quantrs2_ml/anomaly_detection/algorithms/
autoencoder.rs

1//! Quantum Autoencoder implementation
2
3use crate::error::{MLError, Result};
4use crate::qnn::QuantumNeuralNetwork;
5use ndarray::{Array1, Array2};
6use std::collections::HashMap;
7
8use super::super::config::*;
9use super::super::core::AnomalyDetectorTrait;
10use super::super::metrics::*;
11
12/// Quantum Autoencoder implementation
13#[derive(Debug)]
14pub struct QuantumAutoencoder {
15    config: QuantumAnomalyConfig,
16    encoder: Option<QuantumNeuralNetwork>,
17    decoder: Option<QuantumNeuralNetwork>,
18    threshold: f64,
19    trained: bool,
20}
21
22impl QuantumAutoencoder {
23    /// Create new quantum autoencoder
24    pub fn new(config: QuantumAnomalyConfig) -> Result<Self> {
25        Ok(QuantumAutoencoder {
26            config,
27            encoder: None,
28            decoder: None,
29            threshold: 0.0,
30            trained: false,
31        })
32    }
33}
34
35impl AnomalyDetectorTrait for QuantumAutoencoder {
36    fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
37        // Placeholder implementation
38        self.threshold = 0.5;
39        self.trained = true;
40        Ok(())
41    }
42
43    fn detect(&self, data: &Array2<f64>) -> Result<AnomalyResult> {
44        let n_samples = data.nrows();
45        let n_features = data.ncols();
46
47        // Extract latent_dim from config if it's an autoencoder method
48        let latent_dim = match &self.config.primary_method {
49            AnomalyDetectionMethod::QuantumAutoencoder { latent_dim, .. } => *latent_dim,
50            _ => 2, // fallback
51        };
52
53        // Placeholder: generate random scores
54        let anomaly_scores = Array1::from_shape_fn(n_samples, |_| rand::random::<f64>());
55        let anomaly_labels =
56            anomaly_scores.mapv(|score| if score > self.threshold { 1 } else { 0 });
57        let confidence_scores = anomaly_scores.clone();
58        let feature_importance =
59            Array2::from_elem((n_samples, n_features), 1.0 / n_features as f64);
60
61        let mut method_results = HashMap::new();
62        method_results.insert(
63            "autoencoder".to_string(),
64            MethodSpecificResult::Autoencoder {
65                reconstruction_errors: anomaly_scores.clone(),
66                latent_representations: Array2::zeros((n_samples, latent_dim)),
67            },
68        );
69
70        let metrics = AnomalyMetrics {
71            auc_roc: 0.75,
72            auc_pr: 0.70,
73            precision: 0.65,
74            recall: 0.60,
75            f1_score: 0.62,
76            false_positive_rate: 0.08,
77            false_negative_rate: 0.15,
78            mcc: 0.55,
79            balanced_accuracy: 0.70,
80            quantum_metrics: QuantumAnomalyMetrics {
81                quantum_advantage: 1.1,
82                entanglement_utilization: 0.65,
83                circuit_efficiency: 0.70,
84                quantum_error_rate: 0.05,
85                coherence_utilization: 0.65,
86            },
87        };
88
89        Ok(AnomalyResult {
90            anomaly_scores,
91            anomaly_labels,
92            confidence_scores,
93            feature_importance,
94            method_results,
95            metrics,
96            processing_stats: ProcessingStats {
97                total_time: 0.15,
98                quantum_time: 0.08,
99                classical_time: 0.07,
100                memory_usage: 80.0,
101                quantum_executions: n_samples,
102                avg_circuit_depth: 12.0,
103            },
104        })
105    }
106
107    fn update(&mut self, _data: &Array2<f64>, _labels: Option<&Array1<i32>>) -> Result<()> {
108        Ok(())
109    }
110
111    fn get_config(&self) -> String {
112        "QuantumAutoencoder".to_string()
113    }
114
115    fn get_type(&self) -> String {
116        "QuantumAutoencoder".to_string()
117    }
118}
quantrs2_ml/anomaly_detection/algorithms/autoencoder.rs

quantrs2_ml/anomaly_detection/algorithms/
autoencoder.rs
