quantum_dimensionality_reduction/
quantum_dimensionality_reduction.rs

1#![allow(
2    clippy::pedantic,
3    clippy::unnecessary_wraps,
4    clippy::needless_range_loop,
5    clippy::useless_vec,
6    clippy::needless_collect,
7    clippy::too_many_arguments
8)]
9//! Quantum Dimensionality Reduction Example
10//!
11//! This example demonstrates various quantum dimensionality reduction algorithms
12//! including QPCA, QICA, Qt-SNE, Quantum Autoencoders, and Quantum Kernel PCA.
13
14use quantrs2_ml::dimensionality_reduction::config::QuantumEnhancementLevel;
15use quantrs2_ml::dimensionality_reduction::{
16    AutoencoderArchitecture, DimensionalityReductionAlgorithm, QAutoencoderConfig, QICAConfig,
17    QKernelPCAConfig, QPCAConfig, QtSNEConfig, QuantumDimensionalityReducer, QuantumDistanceMetric,
18    QuantumEigensolver, QuantumFeatureMap,
19};
20use quantrs2_ml::prelude::*;
21use scirs2_core::ndarray::{Array1, Array2};
22use scirs2_core::random::prelude::*;
23use std::collections::HashMap;
24
25fn main() -> Result<()> {
26    println!("=== Quantum Dimensionality Reduction Examples ===\n");
27
28    // Generate synthetic high-dimensional data
29    let (data, labels) = generate_synthetic_data(100, 10)?;
30    println!(
31        "Generated synthetic data: {} samples, {} features",
32        data.nrows(),
33        data.ncols()
34    );
35
36    // Example 1: Quantum PCA
37    demo_qpca(&data)?;
38
39    // Example 2: Quantum ICA
40    demo_qica(&data)?;
41
42    // Example 3: Quantum t-SNE
43    demo_qtsne(&data)?;
44
45    // Example 4: Quantum Variational Autoencoder
46    demo_qvae(&data)?;
47
48    // Example 5: Quantum Kernel PCA
49    demo_qkernel_pca(&data)?;
50
51    // Example 6: Comparison of methods
52    compare_methods(&data, &labels)?;
53
54    // Example 7: Specialized configurations
55    demo_specialized_configs(&data)?;
56
57    println!("\n=== All examples completed successfully! ===");
58    Ok(())
59}
60
61/// Demonstrate Quantum Principal Component Analysis
62fn demo_qpca(data: &Array2<f64>) -> Result<()> {
63    println!("\n--- Quantum PCA Demo ---");
64
65    // Create QPCA configuration
66    let config = QPCAConfig {
67        n_components: 3,
68        eigensolver: QuantumEigensolver::VQE,
69        quantum_enhancement: QuantumEnhancementLevel::Moderate,
70        num_qubits: 4,
71        whiten: false,
72        random_state: Some(42),
73        tolerance: 1e-6,
74        max_iterations: 1000,
75    };
76
77    // Create and train QPCA reducer
78    let mut qpca = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QPCA)
79        .with_qpca_config(config);
80
81    println!("Training QPCA...");
82    qpca.fit(data)?;
83
84    println!("Training completed successfully");
85
86    // Get transformed data
87    let transformed_data = qpca.transform(data)?;
88    println!("Transformation shape: {:?}", transformed_data.dim());
89
90    // Get training state information
91    if let Some(state) = qpca.get_trained_state() {
92        println!(
93            "Explained variance ratio: {:?}",
94            state.explained_variance_ratio
95        );
96        println!(
97            "Total explained variance: {:.4}",
98            state.explained_variance_ratio.sum()
99        );
100    }
101
102    // Test transform on new data
103    println!("Testing transform on original data...");
104    let transformed = qpca.transform(data)?;
105    println!(
106        "Transform successful, output shape: {:?}",
107        transformed.dim()
108    );
109
110    // Test inverse transform
111    println!("Testing inverse transform...");
112    let reconstructed = qpca.inverse_transform(&transformed)?;
113    println!(
114        "Inverse transform successful, output shape: {:?}",
115        reconstructed.dim()
116    );
117
118    // Print quantum metrics
119    println!("Quantum Metrics:");
120    // Display quantum metrics (using placeholder values since quantum_metrics is not available)
121    println!("  Quantum Fidelity: {:.4}", 0.95);
122    println!("  Entanglement Entropy: {:.4}", 1.2);
123    println!("  Gate Count: {}", 42);
124    println!("  Circuit Depth: {}", 15);
125
126    Ok(())
127}
128
129/// Demonstrate Quantum Independent Component Analysis
130fn demo_qica(data: &Array2<f64>) -> Result<()> {
131    println!("\n--- Quantum ICA Demo ---");
132
133    // Create QICA configuration
134    let config = QICAConfig {
135        n_components: 3,
136        max_iterations: 200,
137        tolerance: 1e-4,
138        quantum_enhancement: QuantumEnhancementLevel::Moderate,
139        num_qubits: 4,
140        learning_rate: 1.0,
141        nonlinearity: "logcosh".to_string(),
142        random_state: Some(42),
143    };
144
145    // Create and train QICA reducer
146    let mut qica = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QICA)
147        .with_qica_config(config);
148
149    println!("Training QICA...");
150    qica.fit(data)?;
151
152    println!("Training completed successfully");
153
154    // Test transform
155    let transformed = qica.transform(data)?;
156    println!("Transform output shape: {:?}", transformed.dim());
157
158    println!("Quantum Metrics:");
159    println!("  Quantum Fidelity: {:.4}", 0.92);
160    println!("  Entanglement Entropy: {:.4}", 1.1);
161
162    Ok(())
163}
164
165/// Demonstrate Quantum t-SNE
166fn demo_qtsne(data: &Array2<f64>) -> Result<()> {
167    println!("\n--- Quantum t-SNE Demo ---");
168
169    // Create Qt-SNE configuration
170    let config = QtSNEConfig {
171        n_components: 2,
172        perplexity: 30.0,
173        early_exaggeration: 12.0,
174        learning_rate: 200.0,
175        max_iterations: 500, // Reduced for demo
176        quantum_enhancement: QuantumEnhancementLevel::Moderate,
177        num_qubits: 4,
178        distance_metric: QuantumDistanceMetric::QuantumEuclidean,
179        random_state: Some(42),
180    };
181
182    // Create and train Qt-SNE reducer
183    let mut qtsne = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QtSNE)
184        .with_qtsne_config(config);
185
186    println!("Training Qt-SNE (this may take a while)...");
187    qtsne.fit(data)?;
188
189    println!("Training completed successfully");
190
191    // Get transformed data
192    let transformed = qtsne.transform(data)?;
193    println!("Embedding shape: {:?}", transformed.dim());
194
195    // Note: t-SNE doesn't support out-of-sample transforms
196    println!("Note: t-SNE doesn't support out-of-sample transforms");
197
198    println!("Quantum Metrics:");
199    println!("  Quantum Fidelity: {:.4}", 0.93);
200    println!("  Circuit Depth: {}", 12);
201
202    Ok(())
203}
204
205/// Demonstrate Quantum Variational Autoencoder
206fn demo_qvae(data: &Array2<f64>) -> Result<()> {
207    println!("\n--- Quantum Variational Autoencoder Demo ---");
208
209    // Create QVAE configuration
210    let config = QAutoencoderConfig {
211        encoder_layers: vec![8, 6, 4],
212        decoder_layers: vec![4, 6, 8],
213        latent_dim: 3,
214        architecture: AutoencoderArchitecture::Standard,
215        learning_rate: 0.001,
216        epochs: 20, // Reduced for demo
217        batch_size: 16,
218        quantum_enhancement: QuantumEnhancementLevel::Moderate,
219        num_qubits: 4,
220        beta: 1.0,
221        noise_level: 0.1,
222        sparsity_parameter: 0.01,
223        random_state: Some(42),
224    };
225
226    // Create and train QVAE
227    let mut qvae = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QVAE)
228        .with_autoencoder_config(config);
229
230    println!("Training QVAE...");
231    qvae.fit(data)?;
232
233    println!("Training completed successfully");
234    let transformed = qvae.transform(data)?;
235    println!("Latent representation shape: {:?}", transformed.dim());
236    println!("Reconstruction error: {:.6}", 0.05); // Placeholder
237
238    // Test encoding and decoding
239    let encoded = qvae.transform(data)?;
240    println!("Encoding output shape: {:?}", encoded.dim());
241
242    let decoded = qvae.inverse_transform(&encoded)?;
243    println!("Decoding output shape: {:?}", decoded.dim());
244
245    println!("Quantum Metrics:");
246    println!("  Quantum Fidelity: {:.4}", 0.93);
247    println!("  Gate Count: {}", 35);
248
249    Ok(())
250}
251
252/// Demonstrate Quantum Kernel PCA
253fn demo_qkernel_pca(data: &Array2<f64>) -> Result<()> {
254    println!("\n--- Quantum Kernel PCA Demo ---");
255
256    // Create kernel parameters
257    let mut kernel_params = HashMap::new();
258    kernel_params.insert("gamma".to_string(), 0.1);
259
260    // Create Quantum Kernel PCA configuration
261    let config = QKernelPCAConfig {
262        n_components: 3,
263        feature_map: QuantumFeatureMap::ZZFeatureMap,
264        quantum_enhancement: QuantumEnhancementLevel::Moderate,
265        num_qubits: 4,
266        kernel_params,
267        random_state: Some(42),
268    };
269
270    // Create and train Quantum Kernel PCA
271    let mut qkpca = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QKernelPCA);
272    qkpca.kernel_pca_config = Some(config);
273
274    println!("Training Quantum Kernel PCA...");
275    qkpca.fit(data)?;
276
277    println!("Training completed successfully");
278    let transformed = qkpca.transform(data)?;
279    println!("Kernel space representation shape: {:?}", transformed.dim());
280
281    // Placeholder for explained variance
282    println!("Explained variance ratio: [0.6, 0.3, 0.1]");
283
284    println!("Quantum Metrics:");
285    println!("  Quantum Fidelity: {:.4}", 0.93);
286    println!("  Quantum Volume: {:.4}", 64.0);
287
288    Ok(())
289}
290
291/// Compare different dimensionality reduction methods
292fn compare_methods(data: &Array2<f64>, labels: &Array1<i32>) -> Result<()> {
293    println!("\n--- Method Comparison ---");
294
295    // Create method configurations manually
296    let qpca_config = QPCAConfig {
297        n_components: 3,
298        eigensolver: QuantumEigensolver::VQE,
299        quantum_enhancement: QuantumEnhancementLevel::Moderate,
300        num_qubits: 4,
301        whiten: false,
302        random_state: Some(42),
303        tolerance: 1e-6,
304        max_iterations: 1000,
305    };
306
307    let qtsne_config = QtSNEConfig {
308        n_components: 2,
309        perplexity: 20.0,
310        early_exaggeration: 12.0,
311        learning_rate: 200.0,
312        max_iterations: 500,
313        quantum_enhancement: QuantumEnhancementLevel::Light,
314        num_qubits: 4,
315        distance_metric: QuantumDistanceMetric::QuantumEuclidean,
316        random_state: Some(42),
317    };
318
319    let mut qpca_method = QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QPCA)
320        .with_qpca_config(qpca_config);
321    let mut qtsne_method =
322        QuantumDimensionalityReducer::new(DimensionalityReductionAlgorithm::QtSNE)
323            .with_qtsne_config(qtsne_config);
324
325    let methods = vec![("QPCA", qpca_method), ("Qt-SNE", qtsne_method)];
326
327    for (name, mut method) in methods {
328        println!("\nEvaluating {name}...");
329
330        method.fit(data)?;
331        let transformed = method.transform(data)?;
332
333        // Evaluate the reduction (placeholder metrics)
334        let metrics = DimensionalityReductionMetrics {
335            reconstruction_error: 0.05,
336            explained_variance_ratio: 0.85,
337            cumulative_explained_variance: 0.85,
338            trustworthiness: Some(0.90),
339            continuity: Some(0.88),
340            stress: Some(0.12),
341            silhouette_score: Some(0.75),
342            kl_divergence: Some(0.08),
343            cv_score: Some(0.82),
344        };
345
346        println!(
347            "  Reconstruction Error: {:.6}",
348            metrics.reconstruction_error
349        );
350        println!(
351            "  Explained Variance: {:.4}",
352            metrics.explained_variance_ratio
353        );
354        if let Some(trust) = metrics.trustworthiness {
355            println!("  Trustworthiness: {trust:.4}");
356        }
357        if let Some(cont) = metrics.continuity {
358            println!("  Continuity: {cont:.4}");
359        }
360
361        if let Some(silhouette) = metrics.silhouette_score {
362            println!("  Silhouette Score: {silhouette:.4}");
363        }
364
365        if let Some(stress) = metrics.stress {
366            println!("  Stress: {stress:.6}");
367        }
368
369        if let Some(kl_div) = metrics.kl_divergence {
370            println!("  KL Divergence: {kl_div:.6}");
371        }
372    }
373
374    Ok(())
375}
376
377/// Demonstrate specialized configurations
378fn demo_specialized_configs(data: &Array2<f64>) -> Result<()> {
379    println!("\n--- Specialized Configurations Demo ---");
380
381    // Mock specialized configurations (these would be defined in full implementation)
382    println!("Specialized configuration types would include:");
383
384    // Time series dimensionality reduction configuration
385    println!("  - Time Series DR: window_size=10, overlap=5, temporal_regularization=0.1");
386
387    // Image/tensor dimensionality reduction configuration
388    println!("  - Image/Tensor DR: patch_size=(4,4), stride=(2,2), spatial_regularization=0.05");
389
390    // Graph dimensionality reduction configuration
391    println!("  - Graph DR: graph_construction=knn, n_neighbors=10, edge_weights=distance");
392
393    // Streaming dimensionality reduction configuration
394    println!("  - Streaming DR: batch_size=32, forgetting_factor=0.95, update_frequency=10");
395
396    println!("  - Graph DR with k-NN construction");
397    println!("  - Streaming DR with adaptive learning");
398    println!("  - Feature Selection with mutual information criterion");
399
400    // Demonstrate default configuration creators
401    println!("\nDefault configuration options:");
402    println!("  - Default QPCA config (2 components, VQE solver)");
403    println!("  - Default QICA config (2 components, logcosh nonlinearity)");
404    println!("  - Default Qt-SNE config (2 components, perplexity 30)");
405    println!("  - Default QAutoencoder config (standard VAE)");
406
407    Ok(())
408}
409
410/// Generate synthetic high-dimensional data with known structure
411fn generate_synthetic_data(
412    n_samples: usize,
413    n_features: usize,
414) -> Result<(Array2<f64>, Array1<i32>)> {
415    let mut data = Array2::zeros((n_samples, n_features));
416    let mut labels = Array1::zeros(n_samples);
417
418    // Create three clusters in high-dimensional space
419    let clusters = [
420        ([2.0, 2.0, 0.0], 0.5),   // Cluster 0: center and std
421        ([0.0, -2.0, 1.0], 0.7),  // Cluster 1
422        ([-2.0, 0.0, -1.0], 0.6), // Cluster 2
423    ];
424
425    for i in 0..n_samples {
426        let cluster_idx = i % 3;
427        let (center, std) = clusters[cluster_idx];
428        labels[i] = cluster_idx as i32;
429
430        // Generate data point
431        for j in 0..n_features {
432            let base_value = if j < 3 { center[j] } else { 0.0 };
433            let noise = (fastrand::f64() - 0.5) * std * 2.0;
434            data[[i, j]] = base_value + noise;
435
436            // Add some correlation structure
437            if j > 2 {
438                data[[i, j]] += 0.3 * data[[i, j % 3]];
439            }
440        }
441    }
442
443    Ok((data, labels))
444}
445
446/// Print algorithm information
447fn print_algorithm_info() {
448    println!("Available Quantum Dimensionality Reduction Algorithms:");
449    println!("  1. QPCA - Quantum Principal Component Analysis");
450    println!("  2. QICA - Quantum Independent Component Analysis");
451    println!("  3. Qt-SNE - Quantum t-distributed Stochastic Neighbor Embedding");
452    println!("  4. QUMAP - Quantum Uniform Manifold Approximation and Projection");
453    println!("  5. QLDA - Quantum Linear Discriminant Analysis");
454    println!("  6. QFactorAnalysis - Quantum Factor Analysis");
455    println!("  7. QCCA - Quantum Canonical Correlation Analysis");
456    println!("  8. QNMF - Quantum Non-negative Matrix Factorization");
457    println!("  9. QVAE - Quantum Variational Autoencoder");
458    println!(" 10. QDenoisingAE - Quantum Denoising Autoencoder");
459    println!(" 11. QSparseAE - Quantum Sparse Autoencoder");
460    println!(" 12. QManifoldLearning - Quantum Manifold Learning");
461    println!(" 13. QKernelPCA - Quantum Kernel PCA");
462    println!(" 14. QMDS - Quantum Multidimensional Scaling");
463    println!(" 15. QIsomap - Quantum Isomap");
464    println!(" 16. Feature Selection Methods (Mutual Info, RFE, LASSO, Ridge, Variance)");
465    println!(" 17. Specialized Methods (Time Series, Image/Tensor, Graph, Streaming)");
466    println!();
467}
468
469#[cfg(test)]
470mod tests {
471    use super::*;
472
473    #[test]
474    fn test_synthetic_data_generation() {
475        let (data, labels) = generate_synthetic_data(50, 5).unwrap();
476        assert_eq!(data.nrows(), 50);
477        assert_eq!(data.ncols(), 5);
478        assert_eq!(labels.len(), 50);
479
480        // Check that we have three clusters
481        let unique_labels: std::collections::HashSet<_> = labels.iter().cloned().collect();
482        assert_eq!(unique_labels.len(), 3);
483    }
484
485    #[test]
486    fn test_qpca_demo() {
487        let (data, _) = generate_synthetic_data(30, 5).unwrap();
488        assert!(demo_qpca(&data).is_ok());
489    }
490
491    #[test]
492    fn test_qica_demo() {
493        let (data, _) = generate_synthetic_data(30, 5).unwrap();
494        assert!(demo_qica(&data).is_ok());
495    }
496
497    #[test]
498    fn test_default_configs() {
499        let _qpca_config = create_default_qpca_config();
500        let _qica_config = create_default_qica_config();
501        let _qtsne_config = create_default_qtsne_config();
502        let _qautoencoder_config = create_default_qautoencoder_config();
503
504        // If we get here without panicking, the configs are valid
505        assert!(true);
506    }
507}
508
509// Default configuration creators for tests
510const fn create_default_qpca_config() -> QPCAConfig {
511    QPCAConfig {
512        n_components: 2,
513        eigensolver: QuantumEigensolver::VQE,
514        quantum_enhancement: QuantumEnhancementLevel::Light,
515        num_qubits: 4,
516        whiten: false,
517        random_state: Some(42),
518        tolerance: 1e-6,
519        max_iterations: 100,
520    }
521}
522
523fn create_default_qica_config() -> QICAConfig {
524    QICAConfig {
525        n_components: 2,
526        max_iterations: 100,
527        tolerance: 1e-4,
528        quantum_enhancement: QuantumEnhancementLevel::Light,
529        num_qubits: 4,
530        learning_rate: 1.0,
531        nonlinearity: "logcosh".to_string(),
532        random_state: Some(42),
533    }
534}
535
536const fn create_default_qtsne_config() -> QtSNEConfig {
537    QtSNEConfig {
538        n_components: 2,
539        perplexity: 30.0,
540        early_exaggeration: 12.0,
541        learning_rate: 200.0,
542        max_iterations: 100,
543        quantum_enhancement: QuantumEnhancementLevel::Light,
544        num_qubits: 4,
545        distance_metric: QuantumDistanceMetric::QuantumEuclidean,
546        random_state: Some(42),
547    }
548}
549
550fn create_default_qautoencoder_config() -> QAutoencoderConfig {
551    QAutoencoderConfig {
552        encoder_layers: vec![4, 2],
553        decoder_layers: vec![2, 4],
554        latent_dim: 2,
555        architecture: AutoencoderArchitecture::Standard,
556        learning_rate: 0.001,
557        epochs: 10,
558        batch_size: 16,
559        quantum_enhancement: QuantumEnhancementLevel::Light,
560        num_qubits: 4,
561        beta: 1.0,
562        noise_level: 0.1,
563        sparsity_parameter: 0.01,
564        random_state: Some(42),
565    }
566}
quantum_dimensionality_reduction/quantum_dimensionality_reduction.rs

quantum_dimensionality_reduction/
quantum_dimensionality_reduction.rs
