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20212026
most citedDeep Switching State Space Model (DSM) for Nonlinear Time Series Forecasting with Regime Switching

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

quant-ph2026

When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning

Hanqiu Peng, Jianlong Lu, Ying Chen

Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small local perturbations can cross inter…

q-fin.RM2026

Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces

Christopher Gerling, Hanqiu Peng, Ying Chen +1

Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained…

cs.DL2025

Integrating Large Citation Datasets

Inci Yueksel-Erguen, Ida Litzel, Hanqiu Peng

This paper explores methods for building a comprehensive citation graph using big data techniques to evaluate scientific impact more accurately. Traditional citation metrics have l…

quant-ph2025

Transformer-Based Neural Quantum Digital Twins for Many-Body Spectral Reconstruction and Adaptive Quantum-Annealing Schedule Design

Jianlong Lu, Hanqiu Peng, Hongrui Zhang +1

We introduce Transformer-based Neural Quantum Digital Twins (Tx-NQDTs) to reconstruct the low-energy spectral evolution of many-body quantum systems along quantum-annealing paths,…

math.OC2025

Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions

Ying Chen, Thorsten Koch, Hanqiu Peng +1

Quantum computing is poised to transform the financial industry, yet its advantages over traditional methods have not been evidenced. As this technology rapidly evolves, benchmarki…

cs.LG2021★ 1 cited

Deep Switching State Space Model (DSM) for Nonlinear Time Series Forecasting with Regime Switching

Xiuqin Xu, Hanqiu Peng, Ying Chen

Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors. These features present technical challenges in modeling, infer…