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Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang, Danindu Gammanpilage, Xin Jin, Zeguan Wu, Junyu Liu
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Abstract
Abstract:Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two complementary experiments. First, we evaluate near-term quantum-kernel support vector machines (SVMs) on practical network-security classification tasks and compare them with classical SVM baselines on KDD Cup 1999, CICIDS2017, and BoT-IoT. Across these runs, quantum kernels are competitive. They can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in others. This suggests that near-term quantum-kernel methods should be evaluated as practical, dataset-dependent alternatives to classical kernels rather than as uniformly superior replacements. Second, we use quantum oracle sketching (QOS) to study a longer-term memory advantage for classification with streaming classical samples. In QOS, samples are processed online and used to incrementally construct an approximate quantum oracle, which provides coherent query access for downstream quantum algorithms without retaining the entire dataset. Under the QOS-inspired machine-size estimate, comparable accuracy corresponds to a substantially smaller effective memory-size proxy than explicit sparse/QRAM-style storage. Compared with a simple streaming proxy, the result is more nuanced because aggressive feature filtering can make the streaming dimension small. This suggests that the long-term value of quantum computing for network-security classification may lie in memory-efficient data access rather than immediate runtime speedup. Together, these experiments show how quantum computing may contribute to network-security classification from near-term classification performance and longer-term memory efficiency.
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Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency Yuqing Li1, Poonam Bala Nehru2, Yunpeng Zhang2, Danindu Gammanpilage2, Xin Jin1, Zeguan Wu1, and Junyu Liu1,∗ Affiliation: 1University of Pittsburgh, Pittsburgh, PA, USA Emails: yul658@pitt.edu, xij90@pitt.edu, zew79@pitt.edu, junyuliu@pitt.edu Affiliation: 2University of Houston, Houston, TX, USA Emails: pbalaneh@cougarnet.uh.edu, yzhan226@central.uh.edu, dgammanp@cougarnet.uh.edu Affiliation: ∗Corresponding author: Junyu Liu (junyuliu@pitt.edu) Abstract As an emerging technology, quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two complementary experiments. First, we evaluate near-term quantum-kernel support vector machines (SVMs) on practical network-security classification tasks and compare them with classical SVM baselines on KDD Cup 1999, CICIDS2017, and BoT-IoT. Across these runs, quantum kernels are competitive but task-dependent: they can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in others. This suggests that near-term quantum-kernel methods should be evaluated as practical, dataset-dependent alternatives to classical kernels rather than as uniformly superior replacements. Second, we use quantum oracle sketching (QOS) to study a longer-term memory advantage for classification with streaming classical samples. In QOS, samples are processed online and used to incrementally construct an approximate quantum oracle, which provides coherent query access for downstream quantum algorithms without retaining the entire dataset. Under the QOS-inspired machine-size estimate, comparable accuracy corresponds to a substantially smaller effective memory-size proxy than explicit sparse/QRAM-style storage. Compared with a simple streaming proxy, the result is more nuanced because aggressive feature filtering can make the streaming dimension very small. This suggests that the long-term value of quantum computing for network-security classification may lie in memory-efficient data access rather than immediate runtime speedup. Together, these experiments show how quantum computing may contribute to network-security classification from two perspectives: near-term classification performance and longer-term memory efficiency. Index Terms: quantum machine learning, network security, quantum SVM, quantum oracle sketching I Introduction Quantum computing has recently been applied to network-security classification. In this paper, we use network-security classification broadly to include supervised binary classification and one-class anomaly detection with binary decisions. Existing studies have reported high accuracy on intrusion detection, DDoS detection, anomaly detection, and attack-type classification tasks, suggesting that quantum and hybrid quantum-classical models have potential for this domain. Prior work has explored quantum-assisted intrusion detection with classical preprocessing and quantum simulation [1], quantum support vector machines and hybrid quantum-classical neural networks for DDoS detection [2], quantum neural networks for network anomaly detection on noisy quantum computers [3], and broader QML-based systems for intrusion classification evaluated on binary and multiclass security tasks [4, 5]. These results indicate that quantum models can be competitive for selected network-security classification problems, while also showing that performance depends strongly on the dataset, model architecture, and evaluation setting. However, most existing studies evaluate quantum methods mainly from the perspective of near-term classification accuracy: whether a specific quantum or hybrid model improves performance on a specific dataset. This motivates a broader framing of how quantum computing may contribute to network-security classification across different time scales. In the near term, quantum models should be compared with classical baselines under controlled datasets, preprocessing, and evaluation metrics. In the longer term, quantum methods may contribute not only through accuracy, but also through lower memory-size requirements for processing large-scale classical security data. This paper studies network-security classification from these two perspectives. In the near-term experiment, we use two binary SVM settings: one-class anomaly scoring for normal-versus-attack decisions, and supervised binary classification. A one-class anomaly method is binary at the decision-output level because a test sample is classified as either normal/benign or anomalous/attack, but it is not a supervised binary classifier at the training-objective level. We therefore treat it as a special case of binary network-security classification, which also keeps the experimental scope consistent with the binary classification setting considered in the QOS-inspired memory analysis. Unlike prior work that often proposes a specific quantum or hybrid IDS model and reports its accuracy, our near-term experiment focuses on a controlled kernel-level comparison. Quantum and classical SVMs use the same sampled data, preprocessing, and reduced angle features, differing mainly in the similarity function used by the SVM. This design isolates whether the quantum fidelity kernel itself provides a competitive classification signal when the data pipeline and SVM objective are kept fixed, rather than attributing performance differences to different preprocessing pipelines or model families. Specifically, we evaluate quantum-kernel support vector machines (SVMs) on binary network-security classification tasks derived from KDD Cup 1999, CICIDS2017, and BoT-IoT, and compare them with classical SVM baselines. This experiment tests whether quantum fidelity kernels are competitive with classical kernels when the classification setting is fixed. Our results show a dataset-dependent pattern: quantum kernels can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in others. This is consistent with prior network-security QML studies, which report strong quantum or hybrid performance on selected datasets but also show sensitivity to dataset characteristics, model design, and evaluation settings. Thus, the near-term value of quantum-kernel SVMs appears to be dataset-dependent rather than universally superior. The main near-term contribution is therefore not a claim of clear or statistically uniform accuracy advantage. Instead, it is a controlled comparison that shows where quantum kernels are competitive under the same SVM objective and preprocessing pipeline, and where classical kernels remain stronger. The first experiment addresses near-term classification performance. The second part is motivated by a longer-term result: quantum oracle sketching (QOS) [6] showed an exponential machine-size advantage for binary classification on massive classical data. We use this result as a lens for network-security classification. Instead of assuming that the full data matrix must be kept in memory, QOS processes classical samples one at a time and builds an approximate quantum oracle for later computation. Following this machine-size estimate, we evaluate where this memory-size advantage appears on network-security classification tasks by checking whether comparable accuracy can be maintained with a smaller effective memory-size proxy. Our results show that the QOS-based estimate is much smaller than explicit sparse-storage or QRAM-style storage at comparable accuracy levels. Compared with the classical streaming proxy, the advantage is more nuanced: streaming can be smaller after aggressive feature filtering, while QOS models compact coherent data access rather than only the dimension of a fitted streaming classifier. In summary, this paper organizes quantum computing for network-security classification around two parts. The first part evaluates quantum-kernel SVMs against classical SVM baselines on practical binary network-security tasks and uses the mixed results to characterize quantum kernels as task-dependent alternatives rather than universal replacements. The second part uses the QOS machine-size estimate to evaluate a longer-term memory advantage for analogous binary tasks. We study these parts on KDD Cup 1999, CICIDS2017, and BoT-IoT, using matched preprocessing and evaluation metrics where applicable. The detailed dataset setup, model construction, and evaluation protocol are described in the next section. I Experimental Design I-A Datasets and Binary Tasks We evaluate both experimental parts on three network-security datasets: KDD Cup 1999 [7], CICIDS2017 [8], and BoT-IoT [9]. The goal is not to cover every possible attack category, but to build controlled binary network-security tasks that can be used consistently across the near-term SVM comparison and the longer-term QOS memory analysis. For KDD Cup 1999 and CICIDS2017, we use a one-class anomaly setting. The model is trained only on normal or benign samples and evaluated on a test set containing both normal/benign and attack samples. Although this one-class setting is not trained as a standard supervised binary classifier, its output decision is binary: each test sample is labeled as normal/benign or anomalous/attack. Thus, it is binary at the decision-output level but not at the training-objective level. For BoT-IoT, the dataset subset used in our experiments contains too few normal-flow samples for stable one-class training. We therefore use a supervised DoS-versus-DDoS binary task and report it separately from the one-class anomaly tasks. Fig. 1 summarizes the full experimental workflow. The three datasets are first converted into binary network-security classification tasks. The left branch evaluates near-term detection and classification performance by comparing classical RBF-kernel SVMs with quantum fidelity-kernel SVMs under the same samples, preprocessing, and angle features. The right branch evaluates longer-term resource efficiency by comparing classical streaming/sparse machine-size estimates with the QOS-inspired quantum machine-size estimate at comparable accuracy. Fig. 1: Overall workflow for the two experiments. The near-term branch compares classical and quantum SVM kernels for network-security detection and classification performance, while the longer-term branch evaluates QOS-inspired resource efficiency through effective machine-size estimates. I-B Near-Term SVM Comparison In the near-term experiment, we compare quantum-kernel SVMs with classical SVM baselines using the same sampled rows, the same train/test split, and the same low-dimensional angle features. The preprocessing pipeline is fit only on the training data and then applied to the test data. For KDD Cup 1999 and CICIDS2017, the training set contains only normal or benign samples, while the test set contains both normal/benign and attack samples. The one-class SVM therefore learns the normal region and assigns an anomaly score to each test sample. For BoT-IoT, because the dataset subset used in our experiments contains too few normal flows for stable one-class training, we use a supervised DoS-versus-DDoS task with balanced samples from the two attack classes. After sampling, we remove label and identifier columns, one-hot encode categorical variables, and standardize numerical variables using the training set. The preprocessing and SVM baselines use standard scikit-learn routines [10]. This gives a sparse numerical feature matrix for each dataset. We then use TruncatedSVD [11] to map the encoded feature matrix to a shared d-dimensional feature space, where d is set equal to the number of qubits used by the quantum kernel. Specifically, TruncatedSVD is fit on the encoded training matrix and applied to both train and test matrices: Ztrain=XtrainVd,Ztest=XtestVd,Z_train=X_trainV_d, Z_test=X_testV_d, (1) where d=nqubitsd=n_qubits. The SVD coordinates are standardized using the training set statistics. They are then scaled to angle features in [0,π][0,π]: Aij=π⋅Zij−minj(Ztrain)maxj(Ztrain)−minj(Ztrain).A_ij=π· Z_ij- _j(Z_train) _j(Z_train)- _j(Z_train). (2) The same fitted scaler is applied to the test set, and test angles are clipped to [0,π][0,π]. In the main runs, KDD Cup 1999 and CICIDS2017 use d=12d=12, while BoT-IoT uses d=10d=10. Both classical and quantum SVMs use these same angle-scaled SVD features as input. The classical baseline applies an RBF kernel to the angle features. The quantum model instead encodes each angle vector a into a quantum feature state |ϕ(a)⟩|φ(a) and uses the fidelity kernel [12, 13] K(a,b)=|⟨ϕ(a)|ϕ(b)⟩|2.K(a,b)=| φ(a)|φ(b) |^2. (3) In the main experiment, we use the entangled feature map implemented in the code, which applies single-qubit RyR_y rotations followed by nearest-neighbor phase interactions. The resulting kernel matrix is computed by exact statevector simulation in row blocks to control memory. For KDD Cup 1999 and CICIDS2017, both the classical and quantum models use the one-class SVM objective [14]. For BoT-IoT, both use the supervised SVC objective [15]. Thus, within each dataset, the main difference between the classical and quantum models is the kernel function rather than the sampled data, preprocessing, or SVM objective. Because KDD Cup 1999/CICIDS2017 and BoT-IoT use different training objectives, the comparisons should be interpreted within each dataset and objective setting rather than as direct cross-dataset comparisons of identical learning problems. The main comparison reported in Table I uses eight random seeds. We report ROC-AUC, average precision (AP), and best F1 as mean ± standard deviation across these seeds. Best F1 is computed by sweeping the decision threshold on the evaluation scores; it should therefore be interpreted as a threshold-optimized summary rather than as a fixed-threshold deployment metric. I-C Longer-Term QOS Memory-Advantage Analysis The second experiment studies a longer-term memory advantage motivated by quantum oracle sketching (QOS) [6]. The QOS paper considers large-scale binary classification on massive classical data and shows that, under sparsity and conditioning assumptions, a small quantum machine can achieve the same prediction task with exponentially smaller machine size than classical machines. Here, we use machine size as an effective memory-size proxy rather than as a runtime measure. In particular, for a training matrix X∈ℝN×DX ^N× D and binary labels y∈+1,−1Ny∈\+1,-1\^N, the classification rule is based on the ridge or LS-SVM weight vector w=(X⊤X+λI)−1X⊤y,w=(X X+λ I)^-1X y, (4) and a test vector x′x is classified by y^=sign(x′⊤w). y=sign(x w). (5) The key idea is not that the full dataset is stored in a quantum state. Rather, QOS treats the data as samples observed one at a time. These samples are used to construct an approximate quantum oracle for data access, which can be called by downstream quantum algorithms. Under the assumptions of the QOS framework, this enables binary classification with quantum machine size poly(logD)poly( D), while classical machines below a much larger size may require super-polynomially more samples. In this sense, QOS changes the memory target from storing the full data matrix to building a much smaller machine that can still maintain the classification rule. To connect this idea to network-security classification, we follow the machine-size advantage perspective of QOS. For each dataset, we vary a feature filtering threshold, denoted min_dfmin\_df, which removes features that appear in too few samples. This produces a sequence of feature matrices Xdf∈ℝN×DdfX_df ^N× D_df with different effective dimensions and sparsity levels. For each matrix, we train a ridge classifier and record the classification accuracy. For the same feature matrix, we then estimate three machine sizes: Sstream S_stream =Ddf, =D_df, (6) Ssparse S_sparse =nnz(Xdf), =nnz(X_df), (7) SQOS S_QOS =2⌈log2(N+2Ddf)⌉+⌈log2(s+1)⌉+1, =2 _2(N+2D_df) + _2(s+1) +1, (8) where nnz(Xdf)nnz(X_df) is the number of nonzero entries and s is the maximum row or column sparsity. The first estimate represents a classical streaming baseline, the second represents explicit sparse storage or QRAM-style storage, and the third is the QOS-inspired quantum machine-size estimate used in our experiment. This experiment is therefore an estimate-based memory-size analysis, not a runtime or hardware speedup claim. Its purpose is to separate two comparisons: QOS versus explicit sparse/QRAM-style storage, where the QOS-inspired estimate is much smaller, and QOS versus a simple streaming proxy, where the result depends on how aggressively the feature space is filtered. I Near-Term SVM Results TABLE I: Main binary SVM results. Values are mean ± standard deviation across eight random seeds. Dataset Model ROC-AUC Average precision Best F1 KDD Cup 1999 Classical RBF OCSVM 0.9282±0.01050.9282± 0.0105 0.9460±0.00860.9460± 0.0086 0.9092±0.02070.9092± 0.0207 KDD Cup 1999 Quantum OCSVM 0.8972±0.03030.8972± 0.0303 0.9288±0.01950.9288± 0.0195 0.8891±0.02300.8891± 0.0230 CICIDS2017 Classical RBF OCSVM 0.6209±0.00730.6209± 0.0073 0.6975±0.00690.6975± 0.0069 0.6789±0.00120.6789± 0.0012 CICIDS2017 Quantum OCSVM 0.6666±0.03160.6666± 0.0316 0.7034±0.02740.7034± 0.0274 0.7003±0.00600.7003± 0.0060 BoT-IoT Classical RBF SVC 0.9985±0.00020.9985± 0.0002 0.9985±0.00030.9985± 0.0003 0.9887±0.00210.9887± 0.0021 BoT-IoT Quantum SVC 0.9977±0.00070.9977± 0.0007 0.9977±0.00050.9977± 0.0005 0.9955±0.00050.9955± 0.0005 Table I shows that the near-term result is mixed and dataset-dependent. On KDD Cup 1999, the classical RBF OCSVM is stronger on all three metrics, indicating that the RBF geometry is well matched to this low-dimensional feature space. On CICIDS2017, the quantum OCSVM improves ROC-AUC, AP, and best F1, suggesting that the quantum fidelity kernel captures a useful similarity structure for BENIGN-versus-attack classification. On BoT-IoT, both models are near saturation. The classical RBF SVC has slightly higher ROC-AUC and AP, while the quantum SVC has consistently higher best F1. This variation is likely related to the geometry of each task after the shared SVD-to-angle preprocessing. On KDD Cup 1999, the reduced normal-versus-attack space appears well matched to the distance-based RBF OCSVM, while the periodic angle encoding used by the quantum kernel may distort this radial structure. On CICIDS2017, benign and attack flows are more heterogeneous, so the nonlinear interactions introduced by the fidelity kernel can provide a better ranking signal. On BoT-IoT, both kernels are near saturation for the supervised DoS-versus-DDoS task; the higher quantum best F1 mainly reflects a more favorable threshold on this balanced test set rather than a broad ranking advantage. Overall, the table supports controlled comparability and dataset-dependent behavior, not a uniform near-term quantum advantage. Fig. 2: ROC-AUC ablation across qubit counts and kernel choices. Error bars show standard deviation across three seeds. We also test whether the comparison depends strongly on the classical kernel or qubit count. For computational tractability, this auxiliary ablation uses three seeds and smaller samples: 50k normal or benign training samples for KDD/CICIDS2017 and 25k samples per class for BoT-IoT. We compare quantum kernels with RBF, linear, polynomial, and sigmoid SVM kernels across 6, 8, 10, and 12 qubits. Fig. 2 shows the ROC-AUC trend. The corresponding AP and best-F1 ablations show the same main pattern summarized below. RBF is the strongest classical baseline among the tested classical kernels, so the main comparison is not against a weak baseline. Increasing the number of qubits is not monotonic: useful dimension depends on dataset and metric. In this smaller-sample ablation, quantum kernels are strongest for BoT-IoT best F1 and sometimes achieve the strongest KDD ROC-AUC/AP, even though the full main experiment in Table I favors the RBF OCSVM on KDD. This difference suggests sensitivity to sampling scale and reinforces the dataset- and setting-dependent nature of the comparison. These results support a claim of controlled comparability rather than universal quantum advantage. IV Oracle Sketching and Memory Efficiency The near-term SVM experiment evaluates classification performance in our simulation setting. The oracle-sketching experiment complements it with a memory analysis: for the same datasets and corresponding binary labels, it compares the effective memory-size proxies associated with classical storage baselines and with the QOS-inspired compact-oracle estimate at comparable accuracy. Following the feature-truncation strategy used in QOS, we vary a minimum feature-frequency threshold, denoted min_dfmin\_df. A feature is kept only if it is present in at least min_dfmin\_df samples; increasing min_dfmin\_df removes rarer features and reduces the effective feature dimension. For each threshold, we train a ridge classifier and record the resulting accuracy. We then compare three machine-size estimates. The classical streaming size is the feature dimension required by a streaming baseline. The classical sparse/QRAM-style size counts explicit sparse storage. The quantum oracle sketching size is the compact-oracle estimate, which scales logarithmically with dimensions and sparsity in our implementation. Fig. 3: Machine size versus accuracy for the quantum oracle sketching experiment. Because the accuracies are near-saturated for some datasets, the x-axis is zoomed to the observed accuracy range for readability. Fig. 3 shows the memory-oriented comparison. In the figure, red denotes the QOS estimate, blue denotes the classical streaming proxy, and gray denotes explicit sparse/QRAM-style storage. Across all three datasets, comparable accuracy is associated with a much smaller effective memory-size proxy than explicit sparse/QRAM-style storage. The comparison with the streaming proxy is not uniformly in favor of QOS, because the streaming estimate only counts the reduced feature dimension and can become very small when rare features are aggressively removed. This result should not be interpreted as a runtime speedup for the current simulator, and it does not model the engineering cost of building a real quantum data-access mechanism. Instead, it highlights a more specific longer-term point: even when near-term quantum kernels are only comparable to classical kernels, compact quantum data access may still reduce the memory burden relative to explicit data storage for large network-security classification tasks. V Discussion The two experiments address network-security classification from two time scales. The SVM experiment evaluates whether quantum kernels can serve as alternatives to classical kernels under the same preprocessing pipeline and SVM objectives. The oracle-sketching experiment evaluates a longer-term memory-size perspective, especially the gap between compact quantum-oracle estimates and explicit sparse/QRAM-style storage. Interpreted together, they suggest that quantum computing may support network-security classification in more than one way: as task-dependent near-term kernel alternatives for classification tasks, and as memory-efficient data-access mechanisms for future large-scale classification tasks. These two experiments should not be interpreted as a single end-to-end quantum network-security system. The near-term experiment evaluates kernel-level classification performance under simulation, whereas the QOS experiment provides an estimate-based memory-size analysis motivated by future quantum data-access models. Their common role is to identify two possible contribution channels for quantum computing, not to demonstrate a unified deployed quantum IDS pipeline. VI Conclusion This paper studies how quantum computing may contribute to network-security classification along two time scales: near-term classification performance and longer-term memory efficiency. In the near term, quantum kernel SVMs are competitive but task-dependent: they improve CICIDS2017 metrics, improve BoT-IoT best F1, and underperform the RBF baseline on KDD. In the longer term, quantum oracle sketching suggests a separate memory advantage relative to explicit sparse/QRAM-style storage, while the comparison with a simple streaming proxy depends on feature filtering. The central conclusion is therefore measured but optimistic: quantum computing may support future network-security classification both through near-term kernel alternatives for classification tasks and through longer-term memory-efficient data access for large-scale classification. Acknowledgement YL, XJ, ZW, and JL are supported in part by the University of Pittsburgh, School of Computing and Information, Department of Computer Science, Pitt Cyber, Pitt Momentum fund, PQI Community Collaboration Awards, John C. Mascaro Faculty Scholar in Sustainability, Switzerland NSF award 2000-1-243053, NSF award 2535915, 2610010, DOE Genesis Program, Thinking Machines Lab and Cisco Research. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. References [1] A. Gouveia and M. Correia (2020) Towards quantum-enhanced machine learning for network intrusion detection. In 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), p. 1–8. External Links: Document Cited by: §I. [2] E. D. Payares and J. C. Martínez-Santos (2021) Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview. In Quantum Computing, Communication, and Simulation, Vol. 11699, p. 116990B. External Links: Document Cited by: §I. [3] A. Kukliansky, M. Orescanin, C. Bollmann, and T. Huffmire (2024) Network anomaly detection using quantum neural networks on noisy quantum computers. IEEE Transactions on Quantum Engineering 5, p. 1–11. External Links: Document Cited by: §I. [4] D. M. d. Abreu, C. E. Rothenberg, and A. J. G. Abelém (2024) QML-IDS: quantum machine learning intrusion detection system. In 2024 IEEE Symposium on Computers and Communications (ISCC), p. 1–6. External Links: Document Cited by: §I. [5] D. M. d. Abreu, D. Moura, C. E. Rothenberg, and A. J. G. Abelém (2025) QuantumNetSec: quantum machine learning for network security. International Journal of Network Management 35 (4), p. e70018. External Links: Document Cited by: §I. [6] H. Zhao, A. Zlokapa, H. Neven, R. Babbush, J. Preskill, J. R. McClean, and H. Huang (2026) Exponential quantum advantage in processing massive classical data. External Links: 2604.07639, Document, Link Cited by: §I, §I-C. [7] S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan (1999) KDD Cup 1999 Data. UCI Machine Learning Repository. Note: Dataset External Links: Document, Link Cited by: §I-A. [8] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy, p. 108–116. External Links: Document Cited by: §I-A. [9] N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull (2019) Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. Future Generation Computer Systems 100, p. 779–796. External Links: Document Cited by: §I-A. [10] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay (2011) Scikit-learn: machine learning in python. Journal of Machine Learning Research 12, p. 2825–2830. External Links: Link Cited by: §I-B. [11] N. Halko, P. Martinsson, and J. A. Tropp (2011) Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review 53 (2), p. 217–288. External Links: Document Cited by: §I-B. [12] V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567 (7747), p. 209–212. External Links: Document Cited by: §I-B. [13] M. Schuld and N. Killoran (2019) Quantum machine learning in feature hilbert spaces. Physical Review Letters 122 (4), p. 040504. External Links: Document Cited by: §I-B. [14] B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson (2001) Estimating the support of a high-dimensional distribution. Neural Computation 13 (7), p. 1443–1471. External Links: Document Cited by: §I-B. [15] C. Cortes and V. Vapnik (1995) Support-vector networks. Machine Learning 20 (3), p. 273–297. External Links: Document Cited by: §I-B.