Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
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| Veröffentlicht in: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings vol. 01 (2023) |
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| 024 | 7 | |a 10.1109/QCE57702.2023.00075 |2 doi | |
| 035 | |a 2896018808 | ||
| 045 | 2 | |b d20230101 |b d20231231 | |
| 084 | |a 228229 |2 nlm | ||
| 100 | 1 | |a Kilian Tscharke |u Institute for Applied and Integrated Security,Quantum Security Technologies Fraunhofer,Garching near Munich,Germany | |
| 245 | 1 | |a Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2023 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Conference Title: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE)Conference Start Date: 2023, Sept. 17 Conference End Date: 2023, Sept. 22 Conference Location: Bellevue, WA, USAAnomaly detection, an important task in data analysis, involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden patterns and deviations in large-scale data. The potential of quantum computing for machine learning has been widely recognized, leading to extensive research efforts to develop suitable quantum machine learning (QML) algorithms. In particular, the search for QML algorithms for near-term NISQ devices is in full swing. However, NISQ devices pose additional challenges due to their limited qubit coherence times, low number of qubits, and high error rates. Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints. Especially support vector machines (SVM) utilizing quantum kernel estimation have shown success in various supervised learning tasks. However, in the context of anomaly detection, semisupervised learning is of great relevance, and yet there is limited research published in this area. This paper introduces an approach to semisupervised anomaly detection based on the reconstruction loss of a support vector regression (SVR) with quantum kernel. This novel model is an alternative to the variational quantum and quantum kernel one-class classifiers, and is compared to a quantum autoencoder as quantum baseline and a SVR with radial-basis-function (RBF) kernel as well as a classical autoencoder as classical baselines. The models are benchmarked extensively on 10 real-world anomaly detection data sets and one toy data set and it is shown that our SVR model with quantum kernel performs better than the SVR with RBF kernel as well as all other models, achieving highest mean AUC over all data sets. In addition, our QSVR outperforms the Quantum autoencoder on 9 out of 11 data sets. | |
| 653 | |a Data analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Quantum computing | ||
| 653 | |a Datasets | ||
| 653 | |a Radial basis function | ||
| 653 | |a Support vector machines | ||
| 653 | |a Supervised learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Semi-supervised learning | ||
| 653 | |a Anomalies | ||
| 653 | |a Kernel functions | ||
| 653 | |a Cognitive tasks | ||
| 653 | |a Qubits (quantum computing) | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Issel, Sebastian |u Institute for Applied and Integrated Security,Quantum Security Technologies Fraunhofer,Garching near Munich,Germany | |
| 700 | 1 | |a Debus, Pascal |u Institute for Applied and Integrated Security,Quantum Security Technologies Fraunhofer,Garching near Munich,Germany | |
| 773 | 0 | |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings |g vol. 01 (2023) | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2896018808/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |