pwc-archive/papers-with-abstracts huggingface dataset:
On the status of current quantum machine learning software
Authors: Manish K. Gupta, Tomasz Rybotycki, Piotr Gawron
Date: 2025-03-11 00:00:00
Tasks: Image Segmentation, Quantum Machine Learning, Semantic Segmentation

The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to study their application to real-life computational problems. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using satellite image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. We also analyzed the costs of such endeavor and the change in quality of model.
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
Authors: Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi, Sadok Ben Yahia, Muhammad Shafique
Date: 2025-03-11 00:00:00
Tasks: Image Enhancement, Image Restoration, Image Super-Resolution, Quantum Machine Learning, SSIM, Super-Resolution

Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a major obstacle. In this paper, we propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism, built upon variational quantum neural networks. QUIET-SR effectively captures complex residual mappings between low-resolution and high-resolution images, leveraging quantum attention mechanisms to enhance feature extraction and image restoration while requiring a minimal number of qubits, making it suitable for the Noisy Intermediate-Scale Quantum (NISQ) era. We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods while using fewer parameters. These findings highlight the potential of scalable variational quantum machine learning models for SISR, marking a step toward practical quantum-enhanced image super-resolution.
Energy Scale Degradation in Sparse Quantum Solvers: A Barrier to Quantum Utility
Authors: Thang N. Dinh, Cao P. Cong
Date: 2025-03-11 00:00:00

Quantum computing offers a promising route for tackling hard optimization problems by encoding them as Ising models. However, sparse qubit connectivity requires the use of minor-embedding, mapping logical qubits onto chains of physical qubits, which necessitates stronger intra-chain coupling to maintain consistency. This elevated coupling strength forces a rescaling of the Hamiltonian due to hardware-imposed limits on the allowable ranges of coupling strengths, reducing the energy gaps between competing states, thus, degrading the solver's performance. Here, we introduce a theoretical model that quantifies this degradation. We show that as the connectivity degree increases, the effective temperature rises as a polynomial function, resulting in a success probability that decays exponentially. Our analysis further establishes worst-case bounds on the energy scale degradation based on the inverse conductance of chain subgraphs, revealing two most important drivers of chain strength, \textit{chain volume} and \textit{chain connectivity}. Our findings indicate that achieving quantum advantage is inherently challenging. Experiments on D-Wave quantum annealers validate these findings, highlighting the need for hardware with improved connectivity and optimized scale-aware embedding algorithms.
Efficient and Accurate Estimation of Lipschitz Constants for Hybrid Quantum-Classical Decision Models
Authors: Sajjad Hashemian, Mohammad Saeed Arvenaghi
Date: 2025-03-11 00:00:00
Tasks: Computational Efficiency, Fairness, Learning Theory

In this paper, we propose a novel framework for efficiently and accurately estimating Lipschitz constants in hybrid quantum-classical decision models. Our approach integrates classical neural network with quantum variational circuits to address critical issues in learning theory such as fairness verification, robust training, and generalization. By a unified convex optimization formulation, we extend existing classical methods to capture the interplay between classical and quantum layers. This integrated strategy not only provide a tight bound on the Lipschitz constant but also improves computational efficiency with respect to the previous methods.
Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications
Authors: Milan Maksimovic, Ivan S. Maksymov
Date: 2025-03-10 00:00:00
Tasks: Quantum Machine Learning

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain -- all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics.
On the Wisdom of Crowds (of Economists)
Authors: Francis X. Diebold, Aaron Mora, Minchul Shin
Date: 2025-03-12 00:00:00
Tasks: Survey

We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are "portfolios" of forecasts. We characterize the speed and pattern of the gains from diversification and their eventual decrease with portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters (SPF), and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based "crowd size signature plots," which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration, which we characterize. We find that the gains from diversification are greater for inflation forecasts than for growth forecasts, but that both gains nevertheless decrease quite quickly, so that fewer SPF respondents than currently used may be adequate.
Functional Linear Projection and Impulse Response Analysis
Authors: Won-Ki Seo, Dakyung Seong
Date: 2025-03-11 00:00:00

This paper proposes econometric methods for studying how economic variables respond to function-valued shocks. Our methods are developed based on linear projection estimation of predictive regression models with a function-valued predictor and other control variables. We show that the linear projection coefficient associated with the functional variable allows for the impulse response interpretation in a functional structural vector autoregressive model under a certain identification scheme, similar to well-known Sims' (1972) causal chain, but with nontrivial complications in our functional setup. A novel estimator based on an operator Schur complement is proposed and its asymptotic properties are studied. We illustrate its empirical applicability with two examples involving functional variables: economy sentiment distributions and functional monetary policy shocks.
A primer on optimal transport for causal inference with observational data
Authors: Florian F Gunsilius
Date: 2025-03-10 00:00:00
Tasks: Causal Inference, counterfactual, Econometrics

The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science. The fundamental idea of analyzing probabilities by comparing their underlying state space naturally aligns with the core idea of causal inference, where understanding and quantifying counterfactual states is paramount. Despite this intuitive connection, explicit research at the intersection of optimal transport and causal inference is only beginning to develop. Yet, many foundational models in causal inference have implicitly relied on optimal transport principles for decades, without recognizing the underlying connection. Therefore, the goal of this review is to offer an introduction to the surprisingly deep existing connections between optimal transport and the identification of causal effects with observational data -- where optimal transport is not just a set of potential tools, but actually builds the foundation of model assumptions. As a result, this review is intended to unify the language and notation between different areas of statistics, mathematics, and econometrics, by pointing out these existing connections, and to explore novel problems and directions for future work in both areas derived from this realization.
Generative AI Adoption and Higher Order Skills
Authors: Piyush Gulati, Arianna Marchetti, Phanish Puranam, Victoria Sevcenko
Date: 2025-03-12 00:00:00

We study how Generative AI (GenAI) adoption is reshaping work. While prior studies show that GenAI enhances role-level productivity and task composition, its influence on skills - the fundamental enablers of task execution, and the ultimate basis for employability - is less understood. Using job postings from 596 US public firms that recruited explicitly for GenAI skills (2022-2024), we analyze how GenAI adoption shifts the demand for workers' domain-specific as well as higher-order (domain-agnostic) skills. Our findings reveal that roles with higher demand for cognitive skills are also more likely to explicitly advertise GenAI tool requirements such as ChatGPT, Copilot, etc. Further, a difference-in-differences analysis shows that the demand for social skills within GenAI adopting roles decreases by 4.5 percent post-ChatGPT launch. As cognitive and social skills are both meta-skills - i.e., they support the acquisition of future task-specific skills - our results suggest that the adoption of GenAI may be altering the trajectories of feasible upskilling.
Impact of Multi-Platform Social Media Strategy on Sales in E-Commerce
Authors: Xiaoning Wang, Yakov Bart, Serguei Netessine, Lynn Wu
Date: 2025-03-12 00:00:00

Over the past several decades, major social media platforms have become crucial channels for e-commerce retailers to connect with consumers, maintain engagement, and promote their offerings. While some retailers focus their efforts on a few key platforms, others choose a more diversified approach by spreading their efforts across multiple sites. Which strategy proves more effective and why? Drawing on a longitudinal dataset on e-commerce social media metrics and performance indicators, we find that, all else being equal, companies with a more diversified social media strategy outperform those focusing on fewer platforms, increasing total web sales by 2 to 5 percent. The key mechanism driving this finding appears to be the complementary effect of overlapping impressions across platforms. When followers are present on multiple platforms, repeated exposure to consistent messaging reinforces brand awareness and enhances purchase intent. Our findings highlight important managerial implications for diversifying social media efforts to reach potential customers more efficiently and ultimately boost sales.
Impact of the Pandemic on Currency Circulation in Brazil: Projections using the SARIMA Model
Authors: João Victor Monteiros de Andrade, Leonardo Santos da Cruz
Date: 2025-03-10 00:00:00
Tasks: counterfactual, Decision Making

This study analyzes the impact of the COVID-19 pandemic on currency circulation in Brazil by comparing actual data from 2000 to 2023 with counterfactual projections using the \textbf{SARIMA(3,1,1)(3,1,4)\textsubscript{12}} model. The model was selected based on an extensive parameter search, balancing accuracy and simplicity, and validated through the metrics MAPE, RMSE, and AIC. The results indicate a significant deviation between projected and observed values, with an average difference of R\$ 47.57 billion (13.95\%). This suggests that the pandemic, emergency policies, and the introduction of \textit{Pix} had a substantial impact on currency circulation. The robustness of the SARIMA model was confirmed, effectively capturing historical trends and seasonality, though findings emphasize the importance of considering exogenous variables, such as interest rates and macroeconomic policies, in future analyses. Future research should explore multivariate models incorporating economic indicators, long-term analysis of post-pandemic currency circulation trends, and studies on public cash-holding behavior. The results reinforce the need for continuous monitoring and econometric modeling to support decision-making in uncertain economic contexts.
Beyond the heat: The mental health toll of temperature and humidity in India
Authors: Manuela Fritz
Date: 2025-03-11 00:00:00
Tasks: Survey

Evidence on the heat-mental health nexus remains mixed. I show that this can be partly explained by previous studies focusing solely on temperature while neglecting temperature-humidity interactions. Using a measure that considers both indicators (wet bulb temperature), I assess the causal link between extreme heat and mental health, and its heterogeneity across socioeconomic indicators. I combine self-reported depression and anxiety levels from three Indian WHO-SAGE survey waves with climate data, leveraging quasi-random variation in heat exposure due to survey timing and location. The results reveal that extreme heat increases the risk of depression but not of anxiety. Importantly, these effects are consistently smaller when humidity is not considered. Finally, the study provides evidence that the District Mental Health Program plays a protective role in mitigating adverse mental health effects. The findings suggest that the costs induced by climate change need to account for the economic consequences of deteriorated mental health.
Hedonic Adaptation in the Age of AI: A Perspective on Diminishing Satisfaction Returns in Technology Adoption
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman, Nimish Vohra
Date: 2025-03-11 00:00:00
Tasks: Ethics, Marketing

The fast paced progress of artificial intelligence (AI) through scaling laws connecting rising computational power with improving performance has created tremendous technological breakthroughs. These breakthroughs do not translate to corresponding user satisfaction improvements, resulting in a general mismatch. This research suggests that hedonic adaptation the psychological process by which people revert to a baseline state of happiness after drastic change provides a suitable model for understanding this phenomenon. We argue that user satisfaction with AI follows a logarithmic path, thus creating a longterm "satisfaction gap" as people rapidly get used to new capabilities as expectations. This process occurs through discrete stages: initial excitement, declining returns, stabilization, and sporadic resurgence, depending on adaptation rate and capability introduction. These processes have far reaching implications for AI research, user experience design, marketing, and ethics, suggesting a paradigm shift from sole technical scaling to methods that sustain perceived value in the midst of human adaptation. This perspective reframes AI development, necessitating practices that align technological progress with people's subjective experience.
Multiple Speaker Separation from Noisy Sources in Reverberant Rooms using Relative Transfer Matrix
Authors: Wageesha N. Manamperi, Thushara D. Abhayapala
Date: 2025-03-12 00:00:00
Tasks: Speaker Separation

Separation of simultaneously active multiple speakers is a difficult task in environments with strong reverberation and many background noise sources. This paper uses the relative transfer matrix (ReTM), a generalization of the relative transfer function of a room, to propose a simple yet novel approach for separating concurrent speakers using noisy multichannel microphone recordings. The proposed method (i) allows multiple speech and background noise sources, (ii) includes reverberation, (iii) does not need the knowledge of the locations of speech and noise sources nor microphone locations and their relative geometry, and (iv) uses relatively small segment of recordings for training. We illustrate the speech source separation capability with improved intelligibility using a simulation study consisting of four speakers in the presence of three noise sources in a reverberant room. We also show the applicability of the method in a practical experiment in a real room.
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR
Authors: Sewade Ogun, Vincent Colotte, Emmanuel Vincent
Date: 2025-03-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Diversity, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Voice Conversion

Augmenting the training data of automatic speech recognition (ASR) systems with synthetic data generated by text-to-speech (TTS) or voice conversion (VC) has gained popularity in recent years. Several works have demonstrated improvements in ASR performance using this augmentation approach. However, because of the lower diversity of synthetic speech, naively combining synthetic and real data often does not yield the best results. In this work, we leverage recently proposed flow-based TTS/VC models allowing greater speech diversity, and assess the respective impact of augmenting various speech attributes on the word error rate (WER) achieved by several ASR models. Pitch augmentation and VC-based speaker augmentation are found to be ineffective in our setup. Jointly augmenting all other attributes reduces the WER of a Conformer-Transducer model by 11\% relative on Common Voice and by up to 35\% relative on LibriSpeech compared to training on real data only.
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Authors: Nannan Wu, Zhuo Kuang, Zengqiang Yan, Ping Wang, Li Yu
Date: 2025-03-12 00:00:00
Tasks: Fairness, Federated Learning, image-classification, Image Classification, Medical Image Classification

Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality data from a minority of clients-biases federated models toward more common high-quality images. This raises significant fairness concerns. Existing fair federated learning methods have demonstrated some effectiveness in solving this problem by aligning a single 0th- or 1st-order state of convergence (e.g., training loss or sharpness). However, we argue in this work that fairness based on such a single state is still not an adequate surrogate for fairness during testing, as these single metrics fail to fully capture the convergence characteristics, making them suboptimal for guiding fair learning. To address this limitation, we develop a generalized framework. Specifically, we propose assessing convergence using multiple states, defined as sharpness or perturbed loss computed at varying search distances. Building on this comprehensive assessment, we propose promoting fairness for these states across clients to achieve our ultimate fairness objective. This is accomplished through the proposed method, FedISM+. In FedISM+, the search distance evolves over time, progressively focusing on different states. We then incorporate two components in local training and global aggregation to ensure cross-client fairness for each state. This gradually makes convergence equitable for all states, thereby improving fairness during testing. Our empirical evaluations, performed on the well-known RSNA ICH and ISIC 2019 datasets, demonstrate the superiority of FedISM+ over existing state-of-the-art methods for fair federated learning. The code is available at https://github.com/wnn2000/FFL4MIA.
FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model
Authors: Jiahao Xia, Yutao Hu, Yaolei Qi, Zhenliang Li, Wenqi Shao, Junjun He, Ying Fu, Longjiang Zhang, Guanyu Yang
Date: 2025-03-12 00:00:00
Tasks: Image Generation

Solving medical imaging data scarcity through semantic image generation has attracted significant attention in recent years. However, existing methods primarily focus on generating whole-organ or large-tissue structures, showing limited effectiveness for organs with fine-grained structure. Due to stringent topological consistency, fragile coronary features, and complex 3D morphological heterogeneity in cardiac imaging, accurately reconstructing fine-grained anatomical details of the heart remains a great challenge. To address this problem, in this paper, we propose the Fine-grained Cardiac image Synthesis(FCaS) framework, established on 3D template conditional diffusion model. FCaS achieves precise cardiac structure generation using Template-guided Conditional Diffusion Model (TCDM) through bidirectional mechanisms, which provides the fine-grained topological structure information of target image through the guidance of template. Meanwhile, we design a deformable Mask Generation Module (MGM) to mitigate the scarcity of high-quality and diverse reference mask in the generation process. Furthermore, to alleviate the confusion caused by imprecise synthetic images, we propose a Confidence-aware Adaptive Learning (CAL) strategy to facilitate the pre-training of downstream segmentation tasks. Specifically, we introduce the Skip-Sampling Variance (SSV) estimation to obtain confidence maps, which are subsequently employed to rectify the pre-training on downstream tasks. Experimental results demonstrate that images generated from FCaS achieves state-of-the-art performance in topological consistency and visual quality, which significantly facilitates the downstream tasks as well. Code will be released in the future.