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We introduce QCLAB, an object-oriented MATLAB toolbox for constructing, representing, and simulating quantum circuits. Designed with an emphasis on numerical stability, efficiency, and performance, QCLAB provides a reliable platform for prototyping and testing quantum algorithms. For advanced performance needs, QCLAB++ serves as a complementary C++ package optimized for GPU-accelerated quantum circuit simulations. Together, QCLAB and QCLAB++ form a comprehensive toolkit, balancing the simplicity of MATLAB scripting with the computational power of GPU acceleration. This paper serves as an introduction to the package and its features along with a hands-on tutorial that invites researchers to explore its capabilities right away., Comment: 12 pages

We compare two approaches for solving high-dimensional eigenvalue problems with low-rank structure: the inexact Lanczos method and inexact polynomial-filtered subspace iteration. Inexactness stems from low-rank compression, enabling efficient representation of high-dimensional vectors in a low-rank tensor format. A primary challenge in these methods is that standard operations, such as matrix-vector products and linear combinations, increase tensor rank, necessitating rank truncation and hence approximation. The Lanczos method constructs an approximate orthonormal Krylov basis, which is often difficult to represent accurately using low-rank tensor formats, even when the eigenvectors themselves exhibit low-rank structure. In contrast, the low-rank polynomial-filtered subspace iteration uses approximate eigenvectors (Ritz vectors) directly as a subspace basis, bypassing the need for an orthonormal Krylov basis. Our analysis and numerical experiments demonstrate that inexact subspace iteration is much more robust to rank-truncation errors compared to the inexact Lanczos method. We further demonstrate that rank-truncated subspace iteration can converge for problems where the density matrix renormalization group method (DMRG) stagnates., Comment: 26 pages, 7 figures

Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT), is gaining traction in radiotherapy. The SynthRAD2025 dataset and Grand Challenge promote advancements in sCT generation by providing a benchmarking platform for algorithms using cone-beam CT (CBCT) and magnetic resonance imaging (MRI). The dataset includes 2362 cases: 890 MRI-CT and 1472 CBCT-CT pairs from head-and-neck, thoracic, and abdominal cancer patients treated at five European university medical centers (UMC Groningen, UMC Utrecht, Radboud UMC, LMU University Hospital Munich, and University Hospital of Cologne). Data were acquired with diverse scanners and protocols. Pre-processing, including rigid and deformable image registration, ensures high-quality, modality-aligned images. Extensive quality assurance validates image consistency and usability. All imaging data is provided in MetaImage (.mha) format, ensuring compatibility with medical image processing tools. Metadata, including acquisition parameters and registration details, is available in structured CSV files. To maintain dataset integrity, SynthRAD2025 is divided into training (65%), validation (10%), and test (25%) sets. The dataset is accessible at https://doi.org/10.5281/zenodo.14918089 under the SynthRAD2025 collection. This dataset supports benchmarking and the development of synthetic imaging techniques for radiotherapy applications. Use cases include sCT generation for MRI-only and MR-guided photon/proton therapy, CBCT-based dose calculations, and adaptive radiotherapy workflows. By integrating diverse acquisition settings, SynthRAD2025 fosters robust, generalizable image synthesis algorithms, advancing personalized cancer care and adaptive radiotherapy., Comment: 22 pages, 8 tables, 4 figures; Under submission to Medical Physics, as dataset paper for the SynhtRAD2025 Grand Challenge https://synthrad2025.grand-challenge.org/

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits: multiplication, addition, negation, and parity flip, and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-$d$ polynomials, $P_d(x)$, on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today's noisy quantum processors. We present a detailed implementation of $P_4(x)$ and report experimental results for polynomial approximations of common functions, obtained using IBM's Heron-class quantum processors and an ideal Qiskit simulator.

Very massive stars with masses over 100 Msun are key objects in the Universe for our understanding of chemical and energetic feedback in the Universe, but their evolution and fate are almost entirely determined by their wind mass loss. We aim to determine the mass of the most massive star known in the Local Group R136a1. For this we compute the first hydrodynamically consistent non-local thermodynamical equilibrium atmosphere models for both R136a1 (WN5h) as well as the binary system R144 (WN5/6h+WN6/7h) in the Tarantula nebula. Using the Potsdam Wolf-Rayet code, we simultaneously empirically derive and theoretically predict mass-loss rates and wind velocities. By fitting synthetic spectra derived from these models to multi-wavelength observations, we constrain the stellar and wind properties of R144 and R136a1. We first determine the clumping stratification required by our hydro-models to fit the spectra of R144 by using the available dynamical mass estimates for the two components. We then utilise this clumping stratification in hydrodynamic models of R136a1 and estimate a mass of $M_\mathrm{Hydro}$ of 233 Msun. Remarkably, the estimated mass is close to and entirely consistent with chemical homogeneous mass relations. This present-day mass of 233 Msun provides a lower limit to the initial stellar mass, that could be far higher due to previous wind mass loss., Comment: Accepted for publication in A&A, 20 pages, 18 figures, 4 tables

Unlike biological active matter that constantly adapt to their environment, the motors of synthetic active particles are typically agnostic to their surroundings and merely operate at constant force. Here, we design colloidal active rods capable of modulating their inner activity in response to crowding, thereby enforcing a primitive form of quorum sensing interactions. Through experiments, simulations and theory we elucidate the impact of these interactions on the phase behavior of isotropic active matter. We demonstrate that, when conditioned to density, motility regulation can either lead to an absorbing phase transition, where all particles freeze their dynamics, or to atypical phase separation, where flat interfaces supporting a net pressure drop are in mechanical equilibrium. Fully active and fully arrested particles can then form heterogeneous patterns ruled by the competition between quorum sensing and mechanical interactions. Beyond the specifics of motile colloids, we expect our findings to apply broadly to adaptive active matter assembled from living or synthetic units., Comment: 12 pages, 6 figures

This work extends the minimum-dissipation model of large-eddy simulation and symmetry-preserving discretization to account for active or passive scalar transport and complex physical mechanisms.This novel scalar-minimum-dissipation model exhibits several desirable properties. It includes the effect of scalar transport, in addition to shear, on the suppression and production of turbulence. It switches off at no-slip walls, ensuring accurate capture of near-wall flow behavior without needing a wall model. It also switches off in laminar and transitional flows so that it is capable of predicting laminar-turbulent transition. The new scalar-minimum-dissipation model combined with the symmetry-preserving discretization is successfully tested in a differentially heated cavity in OpenFOAM. The results show that the symmetry-preserving discretization significantly improves predictions of flow quantity and heat transfer on highly stretched meshes., Comment: 26 pages, 12 figures

Axisymmetric direct numerical simulations are carried out to study the hydrodynamics of a laminar, stationary, incompressible, Newtonian, single-phase flow in an Annular Plane Couette (APC) channel. This configuration is that of a Straight Plane Couette (SPC) flow but curved around itself to form an annulus. These simulations are validated by Particle Image Velocimetry measurements at different Reynolds numbers. The flow is analyzed using three dimensionless parameters: the channel aspect ratio $A_c$, which controls the effects of sidewall confinement, the channel curvature ratio $C_r$, which affects the centrifugal forces due to curvature, and the Reynolds number ${\rm Re}$. The rotation of the top annular plate generates a main flow in the azimuthal direction, while generating a secondary recirculation flow in the plane of the channel cross-section, due to the presence of a centrifugal force difference. As a result, the vertical profile of the azimuthal velocity deviates from the classical linear profile of the SPC flow, adopting an unexpected S-shape, similar to that observed in a turbulent plane Couette flow. Depending on the values of $A_c$ and $C_r$, the flow exhibits a wide range of behaviors, from a quasi-2D flow with a homogeneous shear rate at moderate ${\rm Re}$ with appropriate geometrical parameters ($A_c \gtrsim 5$, $C_r \lesssim 0.1$), to a complex 3D flow otherwise. Whereas it is laminar, the APC flow shares a strong analogy with a turbulent Taylor Couette (TC) flow. At large Reynolds numbers, the velocity gradients concentrate near the wall and the flow reaches an asymptotic regime where the torque scales as ${\rm Re}^\alpha$ and the flow structure becomes independent of ${\rm Re}$. Such properties make the APC flow an interesting configuration for fundamental investigations as a complement of the TC flow when shear and gravity are required to be in the same plane. In particular, it can be used to investigate the rheology of a dispersed two-phase mixture similarly to the work done by Yi et al. \cite{YI2021,Yi2022} in the TC device.

This is the second part to our companion paper. The novel method to quantify artificial dissipation proposed in Part 1 is further applied in turbulent channel flow at $\mathrm{Re_\tau}=180$ using various subgrid-scale models, with an emphasis on minimum-dissipation models (QR and AMD). We found that the amount of artificial viscosity is comparable to the eddy viscosity, but their distributions are essentially opposite. The artificial viscosity can either produce turbulent kinetic energy (TKE) in the near wall region or dissipate TKE in the bulk region. The eddy viscosity is almost uniformly distributed across the wall-normal direction and actively damps TKE at the channel center. An eddy viscosity level of $\nu_e/\nu < 22\%$ leads to accurate predictions of flow quantities at the current mesh resolution. The optimal coefficients for the QR model combined with symmetry-preserving discretization were found to be C=0.092 for turbulent kinetic energy and C=0.012 for Reynolds stress terms and large scale motions. Adjusting the QR model coefficient changes the artificial viscosity and significantly impact turbulence characteristics: small-scale motions remain unaffected, while larger scales are less accurately captured with larger coefficients. For error quantification, the SGS activity parameter $\nu_{rel} = \frac{\nu_e}{\nu_e + \nu}$ helps relate and reflect the error magnitude with the SGS model. Optimal coefficients for the AMD model were also identified. The trends of varying the coefficient are similar to the QR model but differ in the amplitude of changes. The AMD model is more sensitive to the model coefficients and introduces more eddy viscosity in the near-wall region compared to the QR model. This research highlights the importance of balancing numerical and eddy viscosities for accurate LES predictions., Comment: 18 pages, 13 figures

We propose a novel method to quantify artificial dissipation in large eddy simulation. Here, artificial dissipation is defined as the residual of the discrete turbulent kinetic energy (TKE) equation. This method is applied to turbulent channel flow at Reynolds number 180 and 550 using a minimum-dissipation model within the OpenFOAM framework, employing both symmetry-preserving and standard OpenFOAM discretizations. Our analysis reveals that artificial viscosity can both produce and dissipate TKE. To quantify the interaction between sub-grid-scale model contributions and numerical error, we introduce viscous activity parameters, which also allow for evaluating the balance between molecular and non-molecular viscosity. Examining temporal discretization schemes, we find that all methods can deliver accurate flow predictions if an appropriate time step is used. For very small time steps, Forward-Euler is more efficient and accurate than Crank-Nicolson, though a better balance of accuracy and computational efficiency for larger time steps, making it preferable for large-scale applications. On collocated meshes, Van Kan's pressure correction method predicts better results than Chorin's. Our spatial discretization study shows that the symmetry-preserving scheme outperforms standard OpenFOAM schemes, as excessive artificial errors from spatial discretization can suppress the LES model. For the symmetry-preserving scheme, both eddy and artificial viscosities vary similarly, with eddy viscosity being dominant. The role of artificial viscosity changes with mesh refinement: on coarse meshes, it mainly dissipates TKE, while on finer meshes, it can produce TKE, counteracting eddy viscosity. The most accurate Reynolds shear stress predictions occur when non-molecular dissipation is nearly zero. For accurate mean flow kinetic energy, larger non-molecular dissipation is needed on finer meshes., Comment: 25 pages, 11 figures

Neuromorphic HCI is a new theoretical approach to designing better UX inspired by the neurophysiology of the brain. Here, we apply the neuroscientific theory of Active Inference to HCI, postulating that users perform Bayesian inference on progress and goal distributions to predict their next action (Interactive Inference). We show how Bayesian surprise between goal and progress distributions follows a mean square error function of the signal-to-noise ratio (SNR) of the task. However, capacity to process Bayesian surprise follows the logarithm of SNR, and errors occur when average capacity is exceeded. Our model allows the quantitative analysis of performance and error in one framework with real-time estimation of mental load. We show through mathematical theorems how three basic laws of HCI, Hick's Law, Fitts' Law and the Power Law fit our model. We then test the validity of the general model by empirically measuring how well it predicts human performance in a car following task. Results suggest that driver processing capacity indeed is a logarithmic function of the SNR of the distance to a lead car. This positive result provides initial evidence that Interactive Interference can work as a new theoretical underpinning for HCI, deserving further exploration., Comment: 18 pages, 7 figures, 1 table, 35 mathematical formulas, submitted for review

We apply a mixed quantum-classical (MQC) approach to the quantum Rabi model, involving a classical optical field coupled self-consistently to a quantum two-level system. Under the rotating wave approximation, we analytically show this approach to yield persistent yet anharmonic Rabi oscillations, governed by an undamped and unforced Duffing equation. We consider the single-quantum limit, where we find such anharmonic Rabi oscillations to closely follow full-quantum results once zero-point energy is approximately enforced when initializing the optical field coordinate. Our findings provide guidance in the application of MQC dynamics to classes of problems involving small quantum numbers and far away from decoherence., Comment: 7 pages, 3 figures

The material properties of polycrystals are strongly affected by the evolution and coarsening of their internal grain structures. Yet, studying this process is challenging due to the complex interactions within grain boundary networks. Here, we systematically investigate the shrinkage of isolated loop-shaped grain boundaries in 2D colloidal crystals. Unexpectedly, we find that shear coupling decreases with increasing grain misorientation, contrary to geometric predictions. This counterintuitive result is attributed to enhanced concurrent sliding driven by the annihilation of dislocations. Furthermore, by focusing on the evolution of the grain size, we reveal a transition in shrinkage kinetics between small and large loop sizes, offering an explanation for previously observed discrepancies in grain boundary mobility. These findings reveal a more intricate dependence of grain boundary behavior on curvature and misorientation than previously reported, offering new insights into polycrystal coarsening dynamics.

Safety has become the central value around which dominant AI governance efforts are being shaped. Recently, this culminated in the publication of the International AI Safety Report, written by 96 experts of which 30 nominated by the Organisation for Economic Co-operation and Development (OECD), the European Union (EU), and the United Nations (UN). The report focuses on the safety risks of general-purpose AI and available technical mitigation approaches. In this response, informed by a system safety perspective, I refl ect on the key conclusions of the report, identifying fundamental issues in the currently dominant technical framing of AI safety and how this frustrates meaningful discourse and policy efforts to address safety comprehensively. The system safety discipline has dealt with the safety risks of software-based systems for many decades, and understands safety risks in AI systems as sociotechnical and requiring consideration of technical and non-technical factors and their interactions. The International AI Safety report does identify the need for system safety approaches. Lessons, concepts and methods from system safety indeed provide an important blueprint for overcoming current shortcomings in technical approaches by integrating rather than adding on non-technical factors and interventions. I conclude with why building a system safety discipline can help us overcome limitations in the European AI Act, as well as how the discipline can help shape sustainable investments into Public Interest AI., Comment: A response to the International AI Safety Report, which was released in preparation for the AI Action Summit in Paris, February 2025

FPGAs have transformed digital design by enabling versatile and customizable solutions that balance performance and power efficiency, yielding them essential for today's diverse computing challenges. Research in the Netherlands, both in academia and industry, plays a major role in developing new innovative FPGA solutions. This survey presents the current landscape of FPGA innovation research in the Netherlands by delving into ongoing projects, advancements, and breakthroughs in the field. Focusing on recent research outcome (within the past 5 years), we have identified five key research areas: a) FPGA architecture, b) FPGA robustness, c) data center infrastructure and high-performance computing, d) programming models and tools, and e) applications. This survey provides in-depth insights beyond a mere snapshot of the current innovation research landscape by highlighting future research directions within each key area; these insights can serve as a foundational resource to inform potential national-level investments in FPGA technology.

Autoencoders are frequently used for anomaly detection, both in the unsupervised and semi-supervised settings. They rely on the assumption that when trained using the reconstruction loss, they will be able to reconstruct normal data more accurately than anomalous data. Some recent works have posited that this assumption may not always hold, but little has been done to study the validity of the assumption in theory. In this work we show that this assumption indeed does not hold, and illustrate that anomalies, lying far away from normal data, can be perfectly reconstructed in practice. We revisit the theory of failure of linear autoencoders for anomaly detection by showing how they can perfectly reconstruct out of bounds, or extrapolate undesirably, and note how this can be dangerous in safety critical applications. We connect this to non-linear autoencoders through experiments on both tabular data and real-world image data, the two primary application areas of autoencoders for anomaly detection.

Quantitative tools are increasingly appealing for decision support in healthcare, driven by the growing capabilities of advanced AI systems. However, understanding the predictive uncertainties surrounding a tool's output is crucial for decision-makers to ensure reliable and transparent decisions. In this paper, we present a case study on pulmonary nodule detection for lung cancer screening, enhancing an advanced detection model with an uncertainty quantification technique called conformal risk control (CRC). We demonstrate that prediction sets with conformal guarantees are attractive measures of predictive uncertainty in the safety-critical healthcare domain, allowing end-users to achieve arbitrary validity by trading off false positives and providing formal statistical guarantees on model performance. Among ground-truth nodules annotated by at least three radiologists, our model achieves a sensitivity that is competitive with that generally achieved by individual radiologists, with a slight increase in false positives. Furthermore, we illustrate the risks of using off-the-shelve prediction models when faced with ontological uncertainty, such as when radiologists disagree on what constitutes the ground truth on pulmonary nodules.

Solvation free energy is an important design parameter in reaction kinetics and separation processes, making it a critical property to predict during process development. In previous research, directed message passing neural networks (D-MPNN) have successfully been used to predict solvation free energies and enthalpies in organic solvents. However, solvent mixtures provide greater flexibility for optimizing solvent interactions than monosolvents. This work aims to extend our previous models to mixtures. To handle mixtures in a permutation invariant manner we propose a pooling function; MolPool. With this pooling function, the machine learning models can learn and predict properties for an arbitrary number of molecules. The novel SolProp-mix software that applies MolPool to D-MPNN was compared to state-of-the-art architectures for predicting mixture properties and validated with our new database of COSMOtherm calculations; BinarySolv-QM. To improve predictions towards experimental accuracy, the network was then fine-tuned on experimental data in monosolvents. To demonstrate the benefit of this transfer learning methodology, experimental datasets of solvation free energies in binary (BinarySolv-Exp) and ternary (TernarySolv-Exp) solvent mixtures were compiled from data on vapor-liquid equilibria and activity coefficients. The neural network performed better than COSMOtherm calculations with an MAE of 0.25 kcal/mol and an RMSE of 0.37 kcal/mol for non-aqueous mixed solvents. Additionally, the ability to capture trends for a varying mixture composition was validated successfully. Our model's ability to accurately predict mixture properties from the combination of in silico data and pure component experimental data is promising given the scarcity of experimental data for mixtures in many fields.

The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cognitive processes such as cognitive persistence and visual habituation. We apply our approach by experimenting with a simulated robotic arm on a set of objects with varying difficulty. The robot first discovers new goals via bottom-up attention through motor babbling with an inhibition of return mechanism, then engage to the learning of goals due to neural activity arising within the curiosity mechanism. The architecture is modelled with dynamic neural fields and the learning of goals such as pushing the objects in diverse directions is supported by the use of forward and inverse models implemented by multi-layer perceptrons. The adoption of dynamic neural fields to model curiosity, habituation and persistence allows the robot to demonstrate various learning trajectories depending on the object. In addition, the approach exhibits interesting properties regarding the learning of similar goals as well as the continuous switch between exploration and exploitation.

To effectively address potential harms from AI systems, it is essential to identify and mitigate system-level hazards. Current analysis approaches focus on individual components of an AI system, like training data or models, in isolation, overlooking hazards from component interactions or how they are situated within a company's development process. To this end, we draw from the established field of system safety, which considers safety as an emergent property of the entire system, not just its components. In this work, we translate System Theoretic Process Analysis (STPA) - a recognized system safety framework - for analyzing AI operation and development processes. We focus on systems that rely on machine learning algorithms and conducted STPA on three case studies involving linear regression, reinforcement learning, and transformer-based generative models. Our analysis explored how STPA's control and system-theoretic perspectives apply to AI systems and whether unique AI traits - such as model opacity, capability uncertainty, and output complexity - necessitate significant modifications to the framework. We find that the key concepts and steps of conducting an STPA readily apply, albeit with a few adaptations tailored for AI systems. We present the Process-oriented Hazard Analysis for AI Systems (PHASE) as a guideline that adapts STPA concepts for AI, making STPA-based hazard analysis more accessible. PHASE enables four key affordances for analysts responsible for managing AI system harms: 1) detection of hazards at the systems level, including those from accumulation of disparate issues; 2) explicit acknowledgment of social factors contributing to experiences of algorithmic harms; 3) creation of traceable accountability chains between harms and those who can mitigate the harm; and 4) ongoing monitoring and mitigation of new hazards.

This paper introduces a novel neural network-based approach to solving the Monge-Amp\`ere equation with the transport boundary condition, specifically targeted towards optical design applications. We leverage multilayer perceptron networks to learn approximate solutions by minimizing a loss function that encompasses the equation's residual, boundary conditions, and convexity constraints. Our main results demonstrate the efficacy of this method, optimized using L-BFGS, through a series of test cases encompassing symmetric and asymmetric circle-to-circle, square-to-circle, and circle-to-flower reflector mapping problems. Comparative analysis with a conventional least-squares finite-difference solver reveals the competitive, and often superior, performance of our neural network approach on the test cases examined here. A comprehensive hyperparameter study further illuminates the impact of factors such as sampling density, network architecture, and optimization algorithm. While promising, further investigation is needed to verify the method's robustness for more complicated problems and to ensure consistent convergence. Nonetheless, the simplicity and adaptability of this neural network-based approach position it as a compelling alternative to specialized partial differential equation solvers.

Seamlessly integrating stimulated Brillouin scattering (SBS) in a low-loss and mature photonic integration platform remains a complicated task. Virtually all current approaches fall short in simultaneously achieving strong SBS, low losses, and technological scalability. In this work we incorporate stong SBS into a standard silicon nitride platform by a simple deposition of a tellurium oxide layer, a commonly used material for acousto-optic modulators. In these heterogeneously integrated waveguides, we harness novel SBS interactions actuated by surface acoustic waves (SAWs) leading to more than two orders of magnitude gain enhancement. Three novel applications are demonstrated in this platform: (i) a silicon nitride Brillouin amplifier with 5 dB net optical gain, (ii) a compact intermodal stimulated Brillouin laser (SBL) capable of high purity radio frequency (RF) signal generation with 7 Hz intrinsic linewidth, and (iii) a widely tunable microwave photonic notch filter with ultra-narrow linewidth of 2.2 MHz enabled by Brillouin induced opacity. These advancements can unlock an array of new RF and optical technologies to be directly integrated in silicon nitride.

Zero-dimensional (0D) cardiovascular models are reduced-order models used to study global circulation dynamics and transport. They provide estimates of biomarkers (such as pressure, flow rates, and concentrations) for surgery planning and boundary conditions for high-fidelity 3D models. Although their computational cost is low, tasks like parameter estimation and uncertainty quantification require many model evaluations, making them computationally expensive. This motivates building metamodels. In this work, we propose a pipeline from 0D models to metamodel building for tasks such as sensitivity analysis, parameter estimation, and uncertainty quantification. Three strategies are explored: Neural Networks, Polynomial Chaos Expansion, and Gaussian Processes, applied to three different 0D models. The first model predicts portal vein pressure after surgery, considering liver hemodynamics and global circulation. The second simulates whole-body circulation under pulmonary arterial hypertension before and after shunt insertion. The third assesses organ blood perfusion after revascularization surgery, focusing on contrast agent transport, requiring specific metamodel treatment. Metamodels are trained and tested on synthetic data. Neural networks proved the most efficient in terms of result quality, computational time, and ease for parameter estimation, sensitivity analysis, and uncertainty quantification. Finally, we demonstrate the full pipeline with a neural network as the emulator.

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground state calculations, is underexplored. In this work, we introduce the framework of multi-observable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition, a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thus marking a new application of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as $\exp(- \Delta E t_{\rm max})$, where $\Delta E$ is the Hamiltonian spectral gap and $t_{\rm max}$ is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e. ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices., Comment: 32 pages (main text 15 pages), 7 figures (main text 5 figures)

In this paper, we show how $K$-nearest neighbor ($K$-NN) resampling, an off-policy evaluation method proposed in \cite{giegrich2023k}, can be applied to simulate limit order book (LOB) markets and how it can be used to evaluate and calibrate trading strategies. Using historical LOB data, we demonstrate that our simulation method is capable of recreating realistic LOB dynamics and that synthetic trading within the simulation leads to a market impact in line with the corresponding literature. Compared to other statistical LOB simulation methods, our algorithm has theoretical convergence guarantees under general conditions, does not require optimization, is easy to implement and computationally efficient. Furthermore, we show that in a benchmark comparison our method outperforms a deep learning-based algorithm for several key statistics. In the context of a LOB with pro-rata type matching, we demonstrate how our algorithm can calibrate the size of limit orders for a liquidation strategy. Finally, we describe how $K$-NN resampling can be modified for choices of higher dimensional state spaces.

Rational functions are exceptionally powerful tools in scientific computing, yet their abilities to advance quantum algorithms remain largely untapped. In this paper, we introduce effective implementations of rational transformations of a target operator on quantum hardware. By leveraging suitable integral representations of the operator resolvent, we show that rational transformations can be performed efficiently with Hamiltonian simulations using a linear-combination-of-unitaries (LCU). We formulate two complementary LCU approaches, discrete-time and continuous-time LCU, each providing unique strategies to decomposing the exact integral representations of a resolvent. We consider quantum rational transformation for the ubiquitous task of approximating functions of a Hermitian operator, with particular emphasis on the elementary signum function. For illustration, we discuss its application to the ground and excited state problems. Combining rational transformations with observable dynamic mode decomposition (ODMD), our recently developed noise-resilient quantum eigensolver, we design a fully real-time approach for resolving many-body spectra. Our numerical demonstration on spin systems indicates that our real-time framework is compact and achieves accurate estimation of the low-lying energies., Comment: 34 pages (main text 26 pages)

The Dial-a-Ride Problem (DARP) is an optimization problem that involves determining optimal routes and schedules for several vehicles to pick up and deliver items at minimum cost. Motivated by real-world carpooling and crowdshipping scenarios, we introduce an additional constraint imposing a maximum number on the number of pickups per trip. This results in the Dial-a-Ride Problem with Limited Pickups per Trip (DARP-LPT). We apply a fragment-based method for DARP-LPT, where a fragment is a partial path. Specifically, we extend two formulations from Rist & Forbes (2021): the Fragment Flow Formulation (FFF) and the Fragment Assignment Formulation (FAF). We establish FFF's superiority over FAF, both from a theoretical as well as from a computational perspective. Furthermore, our results show that FFF and FAF significantly outperform traditional arc-based formulations in terms of solution quality and time. Additionally, compared to the two existing fragment sets, one with longer partial paths and another with shorter ones, our newly generated fragment sets perform better in terms of solution quality and time when fed into FFF., Comment: Presented at 38th Annual Conference of the Belgian Operational Research Society, Date: 2024/02/08 - 2024/02/09, Location: Antwerp