论文标题
无监督的量子机器学习欺诈检测
Unsupervised quantum machine learning for fraud detection
论文作者
论文摘要
我们开发用于异常检测的量子协议，并将其应用于信用卡欺诈检测的任务（FD）。首先，我们基于受监督和无监督的机器学习方法建立经典基准，其中平均精度被选择作为检测异常数据的可靠度量。我们专注于基于内核的方法，以易于直接比较，将我们的无监督建模基于单级支持向量机（OC-SVM）。接下来，我们采用不同类型的量子内核来执行异常检测，并观察到量子FD可以以增加功能数量（等于数据嵌入的量子数）来挑战等效的经典协议。用寄存器进行多达20 QUAT的寄存器进行模拟，我们发现具有重新上传的量子内核表现出更好的平均精度，而随着系统尺寸的优势，优势提高了。具体而言，在20 QUAT的情况下，我们达到平均精度等于15％的量子古典分离。我们使用近期和中期量子硬件讨论欺诈检测的前景，并描述可能的未来改进。
We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and unsupervised machine learning methods, where average precision is chosen as a robust metric for detecting anomalous data. We focus on kernel-based approaches for ease of direct comparison, basing our unsupervised modelling on one-class support vector machines (OC-SVM). Next, we employ quantum kernels of different type for performing anomaly detection, and observe that quantum FD can challenge equivalent classical protocols at increasing number of features (equal to the number of qubits for data embedding). Performing simulations with registers up to 20 qubits, we find that quantum kernels with re-uploading demonstrate better average precision, with the advantage increasing with system size. Specifically, at 20 qubits we reach the quantum-classical separation of average precision being equal to 15%. We discuss the prospects of fraud detection with near- and mid-term quantum hardware, and describe possible future improvements.
