aqora

Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study

Chi-Sheng Chen, Xinyu Zhang, En-Jui Kuo +3 more

Jan 1, 20260 citations

Quantum-Inspired Private Equity and Venture Capital Fund Optimization

Mourad Mazouni

Jan 1, 20250 citations

HYBRID QUANTUM-CLASSICAL NEURAL NETWORK FOR SME CREDIT RISK ASSESSMENT UNDER LIMITED TRAINING CONDITIONS

Oleksandr Shmatko, Iryna Mykhailenko, Игорь Михайлович Годлевский

In modern financial information systems, the growing complexity of credit risk assessment for small and medium-sized enterprises (SMEs) creates increasing demands for highly accurate, scalable, and computationally efficient predictive models.Traditional credit scoring approaches based on classical statistical methods and conventional neural networks often demonstrate limited performance when processing nonlinear financial dependencies, noisy datasets, and highdimensional feature spaces.These limitations become especially significant under restricted training conditions, where the amount of labeled financial data is limited and rapid model convergence is required for practical deployment in banking and risk management environments.In such conditions, hybrid quantum-classical machine learning has emerged as a promising direction that combines the representational capabilities of classical neural networks with the computational potential of quantum variational circuits.This paper proposes a hybrid quantum-classical neural network architecture designed for SME credit risk assessment under limited training conditions.The proposed model integrates classical dense layers with a variational quantum circuit that performs nonlinear feature transformation through quantum embedding, parameterized quantum gates, and entanglement operations.Particular attention is devoted to efficient data encoding and optimization of variational circuit depth in order to reduce training complexity while preserving predictive stability.In modern financial information systems, the growing complexity of credit risk assessment for small and medium-sized enterprises (SMEs) creates increasing demands for highly accurate, scalable, and computationally efficient predictive models.Traditional credit scoring approaches based on classical statistical methods and conventional neural networks often demonstrate limited performance when processing nonlinear financial dependencies, noisy datasets, and highdimensional feature spaces.These limitations become especially significant under restricted training conditions, where the amount of labeled financial data is limited and rapid model convergence is required for practical deployment in banking and risk management environments.In such conditions, hybrid quantum-classical machine learning has emerged as a promising direction that combines the representational capabilities of classical neural networks with the computational potential of quantum variational circuits.This paper proposes a hybrid quantum-classical neural network architecture designed for SME credit risk assessment under limited training conditions.The proposed model integrates classical dense layers with a variational quantum circuit that performs nonlinear feature transformation through quantum embedding, parameterized quantum gates, and entanglement operations.Particular attention is devoted to efficient data encoding and optimization of variational circuit depth in order to reduce training complexity while preserving predictive stability.

Наука і техніка сьогодніApr 30, 20260 citations

A Quantum Neural Network Approach for Portfolio Selection

Devi Ahilya

Jan 1, 20110 citations

Hybrid Quantum-Classical Transfer Learning Model for Credit Risk Assessment with Reduced Quantum Overhead and Enhanced Minority Class Prediction

Parvathy Gopakumar, Rubell Marion Lincy G, Salvatore Sinno +2 more

We propose an efficient application of the hybrid quantum-classical transfer learning model to improve the performance of credit risk classification. The Quantum Transfer Learning (QTL) model presented in this study consists of a frozen classical feature extractor and a quantum refinement layer which is trained on the features extracted from the real-world credit risk dataset. For baseline comparison, we evaluated a standalone Quantum Neural Network (QNN) that was trained directly on the credit risk dataset. Our experimental evaluations on a real-world normalized credit risk dataset reveal that the proposed QTL model significantly outperforms the standalone QNN and achieves results on par with the classical neural network.We propose an efficient application of the hybrid quantum-classical transfer learning model to improve the performance of credit risk classification. The Quantum Transfer Learning (QTL) model presented in this study consists of a frozen classical feature extractor and a quantum refinement layer which is trained on the features extracted from the real-world credit risk dataset. For baseline comparison, we evaluated a standalone Quantum Neural Network (QNN) that was trained directly on the credit risk dataset. Our experimental evaluations on a real-world normalized credit risk dataset reveal that the proposed QTL model significantly outperforms the standalone QNN and achieves results on par with the classical neural network.

IEEE International Conference on Quantum Computing and Engineering (QCE)Aug 30, 20250 citations

Hybrid Quantum Deep Learning for Financial Market Trend Prediction

Rajat Kumar Dwibedi, L. K. Hema, Dhinil V Dhinesh +3 more

International Conference on IoT, Communication and Automation Technology (ICICAT)Dec 5, 20250 citations

The Equity Fund Risk Predictions Via Quantum-Classical Hybrid Neural Networks

Related papers

The Equity Fund Risk Predictions Via Quantum-Classical Hybrid Neural Networks

Related papers