دسته بندی:

محاسبات کوانتومی - Quantum-Computing

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection

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ترجمه فارسی عنوان مقاله:

روش ترکیبی کوانتومی-کلاسیک برای تشخیص تقلب با انتخاب ویژگی کوانتومی

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منبع:

ieee - ieee Transactions on Quantum Engineering;2022;PP;99;10:1109/TQE:2022:3213474

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نویسنده:

None

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چکیده انگلیسی:

This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine’s feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum-based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.
INDEX TERMS: Fraud Detection | Quantum | Feature Selection | QSVM | Quantum Kernel Alignment

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قیمت: رایگان

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توضیحات اضافی:

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