pwc-archive/papers-with-abstracts huggingface dataset:
Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance
Authors: Minati Rath, Hema Date
Date: 2025-04-09 00:00:00

This study explores the intersection of continuous-variable quantum computing (CVQC) and classical machine learning, focusing on CVQC data encoding techniques, including Displacement encoding and squeezing encoding, alongside Instantaneous Quantum Polynomial (IQP) encoding from discrete quantum computing. We perform an extensive empirical analysis to assess the impact of these encoding methods on classical machine learning models, such as Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and ensemble methods like Random Forest and LightGBM. Our findings indicate that CVQC-based encoding methods significantly enhance feature expressivity, resulting in improved classification accuracy and F1 scores, especially in high-dimensional and complex datasets. However, these improvements come with varying computational costs, which depend on the complexity of the encoding and the architecture of the machine learning models. Additionally, we examine the trade-off between quantum expressibility and classical learnability, offering valuable insights into the practical feasibility of incorporating these quantum encodings into real-world applications. This study contributes to the growing body of research on quantum-classical hybrid learning, emphasizing the role of CVQC in advancing quantum data representation and its integration into classical machine learning workflows.
Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
Authors: Michael Kölle, Tom Bintener, Maximilian Zorn, Gerhard Stenzel, Leo Sünkel, Thomas Gabor, Claudia Linnhoff-Popien
Date: 2025-04-08 00:00:00

Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T operations, to optimize circuits with four to six qubits. Comprehensive hyperparameter testing revealed that combining delete and swap strategies outperformed other approaches, demonstrating their effectiveness in developing robust GA-based quantum circuit optimizers.
A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
Authors: Azadeh Alavia, Hossein Akhoundib, Fatemeh Kouchmeshkib, Mojtaba Mahmoodianc, Sanduni Jayasinghec, Yongli Rena, Abdolrahman Alavi
Date: 2025-04-08 00:00:00
Tasks: Quantum Machine Learning, Structural Health Monitoring

Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Euclidean assumptions. In parallel, Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages superposition, entanglement, and interference within quantum state manifolds for learning tasks. This paper offers a unifying perspective by casting QML as a specialized yet more expressive branch of GML. We argue that quantum states, whether pure or mixed, reside on curved manifolds (e.g., projective Hilbert spaces or density-operator manifolds), mirroring how covariance matrices inhabit the manifold of symmetric positive definite (SPD) matrices or how image sets occupy Grassmann manifolds. However, QML also benefits from purely quantum properties, such as entanglement-induced curvature, that can yield richer kernel structures and more nuanced data embeddings. We illustrate these ideas with published and newly discussed results, including hybrid classical -quantum pipelines for diabetic foot ulcer classification and structural health monitoring. Despite near-term hardware limitations that constrain purely quantum solutions, hybrid architectures already demonstrate tangible benefits by combining classical manifold-based feature extraction with quantum embeddings. We present a detailed mathematical treatment of the geometrical underpinnings of quantum states, emphasizing parallels to classical Riemannian geometry and manifold-based optimization. Finally, we outline open research challenges and future directions, including Quantum Large Language Models (LLMs), quantum reinforcement learning, and emerging hardware approaches, demonstrating how synergizing GML and QML principles can unlock the next generation of machine intelligence.
Quantum Adaptive Self-Attention for Quantum Transformer Models
Authors: Chi-Sheng Chen, En-Jui Kuo
Date: 2025-04-05 00:00:00

Transformer models have revolutionized sequential learning across various domains, yet their self-attention mechanism incurs quadratic computational cost, posing limitations for real-time and resource-constrained tasks. To address this, we propose Quantum Adaptive Self-Attention (QASA), a novel hybrid architecture that enhances classical Transformer models with a quantum attention mechanism. QASA replaces dot-product attention with a parameterized quantum circuit (PQC) that adaptively captures inter-token relationships in the quantum Hilbert space. Additionally, a residual quantum projection module is introduced before the feedforward network to further refine temporal features. Our design retains classical efficiency in earlier layers while injecting quantum expressiveness in the final encoder block, ensuring compatibility with current NISQ hardware. Experiments on synthetic time-series tasks demonstrate that QASA achieves faster convergence and superior generalization compared to both standard Transformers and reduced classical variants. Preliminary complexity analysis suggests potential quantum advantages in gradient computation, opening new avenues for efficient quantum deep learning models.
Causal Inference under Interference through Designed Markets
Authors: Evan Munro
Date: 2025-04-09 00:00:00
Tasks: Causal Inference

Equilibrium effects make it challenging to evaluate the impact of an individual-level treatment on outcomes in a single market, even with data from a randomized trial. In some markets, however, a centralized mechanism allocates goods and imposes useful structure on spillovers. For a class of strategy-proof "cutoff" mechanisms, we propose an estimator for global treatment effects using individual-level data from one market, where treatment assignment is unconfounded. Algorithmically, we re-run a weighted and perturbed version of the mechanism. Under a continuum market approximation, the estimator is asymptotically normal and semi-parametrically efficient. We extend this approach to learn spillover-aware treatment rules with vanishing asymptotic regret. Empirically, adjusting for equilibrium effects notably diminishes the estimated effect of information on inequality in the Chilean school system.
Optimizing Data-driven Weights In Multidimensional Indexes
Authors: Lidia Ceriani, Chiara Gigliarano, Paolo Verme
Date: 2025-04-08 00:00:00

Multidimensional indexes are ubiquitous, and popular, but present non-negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies.
Rationalizing dynamic choices
Authors: Henrique de Oliveira, Rohit Lamba
Date: 2025-04-07 00:00:00

An analyst observes an agent take a sequence of actions. The analyst does not have access to the agent's information and ponders whether the observed actions could be justified through a rational Bayesian model with a known utility function. We show that the observed actions cannot be justified if and only if there is a single deviation argument that leaves the agent better off, regardless of the information. The result is then extended to allow for distributions over possible action sequences. Four applications are presented: monotonicity of rationalization with risk aversion, a potential rejection of the Bayesian model with observable data, feasible outcomes in dynamic information design, and partial identification of preferences without assumptions on information.
Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning
Authors: Shovon Sengupta, Bhanu Pratap, Amit Pawar
Date: 2025-04-06 00:00:00

The conventional linear Phillips curve model, while widely used in policymaking, often struggles to deliver accurate forecasts in the presence of structural breaks and inherent nonlinearities. This paper addresses these limitations by leveraging machine learning methods within a New Keynesian Phillips Curve framework to forecast and explain headline inflation in India, a major emerging economy. Our analysis demonstrates that machine learning-based approaches significantly outperform standard linear models in forecasting accuracy. Moreover, by employing explainable machine learning techniques, we reveal that the Phillips curve relationship in India is highly nonlinear, characterized by thresholds and interaction effects among key variables. Headline inflation is primarily driven by inflation expectations, followed by past inflation and the output gap, while supply shocks, except rainfall, exert only a marginal influence. These findings highlight the ability of machine learning models to improve forecast accuracy and uncover complex, nonlinear dynamics in inflation data, offering valuable insights for policymakers.
Estimating Demand with Recentered Instruments
Authors: Kirill Borusyak, Mauricio Caceres Bravo, Peter Hull
Date: 2025-04-05 00:00:00

We develop a new approach to estimating flexible demand models with exogenous supply-side shocks. Our approach avoids conventional assumptions of exogenous product characteristics, putting no restrictions on product entry, despite using instrumental variables that incorporate characteristic variation. The proposed instruments are model-predicted responses of endogenous variables to the exogenous shocks, recentered to avoid bias from endogenous characteristics. We illustrate the approach in a series of Monte Carlo simulations.
Regression Discontinuity Design with Distribution-Valued Outcomes
Authors: David Van Dijcke
Date: 2025-04-04 00:00:00
Tasks: regression

This article introduces Regression Discontinuity Design (RDD) with Distribution-Valued Outcomes (R3D), extending the standard RDD framework to settings where the outcome is a distribution rather than a scalar. Such settings arise when treatment is assigned at a higher level of aggregation than the outcome-for example, when a subsidy is allocated based on a firm-level revenue cutoff while the outcome of interest is the distribution of employee wages within the firm. Since standard RDD methods cannot accommodate such two-level randomness, I propose a novel approach based on random distributions. The target estimand is a "local average quantile treatment effect", which averages across random quantiles. To estimate this target, I introduce two related approaches: one that extends local polynomial regression to random quantiles and another based on local Fr\'echet regression, a form of functional regression. For both estimators, I establish asymptotic normality and develop uniform, debiased confidence bands together with a data-driven bandwidth selection procedure. Simulations validate these theoretical properties and show existing methods to be biased and inconsistent in this setting. I then apply the proposed methods to study the effects of gubernatorial party control on within-state income distributions in the US, using a close-election design. The results suggest a classic equality-efficiency tradeoff under Democratic governorship, driven by reductions in income at the top of the distribution.
Market-Based Portfolio Variance
Authors: Victor Olkhov
Date: 2025-04-10 00:00:00
Tasks: Time Series

The investor, who holds his portfolio and doesn't trade his shares, at current time can use the time series of the market trades that were made during the averaging interval with the securities of his portfolio to assess the current variance of the portfolio. We show how the time series of trades with the securities of the portfolio determine the time series of trades with the portfolio as a single market security. The time series of portfolio trades determine the return and variance of the portfolio in the same form as the time series of trades with securities determine their returns and variances. The description of any portfolio and any single market security is equal. The time series of portfolio trades define the decomposition of the portfolio variance by its securities. If the volumes of trades with all securities are assumed constant, the decomposition of the portfolio variance coincides with Markowitz's (1952) expression of variance. However, the real markets expose random volumes of trades. The portfolio variance that accounts for the randomness of trade volumes is a polynomial of the 4th degree in the variables of relative amounts invested into securities and with the coefficients different from covariances of securities returns. We discuss the possible origin of the latent and unintended assumption that Markowitz (1952) made to derive his result. Our description of the portfolio variance that accounts for the randomness of real trade volumes could help the portfolio managers and the majors like BlackRock's Aladdin and Asimov, JP Morgan, and the U.S. Fed to adjust their models and forecasts to the reality of random markets.
Trading Graph Neural Network
Authors: Xian Wu
Date: 2025-04-10 00:00:00
Tasks: Graph Neural Network

This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets.
DeepGreen: Effective LLM-Driven Green-washing Monitoring System Designed for Empirical Testing -- Evidence from China
Authors: Congluo Xu, Yu Miao, Yiling Xiao, Chengmengjia Lin
Date: 2025-04-10 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This paper proposes DeepGreen, an Large Language Model Driven (LLM-Driven) system for detecting corporate green-washing behaviour. Utilizing dual-layer LLM analysis, DeepGreen preliminarily identifies potential green keywords in financial statements and then assesses their implementation degree via iterative semantic analysis of LLM. A core variable GreenImplement is derived from the ratio from the two layers' output. We extract 204 financial statements of 68 companies from A-share market over three years, comprising 89,893 words, and analyse them through DeepGreen. Our analysis, supported by violin plots and K-means clustering, reveals insights and validates the variable against the Huazheng ESG rating. It offers a novel perspective for regulatory agencies and investors, serving as a proactive monitoring tool that complements traditional methods.Empirical tests show that green implementation can significantly boost the asset return rate of companies, but there is heterogeneity in scale. Small and medium-sized companies have limited contribution to asset return via green implementation, so there is a stronger motivation for green-washing.
Inequality at risk of automation? Gender differences in routine tasks intensity in developing country labor markets
Authors: Janneke Pieters, Ana Kujundzic, Rulof Burger, Joel Gondwe
Date: 2025-04-10 00:00:00

Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.
Blameocracy: Causal Attribution in Political Communication
Authors: Francesco Bilotta, Alberto Binetti, Giacomo Manferdini
Date: 2025-04-09 00:00:00

We propose a supervised method to detect causal attribution in political texts, distinguishing between expressions of merit and blame. Analyzing four million tweets shared by U.S. Congress members from 2012 to 2023, we document a pronounced shift toward causal attribution following the 2016 presidential election. The shift reflects changes in rhetorical strategy rather than compositional variation in the actors or topics of the political debate. Within causal communication, a trade-off emerges between positive and negative tone, with power status as the key determinant: government emphasizes merit, while opposition casts blame. This pattern distinguishes causal from purely affective communication. Finally, we show that blame is markedly more viral than merit, with this gap widening in the upper tail of the virality distribution, where blame is increasingly more prevalent among the most widely shared tweets.
The hybrid dilemma -- do hybrid technologies play a transitionary or stationary role in transitions processes?
Authors: Amir Mirzadeh Phirouzabadi
Date: 2025-04-08 00:00:00

This research unravels the stationary or transitionary dilemma of hybrid technologies in transitions processes. A system dynamics technology interaction framework is built and simulated based on Technological Innovation System and Lotka-Volterra to investigate the inter-technology relationship impacts and modes that hybrid technologies establish with incumbent and emerging technologies. This is conducted for the case of conventional, hybrid and battery electric vehicles under various scenarios . Results reveal that, by acting as an exploration-hybrid solution, hybrid technologies maintain a transitionary role by supporting mainly the technological development side of emerging technology. On the contrary, by acting as an exploitation-hybrid solution, they hardly (or never) sustain an inhibitive role against both the technological and market development sides of incumbent technology. While hybrid technologies may play a stationary role on the market development side in transitions processes, simulation results show that maintaining all inter-technology relationship modes as business-as-usual (i.e., baseline scenario) but instead simultaneously strengthening the various socio-technical dimensions of emerging technology and destabilising the various socio-technical dimensions of incumbent technology (i.e., sociotechnical scenario) is a more promising pathway in both short term (e.g., an accelerated uptake of emerging technology and decline of incumbent technology) and long term (e.g., highest emission reduction). Findings, additionally, reinforce the existence of both spillover and try-harder versions of 'sailing-ship effect', which are either seriously doubted in the literature or partially validated using raw bibliometric and patents data.
A model-based analysis of the AggregateEU mechanism: Implications of overbidding and non-commitment
Authors: Dávid Csercsik, Borbála Takácsné Tóth, Péter Kotek, László Á. Kóczy, Anne Neumann
Date: 2025-04-07 00:00:00

AggregateEU is a new centralised mechanism that provides a no-commitment platform to trade natural gas in the European Union. Throughout the consultation process, AggregateEU has been mocked as `Tinder of the European gas markets' as it helps consumers and suppliers to find partners, but leaves it up to the matched partners to decide whether or not to contract on the possible trade. The non-commitment nature leads to substantial overbidding and many non-realised matches. We propose a quantitative modelling framework to study the effect of overbidding in the AggergateEU demand aggregation or joint purchasing mechanism. We conclude that the mechanism is prone to overbidding and that overbidding has ambiguous effects on trade. Depending on the parameters, overbidding may facilitate trade, but may also result in highly inefficient outcomes when overbidding is combined with a miscoordination over the delivery points. Suggested remedies include allowing for convex bids, restrictions on overbidding, or giving up part of the non-binding character of the market. %Ideally, the traditional mechanisms of gas exchanges should be augmented by features of AggregateEU. Our results sugge
Underreporting of Intimate Partner Violence in Brazil
Authors: Diego de Maria André, José Raimundo Carvalho
Date: 2025-04-07 00:00:00

According to WHO (2013), in general 30% of all women worldwide who have been in a relationship have experienced physical and/or sexual violence by their intimate partner. However, only a small percentage of intimate partner violence (IPV) victims report it to the police. This phenomenon of under-reporting is known as ``dark figure''. This paper aims to investigate the factors associated with the reporting decision of IPV victims to the police in Brazil using the third wave of the ``Pesquisa de Condi\c{c}\~{o}es Socioecon\^{o}micas e Viol\^{e}ncia Dom\'{e}stica e Familiar contra a Mulher ($PCSVDF^{Mulher}$)''. Using a bivariate probit regression model with sample selection, we found that older white women, those who do not tolerate domestic violence, and women who have experienced physical violence are more likely to report IPV to the police. In contrast, married women, those with partners who abuse alcohol and those who witnessed or knew that their mothers had experienced IPV, are less likely to report it to law enforcement.