Quantum Computing: Computational Excellence for Society 5.0

Griffin, Paul R.; Boguslavsky, Michael; Huang, Junye; Kauffman, Robert J.; Tan, Brian Tiong Gee

doi:10.1201/9781003132080-1

Cited by 2 publications

(1 citation statement)

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“…In the book chapter co-authored by M. Boguslavsky, P. Griffin et al [36], the authors introduce a new framework for addressing business problems with quantum computing, assessing classes of problems that could benefit, and showing a use case for QML algorithms. The authors outline two frameworks for quantum neural networks: (i) a 2-qubit perceptron inspired by the Entropica Labs algorithm for the classification of cancerous cells, and (ii) a hybrid neural networks where it is suggested to establish an interface between classical and quantum neural networks using PYTORCH (v2.3.0) and Qiskit (v1.0.2) [37].…”

Section: Quantum Modelsmentioning

confidence: 99%

Quantum Machine Learning for Credit Scoring

Schetakis,

Aghamalyan,

Boguslavsky

et al. 2024

Mathematics

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This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.

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Section: Quantum Modelsmentioning

confidence: 99%