pwc-archive/papers-with-abstracts huggingface dataset:
A quantum annealing approach to graph node embedding
Authors: Hristo N. Djidjev
Date: 2025-03-08 00:00:00

Node embedding is a key technique for representing graph nodes as vectors while preserving structural and relational properties, which enables machine learning tasks like feature extraction, clustering, and classification. While classical methods such as DeepWalk, node2vec, and graph convolutional networks learn node embeddings by capturing structural and relational patterns in graphs, they often require significant computational resources and struggle with scalability on large graphs. Quantum computing provides a promising alternative for graph-based learning by leveraging quantum effects and introducing novel optimization approaches. Variational quantum circuits and quantum kernel methods have been explored for embedding tasks, but their scalability remains limited due to the constraints of noisy intermediate-scale quantum (NISQ) hardware. In this paper, we investigate quantum annealing (QA) as an alternative approach that mitigates key challenges associated with quantum gate-based models. We propose several formulations of the node embedding problem as a quadratic unconstrained binary optimization (QUBO) instance, making it compatible with current quantum annealers such as those developed by D-Wave. We implement our algorithms on a D-Wave quantum annealer and evaluate their performance on graphs with up to 100 nodes and embedding dimensions of up to 5. Our findings indicate that QA is a viable approach for graph-based learning, providing a scalable and efficient alternative to previous quantum embedding techniques.
Bypassing orthogonalization in the quantum DPP sampler
Authors: Michaël Fanuel, Rémi Bardenet
Date: 2025-03-07 00:00:00

Given an $n\times r$ matrix $X$ of rank $r$, consider the problem of sampling $r$ integers $\mathtt{C}\subset \{1, \dots, n\}$ with probability proportional to the squared determinant of the rows of $X$ indexed by $\mathtt{C}$. The distribution of $\mathtt{C}$ is called a projection determinantal point process (DPP). The vanilla classical algorithm to sample a DPP works in two steps, an orthogonalization in $\mathcal{O}(nr^2)$ and a sampling step of the same cost. The bottleneck of recent quantum approaches to DPP sampling remains that preliminary orthogonalization step. For instance, (Kerenidis and Prakash, 2022) proposed an algorithm with the same $\mathcal{O}(nr^2)$ orthogonalization, followed by a $\mathcal{O}(nr)$ classical step to find the gates in a quantum circuit. The classical $\mathcal{O}(nr^2)$ orthogonalization thus still dominates the cost. Our first contribution is to reduce preprocessing to normalizing the columns of $X$, obtaining $\mathsf{X}$ in $\mathcal{O}(nr)$ classical operations. We show that a simple circuit inspired by the formalism of Kerenidis et al., 2022 samples a DPP of a type we had never encountered in applications, which is different from our target DPP. Plugging this circuit into a rejection sampling routine, we recover our target DPP after an expected $1/\det \mathsf{X}^\top\mathsf{X} = 1/a$ preparations of the quantum circuit. Using amplitude amplification, our second contribution is to boost the acceptance probability from $a$ to $1-a$ at the price of a circuit depth of $\mathcal{O}(r\log n/\sqrt{a})$ and $\mathcal{O}(\log n)$ extra qubits. Prepending a fast, sketching-based classical approximation of $a$, we obtain a pipeline to sample a projection DPP on a quantum computer, where the former $\mathcal{O}(nr^2)$ preprocessing bottleneck has been replaced by the $\mathcal{O}(nr)$ cost of normalizing the columns and the cost of our approximation of $a$.
On the similarity of bandwidth-tuned quantum kernels and classical kernels
Authors: Roberto Flórez-Ablan, Marco Roth, Jan Schnabel
Date: 2025-03-07 00:00:00
Tasks: Quantum Machine Learning

Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains uncertain. This limitation can be attributed to the exponential concentration phenomenon, which can impair both trainability and generalization. A common strategy to alleviate this is bandwidth tuning, which involves rescaling data points in the quantum model to improve generalization. In this work, we numerically demonstrate that optimal bandwidth tuning results in QKs that closely resemble radial basis function (RBF) kernels, leading to a lack of quantum advantage over classical methods. Moreover, we reveal that the size of optimal bandwidth tuning parameters further simplifies QKs, causing them to behave like polynomial kernels, corresponding to a low-order Taylor approximation of a RBF kernel. We thoroughly investigate this for fidelity quantum kernels and projected quantum kernels using various data encoding circuits across several classification datasets. We provide numerical evidence and derive a simple analytical model that elucidates how bandwidth tuning influences key quantities in classification tasks. Overall, our findings shed light on the mechanisms that render QK methods classically simulatable.
Architecture for a Trustworthy Quantum Chatbot
Authors: Yaiza Aragonés-Soria, Manuel Oriol
Date: 2025-03-06 00:00:00
Tasks: Chatbot, Large Language Model, Question Answering, Text Generation

Large language model (LLM)-based tools such as ChatGPT seem useful for classical programming assignments. The more specialized the field, the more likely they lack reliability because of the lack of data to train them. In the case of quantum computing, the quality of answers of generic chatbots is low. C4Q is a chatbot focused on quantum programs that addresses this challenge through a software architecture that integrates specialized LLMs to classify requests and specialized question answering modules with a deterministic logical engine to provide trustworthy quantum computing support. This article describes the latest version (2.0) of C4Q, which delivers several enhancements: ready-to-run Qiskit code for gate definitions and circuit operations, expanded features to solve software engineering tasks such as the travelling salesperson problem and the knapsack problem, and a feedback mechanism for iterative improvement. Extensive testing of the backend confirms the system's reliability, while empirical evaluations show that C4Q 2.0's classification LLM reaches near-perfect accuracy. The evaluation of the result consists in a comparative study with three existing chatbots highlighting C4Q 2.0's maintainability and correctness, reflecting on how software architecture decisions, such as separating deterministic logic from probabilistic text generation impact the quality of the results.
Bi-Lipschitz Ansatz for Anti-Symmetric Functions
Authors: Nadav Dym, Jianfeng Lu, Matan Mizrachi
Date: 2025-03-06 00:00:00

Motivated by applications for simulating quantum many body functions, we propose a new universal ansatz for approximating anti-symmetric functions. The main advantage of this ansatz over previous alternatives is that it is bi-Lipschitz with respect to a naturally defined metric. As a result, we are able to obtain quantitative approximation results for approximation of Lipschitz continuous antisymmetric functions. Moreover, we provide preliminary experimental evidence to the improved performance of this ansatz for learning antisymmetric functions.
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence
Authors: Alireza Habibi, Saeed Ghoorchian, Setareh Maghsudi
Date: 2025-03-06 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Q-Learning

The complexity of online decision-making under uncertainty stems from the requirement of finding a balance between exploiting known strategies and exploring new possibilities. Naturally, the uncertainty type plays a crucial role in developing decision-making strategies that manage complexity effectively. In this paper, we focus on a specific form of uncertainty known as epistemic ambivalence (EA), which emerges from conflicting pieces of evidence or contradictory experiences. It creates a delicate interplay between uncertainty and confidence, distinguishing it from epistemic uncertainty that typically diminishes with new information. Indeed, ambivalence can persist even after additional knowledge is acquired. To address this phenomenon, we propose a novel framework, called the epistemically ambivalent Markov decision process (EA-MDP), aiming to understand and control EA in decision-making processes. This framework incorporates the concept of a quantum state from the quantum mechanics formalism, and its core is to assess the probability and reward of every possible outcome. We calculate the reward function using quantum measurement techniques and prove the existence of an optimal policy and an optimal value function in the EA-MDP framework. We also propose the EA-epsilon-greedy Q-learning algorithm. To evaluate the impact of EA on decision-making and the expedience of our framework, we study two distinct experimental setups, namely the two-state problem and the lattice problem. Our results show that using our methods, the agent converges to the optimal policy in the presence of EA.
Nonlinear Temperature Sensitivity of Residential Electricity Demand: Evidence from a Distributional Regression Approach
Authors: Kyungsik Nam, Won-Ki Seo
Date: 2025-03-10 00:00:00
Tasks: Sensitivity

We estimate the temperature sensitivity of residential electricity demand during extreme temperature events using the distribution-to-scalar regression model. Rather than relying on simple averages or individual quantile statistics of raw temperature data, we construct distributional summaries, such as probability density, hazard rate, and quantile functions, to retain a more comprehensive representation of temperature variation. This approach not only utilizes richer information from the underlying temperature distribution but also enables the examination of extreme temperature effects that conventional models fail to capture. Additionally, recognizing that distribution functions are typically estimated from limited discrete observations and may be subject to measurement errors, our econometric framework explicitly addresses this issue. Empirical findings from the hazard-to-demand model indicate that residential electricity demand exhibits a stronger nonlinear response to cold waves than to heat waves, while heat wave shocks demonstrate a more pronounced incremental effect. Moreover, the temperature quantile-to-demand model produces largely insignificant demand response estimates, attributed to the offsetting influence of two counteracting forces.
Singularity-Based Consistent QML Estimation of Multiple Breakpoints in High-Dimensional Factor Models
Authors: Jiangtao Duan, Jushan Bai, Xu Han
Date: 2025-03-09 00:00:00

This paper investigates the estimation of high-dimensional factor models in which factor loadings undergo an unknown number of structural changes over time. Given that a model with multiple changes in factor loadings can be observationally indistinguishable from one with constant loadings but varying factor variances, this reduces the high-dimensional structural change problem to a lower-dimensional one. Due to the presence of multiple breakpoints, the factor space may expand, potentially causing the pseudo factor covariance matrix within some regimes to be singular. We define two types of breakpoints: {\bf a singular change}, where the number of factors in the combined regime exceeds the minimum number of factors in the two separate regimes, and {\bf a rotational change}, where the number of factors in the combined regime equals that in each separate regime. Under a singular change, we derive the properties of the small eigenvalues and establish the consistency of the QML estimators. Under a rotational change, unlike in the single-breakpoint case, the pseudo factor covariance matrix within each regime can be either full rank or singular, yet the QML estimation error for the breakpoints remains stably bounded. We further propose an information criterion (IC) to estimate the number of breakpoints and show that, with probability approaching one, it accurately identifies the true number of structural changes. Monte Carlo simulations confirm strong finite-sample performance. Finally, we apply our method to the FRED-MD dataset, identifying five structural breaks in factor loadings between 1959 and 2024.
Bounding the Effect of Persuasion with Monotonicity Assumptions: Reassessing the Impact of TV Debates
Authors: Sung Jae Jun, Sokbae Lee
Date: 2025-03-08 00:00:00

Televised debates between presidential candidates are often regarded as the exemplar of persuasive communication. Yet, recent evidence from Le Pennec and Pons (2023) indicates that they may not sway voters as strongly as popular belief suggests. We revisit their findings through the lens of the persuasion rate and introduce a robust framework that does not require exogenous treatment, parallel trends, or credible instruments. Instead, we leverage plausible monotonicity assumptions to partially identify the persuasion rate and related parameters. Our results reaffirm that the sharp upper bounds on the persuasive effects of TV debates remain modest.
When can we get away with using the two-way fixed effects regression?
Authors: Apoorva Lal
Date: 2025-03-07 00:00:00
Tasks: Econometrics, Navigate, regression

The use of the two-way fixed effects regression in empirical social science was historically motivated by folk wisdom that it uncovers the Average Treatment effect on the Treated (ATT) as in the canonical two-period two-group case. This belief has come under scrutiny recently due to recent results in applied econometrics showing that it fails to uncover meaningful averages of heterogeneous treatment effects in the presence of effect heterogeneity over time and across adoption cohorts, and several heterogeneity-robust alternatives have been proposed. However, these estimators often have higher variance and are therefore under-powered for many applications, which poses a bias-variance tradeoff that is challenging for researchers to navigate. In this paper, we propose simple tests of linear restrictions that can be used to test for differences in dynamic treatment effects over cohorts, which allows us to test for when the two-way fixed effects regression is likely to yield biased estimates of the ATT. These tests are implemented as methods in the pyfixest python library.
Enhancing Poverty Targeting with Spatial Machine Learning: An application to Indonesia
Authors: Rolando Gonzales Martinez, Mariza Cooray
Date: 2025-03-06 00:00:00
Tasks: Survey

This study leverages spatial machine learning (SML) to enhance the accuracy of Proxy Means Testing (PMT) for poverty targeting in Indonesia. Conventional PMT methodologies are prone to exclusion and inclusion errors due to their inability to account for spatial dependencies and regional heterogeneity. By integrating spatial contiguity matrices, SML models mitigate these limitations, facilitating a more precise identification and comparison of geographical poverty clusters. Utilizing household survey data from the Social Welfare Integrated Data Survey (DTKS) for the periods 2016 to 2020 and 2016 to 2021, this study examines spatial patterns in income distribution and delineates poverty clusters at both provincial and district levels. Empirical findings indicate that the proposed SML approach reduces exclusion errors from 28% to 20% compared to standard machine learning models, underscoring the critical role of spatial analysis in refining machine learning-based poverty targeting. These results highlight the potential of SML to inform the design of more equitable and effective social protection policies, particularly in geographically diverse contexts. Future research can explore the applicability of spatiotemporal models and assess the generalizability of SML approaches across varying socio-economic settings.
Optimal Policy Choices Under Uncertainty
Authors: Sarah Moon
Date: 2025-03-05 00:00:00
Tasks: Decision Making

Policymakers often make changes to policies whose benefits and costs are unknown and must be inferred from statistical estimates in empirical studies. The sample estimates are noisier for some policies than for others, which should be adjusted for when comparing policy changes in decision-making. In this paper I consider the problem of a planner who makes changes to upfront spending on a set of policies to maximize social welfare but faces statistical uncertainty about the impact of those changes. I set up an optimization problem that is tractable under statistical uncertainty and solve for the Bayes risk-minimizing decision rule. I propose an empirical Bayes approach to approximating the optimal decision rule when the planner does not know a prior. I show theoretically that the empirical Bayes decision rule can approximate the optimal decision rule well, including in cases where a sample plug-in rule does not.
How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model
Authors: Diego Vallarino
Date: 2025-03-03 00:00:00
Tasks: Decision Making, Demand Forecasting, Marketing, Mixture-of-Experts

Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consumer decision-making. This study introduces the Mixture of Experts (MoE) framework as a machine learning-driven alternative that dynamically segments consumers based on latent behavioral patterns. By leveraging probabilistic gating functions and specialized expert networks, MoE provides a flexible, nonparametric approach to modeling heterogeneous preferences. Empirical validation using large-scale retail data demonstrates that MoE significantly enhances predictive accuracy over traditional econometric models, capturing nonlinear consumer responses to price variations, brand preferences, and product attributes. The findings underscore MoEs potential to improve demand forecasting, optimize targeted marketing strategies, and refine segmentation practices. By offering a more granular and adaptive framework, this study bridges the gap between data-driven machine learning approaches and marketing theory, advocating for the integration of AI techniques in managerial decision-making and strategic consumer insights.
Optimal Diversification and Leverage in a Utility-Based Portfolio Allocation Approach
Authors: Vladimir Markov
Date: 2025-03-10 00:00:00

We examine the problem of optimal portfolio allocation within the framework of utility theory. We apply exponential utility to derive the optimal diversification strategy and logarithmic utility to determine the optimal leverage. We enhance existing methodologies by incorporating compound probability distributions to model the effects of both statistical and non-stationary uncertainties. Additionally, we extend the maximum expected utility objective by including the variance of utility in the objective function, which we term generalized mean-variance. In the case of logarithmic utility, it provides a natural explanation for the half-Kelly criterion, a concept widely used by practitioners.
Optimal energy storage management for self-consumption groups
Authors: Almendra Awerkin, Elena De Giuli, Tiziano Vargiolu
Date: 2025-03-10 00:00:00
Tasks: Management

We study the optimal management of a photovoltaic system's battery owned by a self-consumption group that aims to minimize energy consumption costs. We assume that the photovoltaic system is composed of a photovoltaic panel and a battery, where the photovoltaic panel produces energy according to a certain stochastic process. The management of the battery is the responsibility of a group administrator, who makes the joint decision to either store part of the photovoltaic energy production and sell the remaining energy at the electricity spot price, or discharge part of the energy stored in the battery and sell it in the electricity market. Inspired by European Union and Italian legislation, which promote incentives for energy transition and renewable energy production, we assume that the group receives a monetary incentive for the virtual self-consumed energy, defined as the minimum between the power bought from the grid to satisfy the group's power demand and the energy sold to the market. In this case, the energy sold by the group is a mix of part of the photovoltaic production that is not stored and part of the energy discharged from the battery. We model the problem as a stochastic optimal control problem, where the optimal strategy is the joint charge-discharge decision that minimizes the group's energy consumption costs. We find the solution numerically by applying a finite difference scheme to solve the Hamilton-Jacobi-Bellman equation associated with the value function of the optimal control problem.
The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI
Authors: Jakub Growiec, Klaus Prettner
Date: 2025-03-10 00:00:00

Recent advances in artificial intelligence (AI) have led to a diverse set of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from human extinction after a misaligned TAI takes over ("AI doom") to unprecedented economic growth and abundance ("post-scarcity"). However, the probabilities and implications of these scenarios remain highly uncertain. Here, we organize the various scenarios and evaluate their associated existential risks and economic outcomes in terms of aggregate welfare. Our analysis shows that even low-probability catastrophic outcomes justify large investments in AI safety and alignment research. We find that the optimizing representative individual would rationally allocate substantial resources to mitigate extinction risk; in some cases, she would prefer not to develop TAI at all. This result highlights that current global efforts in AI safety and alignment research are vastly insufficient relative to the scale and urgency of existential risks posed by TAI. Our findings therefore underscore the need for stronger safeguards to balance the potential economic benefits of TAI with the prevention of irreversible harm. Addressing these risks is crucial for steering technological progress toward sustainable human prosperity.
Financial Markets and ESG: How Big Data is Transforming Sustainable Investing in Developing countries
Authors: A T M Omor Faruq, Md Ataur Rahman Chowdhury
Date: 2025-03-09 00:00:00

This study explores the role of big data adoption and financial market development in driving ESG investments in developing countries, using an instrumental variable (IV) approach to address endogeneity. The results show that big data adoption significantly enhances ESG investing, as data-driven analytics improve sustainability assessments and capital allocation. Financial market development also positively influences ESG investments, but its effect is relatively small. A key finding is that inflation negatively impacts ESG investment, highlighting the importance of macroeconomic stability in fostering sustainable finance. In contrast, GDP per capita and foreign direct investment (FDI) are not significant determinants, suggesting that economic growth alone does not drive sustainability efforts. Overall, this study provides empirical evidence that leveraging big data and financial market improvements can accelerate sustainable investing in emerging economies. Policymakers should focus on technological advancements, financial reforms, and inflation control to strengthen ESG investments and long-term sustainability commitments.