In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium size businesses (SMEs). A quantum/classical hybrid approach has been used for two years of experimentation with several models, activation functions, epochs, 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 for comparable prediction performance. Practical issues are also explored including a quadratic computational slow down with the number of qubits and a linear slow down with the number of blocks of classifiers using classical simulators. Running the models on real quantum computers is discussed including the number of times a circuit has to be executed. 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. Overall, we see great promise in this first in-depth exploration of the use of hybrid QML in credit scoring.

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