonomy (analysis, synthesis, and evaluation). Assignments that expect students to follow a prescribed approach to reach a well-defined solution contribute to a lack of higher order thinking at the college level. Professional engineers often face complex and ambiguous problems that require design decisions for which there is no straightforward answer. To strengthen the higher order thinking skills demanded by such problems, we developed a project in a semester-long mechatronics course in which students must evaluate two automatic control methodologies without being given explicit performance criteria or experimental procedures. Specifically, the project involves determining the superior control method for leader-follower behavior, where a ground vehicle autonomously follows a lead vehicle. Laboratory exercises throughout the semester expose students to the skills required for the project, incl
Space-filling experimental designs are widely used in engineering computer experiments, where only a limited number of expensive model evaluations can be afforded. Distance-based designs such as Maximin or Minimax ensure global space-filling, while Latin hypercube sampling enforces uniform one-dimensional projections, yet neither guarantees uniformity in lowdimensional subspaces. Maximum Projection (MaxPro) designs were introduced to improve uniformity in low-dimensional subspaces, yet their original formulation relies on the Euclidean distance and may induce systematic density distortions in bounded domains. We demonstrate that the standard MaxPro criterion leads to statistically non-uniform sampling, resulting in undersampling of corner regions and biased Monte Carlo estimates. To remedy this issue, we introduce a periodic variant of the criterion, termed Uniform Maximum Projection (uMaxPro), in which the Euclidean metric is replaced by a periodic distance based on the minimum image convention. The proposed uMaxPro designs preserve the projection-aware structure of MaxPro while achieving statistical uniformity of the design-generation mechanism. Numerical experiments show unbiase
The evaluation of elastodynamic Green's functions across numerous source-receiver locations, frequencies, and material properties, particularly in the context of parametric studies or boundary element computations, is computationally demanding and memory intensive. This paper presents a reduced order modeling strategy based on the Greedy Tucker Approximation (GTA), which incrementally constructs a low-rank representation of the Green's tensor through rank-one enrichments obtained via a Proper Generalized Decomposition (PGD)-type alternating least squares procedure. A Petrov-Galerkin formulation is employed to improve convergence and approximation accuracy. The resulting multi-dimensional tensor, expressed in terms of one-dimensional basis functions and a compact core, achieves substantial reductions in memory requirements. The methodology is demonstrated for two cases: a soil layer on rigid bedrock and a layered halfspace. Different separable dimensions are considered to capture various combinations of source and receiver configurations, frequencies, and material parameters. Results are validated against those obtained with the direct stiffness method and computation times and memo
In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity, we build on four overlapping traditions of digital writing research in computers and composition that inform how we frame literacies, how we study social media rhetorics, how we engage iteratively and reflexively with methodologies and technologies, and how we blend computational methods with qualitative methods. Drawing on these four traditions, our paper shows our iterative research process through which we gathered and analyzed a dataset of 32,000 posts (formerly known as tweets) from X (formerly Twitter) about prompt writing posted between November 2022 to May 2023. We present five themes about these emerging AI literacy practices: (1) areas of communication impacted by prompt writing, (2) micro-literacy resources shared for prompt writing, (3) market rhetoric shaping prompt writing, (4) rhetorical characteristics of prompts
Industry demands are growing for hyper-distributed applications that span from the cloud to the edge in domains such as smart manufacturing, transportation, and agriculture. Yet today's solutions struggle to meet these demands due to inherent limitations in scalability, interoperability, and trust. In this article, we introduce HERMES (Heterogeneous Computing Continuum with Resource Monetization, Orchestration, and Semantic) - a novel framework designed to transform connectivity and data utilization across the computing continuum. HERMES establishes an open, seamless, and secure environment where resources, from cloud servers to tiny edge devices, can be orchestrated intelligently, data and services can be monetized in a distributed marketplace, and knowledge is shared through semantic interoperability. By bridging these key facets, HERMES lays a foundation for a new generation of distributed applications that are more efficient, trustworthy, and autonomous.
Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly manual labeling by experts, limiting dataset size and model generalizability. Additionally, the complexity of ultrasound physics and acoustic shadow makes the images difficult for humans to interpret, leading to incomplete labels in anechoic regions and limiting model performance. To advance ultrasound bone segmentation and establish effective model benchmarks, larger and higher-quality datasets are needed. We propose a methodology for collecting ex-vivo ultrasound datasets with automatically generated bone labels, including anechoic regions. The proposed labels are derived by accurately superimposing tracked bone CT models onto the tracked ultrasound images. These initial labels are refined to account for ultrasound physics. A clinical evaluation is conducted by an expert physician specialized on orthopedic sonography to assess the quality of the generated bone labels.
Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model genera
Bilevel optimization provides a powerful framework for modelling hierarchical decision-making systems. This work presents a sensitivity-based algorithm that addresses the bilevel structure directly by treating the lower-level optimal solution as an implicit, locally differentiable function of the upper-level variables, thereby avoiding classical single-level reformulations. Under standard regularity assumptions on the lower level, an adjoint-based representation of the reduced upper-level gradient is derived, replacing explicit construction of the sensitivity Jacobian with a single linear adjoint solve per iteration and reducing gradient evaluation cost by a factor equal to the upper-level dimension. The reduced problem is solved within an Augmented Lagrangian framework, with inner subproblems managed by an L-BFGS-B quasi-Newton solver. Convergence to KKT points of the reduced problem is established, and these points are shown to be equivalent to S-stationary solutions of the associated mathematical programme with complementarity constraints under MPEC-LICQ. Computational experiments on benchmark bilevel problems validate the method's correctness and robustness, and demonstrate the
Representing turbulent flow fields in a compact yet physically faithful form remains a central challenge in computational fluid dynamics. We propose a continuous parametric representation based on localized Gaussian primitives, in which the velocity field is modeled as a superposition of kernels with learnable positions, amplitudes, and scales. This formulation yields a compact, grid-independent encoding while enabling evaluation of derived quantities such as vorticity and enstrophy. The approach is assessed on three-dimensional Taylor-Green vortex fields spanning stages from smooth flow to fully developed turbulence. We quantify the compression-accuracy trade-off using both primary variables and derivative-sensitive diagnostics. The baseline isotropic formulation achieves high velocity accuracy at compression ratios exceeding 1e3-1e4, but exhibits substantial enstrophy degradation due to loss of small-scale structure. To address this limitation, we investigate structure-aware extensions including adaptive placement, multi-resolution kernels, and anisotropic Gaussians. The anisotropic formulation provides the most consistent improvement, better aligning with elongated vortical stru
As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system's robustness and security. To protect vehicle information, we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing. To address the additional latency introduced by blockchain, we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency, ensuring that the system meets the requirements for low latency and high reliability. Simulation results demonstrate that the optimized data extraction rate significantly reduces system delay, with relatively stable variations in latency. Mor
Dark personality traits have long been associated with antisocial and toxic online behaviors, yet their relationship with observable online activity remains unclear. We investigate the association between validated dark personality measures, self-reported experiences of online incivility, and linguistic and behavioral features extracted from real-world user activity. To this end, we developed a Web application that securely links responses to validated psychological questionnaires collected via Amazon Mechanical Turk with participants' Reddit activity. This yielded a dataset of nearly 57K comments (2.2M tokens) from 114 users, represented through a broad set of linguistic and behavioral features. Our analyses reveal a clear distinction between self-reported and observed behavior. Dark personality traits show consistent associations with self-reported engagement in uncivil interactions. However, no validated dark personality dimension significantly predicts text-derived toxicity or linguistic features. In contrast, self-reported experiences of engaging in or being targeted by toxic behavior are robustly reflected in users' language, exhibiting consistent associations with measures o