Quantum Machine Learning for Credit Scoring

TL;DR

This paper investigates hybrid quantum-classical machine learning models for credit scoring of SMEs, demonstrating more efficient training but challenges with accuracy as quantum complexity increases.

Contribution

It presents the first in-depth exploration of hybrid quantum machine learning for credit scoring, highlighting training efficiency and practical implementation issues.

Findings

Quantum models train faster than classical models.

Accuracy degrades with more than 12 qubits.

Practical execution issues on simulators and real quantum hardware.

Abstract

In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium-sized enterprises (SME). A quantum/classical hybrid approach has been used with several models, activation functions, epochs and other parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models with the quantum model trained for 350 epochs compared to 3500 epochs for comparable prediction performance. Surprisingly, a degradation in the accuracy was observed as the number of qubits was increased beyond 12 qubits and also with the addition of extra classifier blocks in the quantum model. Practical issues for executing on simulators and real quantum computers are also explored. Overall, we see great promise in this first in-depth exploration of the use of hybrid QML in credit scoring.