Das Pemmaraju | Searcharxiv

arXiv2026

Many-Body Destabilization of Intermediate Oxygen-Hole States

Oxygen holes in transition-metal oxides can appear as localized polarons, symmetry-delocalized ligand holes, or intermediate states whose stability is controlled by subtle electron-correlation effects. In layered Na$_{2-x}$Mn$_3$O$_7$, hybrid density functional theory (DFT) predicts an unusual bond-centered split oxygen-hole polaron stabilized near ordered Mn vacancies. Here we resolve the nature of this state using diffusion Quantum Monte Carlo (QMC). Although hybrid DFT favors the split configuration, QMC reverses the energetic ordering and identifies the localized oxygen polaron as the lower-energy state. The result is robust to the class of trial wavefunctions used, including hybrid and generalized-gradient DFT wavefunctions. Many-body spin densities further show that the nominal split state partially collapses toward a localized polaron. Because localized and split configurations produce similar O K-edge spectral features, this qualitative failure is not resolved by conventional X-ray absorption signatures alone. These findings identify Na$_{2-x}$Mn$_3$O$_7$ as a stringent benchmark for oxygen-hole polarons and reveal a failure mode of hybrid functionals in correlated oxides.

cond-mat.mtrl-sci↗

arXiv2025

Enhanced fill probability estimates in institutional algorithmic bond trading using statistical learning algorithms with quantum computers

The estimation of fill probabilities for trade orders represents a key ingredient in the optimization of algorithmic trading strategies. It is bound by the complex dynamics of financial markets with inherent uncertainties, and the limitations of models aiming to learn from multivariate financial time series that often exhibit stochastic properties with hidden temporal patterns. In this paper, we focus on algorithmic responses to trade inquiries in the corporate bond market and investigate fill probability estimation errors of common machine learning models when given real production-scale intraday trade event data, transformed by a quantum algorithm running on IBM Heron processors, as well as on noiseless quantum simulators for comparison. We introduce a framework to embed these quantum-generated data transforms as a decoupled offline component that can be selectively queried by models in low-latency institutional trade optimization settings. A trade execution backtesting method is employed to evaluate the fill prediction performance of these models in relation to their input data. We observe a relative gain of up to ~ 34% in out-of-sample test scores for those models with access to quantum hardware-transformed data over those using the original trading data or transforms by noiseless quantum simulation. These empirical results suggest that the inherent noise in current quantum hardware contributes to this effect and motivates further studies. Our work demonstrates the emerging potential of quantum computing as a complementary explorative tool in quantitative finance and encourages applied industry research towards practical applications in trading.

quant-ph↗