Anomaly Detection using Quantum Circuit Born Machines (en)

Anomaly Detection using Quantum Circuit Born Machines

Abstract:

Anomaly detection is a critical component in various fields, including finance, medical diagnosis, and fraud detection. As datasets become increasingly complex and larger, traditional computers face limitations in processing power. In contrast, quantum computers oer promising solutions through the physical properties of their qubits, such as entanglement and superposition. The emergence of quantum machine learning, particularly the quantum circuit born machines (QCBMs), is introduced as a promising approach to tackle such complex problems. QCBMs are parameterized quantum circuits that can be trained to generate samples from a target distribution. The goal of this work is to leverage this ability for detecting anomalies that have a distribution dierent from that of normal data points. The effectiveness of QCBMs for anomaly detection is explored using a dataset generated by the make_blobs method from the Scikit-learn package in Python, where some outliers can be clearly distinguished from the clusters. And its performance is compared with an autoencoder model using the ROC-curve and the Matthews correlation coecient (MCC). These metrics are used to evaluate the models’ ability to detect anomalies and avoid false positives. The results show that QCBMs outperform the autoencoder when trained with a smaller dataset, indicating that QCBMs are more eective in dealing with data and can learn the underlying distribution more eciently than the autoencoder. However, both models can learn the distribution when trained with the full dataset.

Author:

Ahmad Almohamad Alissa

Advisors:

Jonas Stein, Danielle Schumann, Claudia Linnhoff-Popien

Student Thesis | Published April 2023 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors