pwc-archive/papers-with-abstracts huggingface dataset:
Fast Jet Tagging with MLP-Mixers on FPGAs
Authors: Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu
Date: 2025-03-05 00:00:00
Tasks: Jet Tagging, Quantization

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.
RF Desense significance and its impact on the EVM at Signal Near the Noise Floor
Authors: Rajesh Vedala, Harkirat Kaur, Palak Kapoor
Date: 2025-02-26 00:00:00

Hardware impairments and system non-linearities impacting communication signal is one of key aspect for having harmonics and RF desense which overall causing the lower quality and integrity of the modulated signal, resulting in I/Q imbalance, further bit error and spectral efficiency degradation. This presentation outlines the RF Desense results, EVM Measurement and it impact at the almost noise floor with step size by 1 dB in QPSK at LTE Bands, Note for mmW 3GPP 38.521-2 clause 6.4.2.1 indicates single polarization.
Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information
Authors: Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu, Yanqiu Feng, Xinyuan Zhang
Date: 2025-03-05 00:00:00
Tasks: Diffusion  MRI

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods.
Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine
Authors: Andrei V. Ermolaev, Mathilde Hary, Lev Leybov, Piotr Ryczkowski, Anas Skalli, Daniel Brunner, Goëry Genty, John M. Dudley
Date: 2025-03-05 00:00:00

We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit dataset as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. For this dataset and with quantum noise limited input, test accuracies of : over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.
Scene-based nonuniformity correction with homography transformation
Authors: Peretz Yafin, Nir Sochen, Iftach Klapp
Date: 2025-03-04 00:00:00

Due to their affordable, low mass, and small dimensions, uncooled microbolometer-based thermal focal plane arrays (UC-FPAs) are useful for long-wave infrared (LWIR)imaging applications. However, in outdoor conditions typical in agricultural remote sensing, cameras based on UC-FPAs may suffer from drift in offset and gain. To tackle the persistent drift, the system requires continuous calibration. Our goal in this study was to eliminate this requirement via a computational schema. In a former study, we estimated unknown gain and offset values and thermographic images of an object from a sequence of pairs of successive images taken at two different blur levels.In the current work, we took on a similar problem using a sequence of shifted images, with relative shifts caused by realistic drone hovering modeled by homography transformation. This places our work in the realm of scene-based nonuniformity correction problems. We show that an object's thermographic values, as well as gain and offset, can be jointly estimated by relying on a few sets of shifted images. We use a minimum likelihood estimator, which is found using alternating minimization. Registration is done using a generalized Lucas-Kanade method. Simulations show promising accuracy with mean Pearson correlation of more than 0.9999998 between ground truth and restoration. Under ideal assumptions, this is equivalent to a mean restoration error of less than 0.01 Celsius degree.
Integrated Computation and Communication with Fiber-optic Transmissions
Authors: Jiahao Zhang, Lu Zhang, Xiaodan Pang, Oskars Ozolins, Qun Zhang, Xianbin Yu
Date: 2025-03-04 00:00:00

Fiber-optic transmission systems are leveraged not only as high-speed communication channels but also as nonlinear kernel functions for machine learning computations, enabling the seamless integration of computational intelligence and communication.
Surface Topography as an Energy State: Hamiltonian Description of Functional Properties in Phase Space and Quantification via Optical Methods
Authors: Boris Brodmann, Matthias Eifler
Date: 2025-03-03 00:00:00

The assessment of tribological processes necessitates comprehensive monitoring and interpretation of surface states, traditionally by topography height values and surface texture parameters. This paper proposes a phase space representation as generic interpretation of surfaces to enhance tribological assessments. By deriving distinctions between two-dimensional and three-dimensional manifolds from phenomenological thermodynamics, we demonstrate the existence of fractal dimensions on physical surfaces. Utilizing the smallest representable wavelength, a coordinate ensemble where each coordinate has a generalized degree of freedom and a differential amount across fractal dimensions is proposed. The metric is interpreted as a time coordinate, allowing the derivative to be seen as velocity. Through geometric dimension analysis, we derive a topographic Hamiltonian via generalized momentum. Each coordinate pair in the ensemble is assigned a location in phase space. Statistical considerations define a symplectic structure and phase space volume. As a use-case, a two-disc experiment demonstrates that topography changes under load can be understood as a self-organization process through thermodynamic reasoning. As a possibility for a direct assessment of the phase space of a surface for monitoring tribological processes, we propose the combination of topographic measuring systems with scattering light measurement. This multi sensor approach offers opportunities for optimizing performance in tribological applications.
Quantum Non-Linear Bandit Optimization
Authors: Zakaria Shams Siam, Chaowen Guan, Chong Liu
Date: 2025-03-04 00:00:00
Tasks: Drug Discovery

We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfully applied in many critical applications such as drug discovery and hyperparameter tuning. Existing works have showed that with the aid of quantum computing, it is possible to break the $\Omega(\sqrt{T})$ regret lower bound in classical settings and achieve the new $O(\mathrm{poly}\log T)$ upper bound. However, they usually assume that the objective function sits within the reproducing kernel Hilbert space and their algorithms suffer from the curse of dimensionality. In this paper, we propose the new Q-NLB-UCB algorithm which uses the novel parametric function approximation technique and enjoys performance improvement due to quantum fast-forward and quantum Monte Carlo mean estimation. We prove that the regret bound of Q-NLB-UCB is not only $O(\mathrm{poly}\log T)$ but also input dimension-free, making it applicable for high-dimensional tasks. At the heart of our analyses are a new quantum regression oracle and a careful construction of parameter uncertainty region. Our algorithm is also validated for its efficiency on both synthetic and real-world tasks.
CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI
Authors: Mominul Islam, Mohammad Junayed Hasan, M. R. C. Mahdy
Date: 2025-03-04 00:00:00
Tasks: Alzheimer's Disease Detection, Quantum Machine Learning

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains in its early stages and requires further experimental analysis to better understand its behavior and limitations. In this paper, we propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data. Our approach involves developing a framework to make 3D MRI data usable for machine learning, designing and training a brain tissue segmentation model (Skull Net), and training a diffusion model to generate synthetic images for the minority class. Our converged models exhibit potential quantum advantages, achieving higher accuracy in fewer epochs than classical models. The proposed beta8 3 qubit model achieves an accuracy of 97.50%, surpassing state of the art (SOTA) models while requiring significantly fewer computational resources. In particular, the architecture employs only 13K parameters (0.48 MB), reducing the parameter count by more than 99.99% compared to current SOTA models. Furthermore, the diffusion-generated data used to train our quantum models, in conjunction with real samples, preserve clinical structural standards, representing a notable first in the field of QML. We conclude that CQCNN architecture like models, with further improvements in gradient optimization techniques, could become a viable option and even a potential alternative to classical models for AD detection, especially in data limited and resource constrained clinical settings.
Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
Authors: Hang Xu, Tailong Xiao, Jingzheng Huang, Ming He, Jianping Fan, Guihua Zeng
Date: 2025-03-04 00:00:00

Critical ground states of quantum many-body systems have emerged as vital resources for quantum-enhanced sensing. Traditional methods to prepare these states often rely on adiabatic evolution, which may diminish the quantum sensing advantage. In this work, we propose a quantum reinforcement learning (QRL)-enhanced critical sensing protocol for quantum many-body systems with exotic phase diagrams. Starting from product states and utilizing QRL-discovered gate sequences, we explore sensing accuracy in the presence of unknown external magnetic fields, covering both local and global regimes. Our results demonstrate that QRL-learned sequences reach the finite quantum speed limit and generalize effectively across systems of arbitrary size, ensuring accuracy regardless of preparation time. This method can robustly achieve Heisenberg and super-Heisenberg limits, even in noisy environments with practical Pauli measurements. Our study highlights the efficacy of QRL in enabling precise quantum state preparation, thereby advancing scalable, high-accuracy quantum critical sensing.
QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
Authors: Kangyu Zheng, Tianfan Fu, Zhiding Liang
Date: 2025-03-02 00:00:00
Tasks: Classification, imbalanced classification, Property Prediction, Quantum Machine Learning, regression

The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions. We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents the first comprehensive approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks-one imbalanced classification and one regression-demonstrates moderate positive correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation.
Adaptive Entanglement Routing with Deep Q-Networks in Quantum Networks
Authors: Lamarana Jallow, Majid Iqbal Khan
Date: 2025-03-01 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning

The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.
Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market
Authors: Katarzyna Chęć, Bartosz Uniejewski, Rafał Weron
Date: 2025-03-04 00:00:00

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 hours. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3\% to 15\% in terms of the root mean squared error and exceed 1\% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage.
Enhancing Efficiency of Local Projections Estimation with Volatility Clustering in High-Frequency Data
Authors: Chew Lian Chua, David Gunawan, Sandy Suardi
Date: 2025-03-04 00:00:00
Tasks: Clustering

This paper advances the local projections (LP) method by addressing its inefficiency in high-frequency economic and financial data with volatility clustering. We incorporate a generalized autoregressive conditional heteroskedasticity (GARCH) process to resolve serial correlation issues and extend the model with GARCH-X and GARCH-HAR structures. Monte Carlo simulations show that exploiting serial dependence in LP error structures improves efficiency across forecast horizons, remains robust to persistent volatility, and yields greater gains as sample size increases. Our findings contribute to refining LP estimation, enhancing its applicability in analyzing economic interventions and financial market dynamics.
Dynamic Factor Correlation Model
Authors: Chen Tong, Peter Reinhard Hansen
Date: 2025-03-03 00:00:00
Tasks: model

We introduce a new dynamic factor correlation model with a novel variation-free parametrization of factor loadings. The model is applicable to high dimensions and can accommodate time-varying correlations, heterogeneous heavy-tailed distributions, and dependent idiosyncratic shocks, such as those observed in returns on stocks in the same subindustry. We apply the model to a "small universe" with 12 asset returns and to a "large universe" with 323 asset returns. The former facilitates a comprehensive empirical analysis and comparisons and the latter demonstrates the flexibility and scalability of the model.
Bayesian inference for dynamic spatial quantile models with interactive effects
Authors: Tomohiro Ando, Jushan Bai, Kunpeng Li, Yong Song
Date: 2025-03-02 00:00:00
Tasks: Bayesian Inference

With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that incorporates unobserved heterogeneity. The proposed model captures the dynamic structure of panel data, high-dimensional cross-sectional dependence, and allows for heterogeneous regression coefficients. To estimate the model, we propose a novel Bayesian Markov Chain Monte Carlo (MCMC) algorithm. Contributions to Bayesian computation include the development of quantile randomization, a new Gibbs sampler for structural parameters, and stabilization of the tail behavior of the inverse Gaussian random generator. We establish Bayesian consistency for the proposed estimation method as both the time and cross-sectional dimensions of the panel approach infinity. Monte Carlo simulations demonstrate the effectiveness of the method. Finally, we illustrate the applicability of the approach through a case study on the quantile co-movement structure of the gasoline market.
Uniform Limit Theory for Network Data
Authors: Yuya Sasaki
Date: 2025-03-01 00:00:00

I present a novel uniform law of large numbers (ULLN) for network-dependent data. While Kojevnikov, Marmer, and Song (KMS, 2021) provide a comprehensive suite of limit theorems and a robust variance estimator for network-dependent processes, their analysis focuses on pointwise convergence. On the other hand, uniform convergence is essential for nonlinear estimators such as M and GMM estimators (e.g., Newey and McFadden, 1994, Section 2). Building on KMS, I establish the ULLN under network dependence and demonstrate its utility by proving the consistency of both M and GMM estimators. A byproduct of this work is a novel maximal inequality for network data, which may prove useful for future research beyond the scope of this paper.
When Should we Expect Non-Decreasing Returns from Data in Prediction Tasks?
Authors: Maximilian Schaefer
Date: 2025-03-05 00:00:00
Tasks: Collaborative Filtering

This article studies the change in the prediction accuracy of a response variable when the number of predictors increases, and all variables follow a multivariate normal distribution. Assuming that the correlations between variables are independently drawn, I show that adding variables leads to globally increasing returns to scale when the mean of the correlation distribution is zero. The speed of learning depends positively on the variance of the correlation distribution. I use simulations to study the more complex case of correlation distributions with a non-zero mean and find a pattern of decreasing returns followed by increasing returns to scale - as long as the variance of correlations is not degenerate, in which case globally decreasing returns emerge. I train a collaborative filtering algorithm using the MovieLens 1M dataset to analyze returns from adding variables in a more realistic setting and find globally increasing returns to scale across $2,000$ variables. The results suggest significant scale advantages from additional variables in prediction tasks.
Exchange Rate Sensitivity in Free Zone Trade: An Empirical Study of the Istanbul Ataturk Airport Free Zone
Authors: Sukru C. Demirtas
Date: 2025-03-05 00:00:00
Tasks: Sensitivity

This study as part of an ongoing research effort, empirically examines the relationship between foreign trade in the Istanbul Ataturk Airport Free Zone and exchange rate movements. Monthly data from 2003 to 2016 were analyzed through stationarity tests (Unit Root), followed by the Vector Autoregressive (VAR) model, Cointegration Analysis, and the Toda-Yamamoto Causality Test. The findings indicate that the exchange rate does not significantly affect imports and exports in the free zone. This result suggests that free zones, due to their structural characteristics and operational framework, may be relatively insulated from exchange rate fluctuations. The study contributes to the literature by providing a focused analysis of a specific free zone in Turkiye, highlighting the potential independence of free zone trade from exchange rate volatility.
How manipulable are prediction markets?
Authors: Itzhak Rasooly, Roberto Rozzi
Date: 2025-03-05 00:00:00
Tasks: Prediction

In this paper, we conduct a large-scale field experiment to investigate the manipulability of prediction markets. The main experiment involves randomly shocking prices across 817 separate markets; we then collect hourly price data to examine whether the effects of these shocks persist over time. We find that prediction markets can be manipulated: the effects of our trades are visible even 60 days after they have occurred. However, as predicted by our model, the effects of the manipulations somewhat fade over time. Markets with more traders, greater trading volume, and an external source of probability estimates are harder to manipulate.