TaskGenerator

quantrs2_ml::meta_learning

Struct TaskGenerator 

Source 
pub struct TaskGenerator { /* private fields */ }
Expand description

Task generator for meta-learning experiments

Implementations§

Source§

impl TaskGenerator

Source

pub fn new(feature_dim: usize, num_classes: usize) -> Self

Create new task generator

Examples found in repository?
examples/quantum_meta_learning.rs (line 79)
48fn maml_demo() -> Result<()> {
49    // Create quantum model
50    let layers = vec![
51        QNNLayerType::EncodingLayer { num_features: 4 },
52        QNNLayerType::VariationalLayer { num_params: 12 },
53        QNNLayerType::EntanglementLayer {
54            connectivity: "circular".to_string(),
55        },
56        QNNLayerType::VariationalLayer { num_params: 12 },
57        QNNLayerType::MeasurementLayer {
58            measurement_basis: "computational".to_string(),
59        },
60    ];
61
62    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 3)?;
63
64    // Create MAML learner
65    let algorithm = MetaLearningAlgorithm::MAML {
66        inner_steps: 5,
67        inner_lr: 0.01,
68        first_order: true, // Use first-order approximation for efficiency
69    };
70
71    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
72
73    println!("   Created MAML meta-learner:");
74    println!("   - Inner steps: 5");
75    println!("   - Inner learning rate: 0.01");
76    println!("   - Using first-order approximation");
77
78    // Generate tasks
79    let generator = TaskGenerator::new(4, 3);
80    let tasks: Vec<MetaTask> = (0..20)
81        .map(|_| generator.generate_rotation_task(30))
82        .collect();
83
84    // Meta-train
85    println!("\n   Meta-training on 20 rotation tasks...");
86    let mut optimizer = Adam::new(0.001);
87    meta_learner.meta_train(&tasks, &mut optimizer, 50, 5)?;
88
89    // Test adaptation
90    let test_task = generator.generate_rotation_task(20);
91    println!("\n   Testing adaptation to new task...");
92
93    let adapted_params = meta_learner.adapt_to_task(&test_task)?;
94    println!("   Successfully adapted to new task");
95    println!(
96        "   Parameter adaptation magnitude: {:.4}",
97        (&adapted_params - meta_learner.meta_params())
98            .mapv(f64::abs)
99            .mean()
100            .unwrap()
101    );
102
103    Ok(())
104}
105
106/// Reptile algorithm demonstration
107fn reptile_demo() -> Result<()> {
108    let layers = vec![
109        QNNLayerType::EncodingLayer { num_features: 2 },
110        QNNLayerType::VariationalLayer { num_params: 8 },
111        QNNLayerType::MeasurementLayer {
112            measurement_basis: "Pauli-Z".to_string(),
113        },
114    ];
115
116    let qnn = QuantumNeuralNetwork::new(layers, 4, 2, 2)?;
117
118    let algorithm = MetaLearningAlgorithm::Reptile {
119        inner_steps: 10,
120        inner_lr: 0.1,
121    };
122
123    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
124
125    println!("   Created Reptile meta-learner:");
126    println!("   - Inner steps: 10");
127    println!("   - Inner learning rate: 0.1");
128
129    // Generate sinusoid tasks
130    let generator = TaskGenerator::new(2, 2);
131    let tasks: Vec<MetaTask> = (0..15)
132        .map(|_| generator.generate_sinusoid_task(40))
133        .collect();
134
135    println!("\n   Meta-training on 15 sinusoid tasks...");
136    let mut optimizer = Adam::new(0.001);
137    meta_learner.meta_train(&tasks, &mut optimizer, 30, 3)?;
138
139    println!("   Reptile training complete");
140
141    // Analyze task similarities
142    println!("\n   Task parameter statistics:");
143    for (i, task) in tasks.iter().take(3).enumerate() {
144        if let Some(amplitude) = task.metadata.get("amplitude") {
145            if let Some(phase) = task.metadata.get("phase") {
146                println!("   Task {i}: amplitude={amplitude:.2}, phase={phase:.2}");
147            }
148        }
149    }
150
151    Ok(())
152}
153
154/// `ProtoMAML` demonstration
155fn protomaml_demo() -> Result<()> {
156    let layers = vec![
157        QNNLayerType::EncodingLayer { num_features: 8 },
158        QNNLayerType::VariationalLayer { num_params: 16 },
159        QNNLayerType::EntanglementLayer {
160            connectivity: "full".to_string(),
161        },
162        QNNLayerType::MeasurementLayer {
163            measurement_basis: "computational".to_string(),
164        },
165    ];
166
167    let qnn = QuantumNeuralNetwork::new(layers, 4, 8, 16)?;
168
169    let algorithm = MetaLearningAlgorithm::ProtoMAML {
170        inner_steps: 5,
171        inner_lr: 0.01,
172        proto_weight: 0.5, // Weight for prototype regularization
173    };
174
175    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
176
177    println!("   Created ProtoMAML meta-learner:");
178    println!("   - Combines MAML with prototypical networks");
179    println!("   - Prototype weight: 0.5");
180
181    // Generate classification tasks
182    let generator = TaskGenerator::new(8, 4);
183    let tasks: Vec<MetaTask> = (0..10)
184        .map(|_| generator.generate_rotation_task(50))
185        .collect();
186
187    println!("\n   Meta-training on 4-way classification tasks...");
188    let mut optimizer = Adam::new(0.001);
189    meta_learner.meta_train(&tasks, &mut optimizer, 40, 2)?;
190
191    println!("   ProtoMAML leverages both gradient-based and metric-based learning");
192
193    Ok(())
194}
195
196/// Meta-SGD demonstration
197fn metasgd_demo() -> Result<()> {
198    let layers = vec![
199        QNNLayerType::EncodingLayer { num_features: 4 },
200        QNNLayerType::VariationalLayer { num_params: 12 },
201        QNNLayerType::MeasurementLayer {
202            measurement_basis: "Pauli-XYZ".to_string(),
203        },
204    ];
205
206    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 3)?;
207
208    let algorithm = MetaLearningAlgorithm::MetaSGD { inner_steps: 3 };
209
210    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
211
212    println!("   Created Meta-SGD learner:");
213    println!("   - Learns per-parameter learning rates");
214    println!("   - Inner steps: 3");
215
216    // Generate diverse tasks
217    let generator = TaskGenerator::new(4, 3);
218    let mut tasks = Vec::new();
219
220    // Mix different task types
221    for i in 0..12 {
222        if i % 2 == 0 {
223            tasks.push(generator.generate_rotation_task(30));
224        } else {
225            tasks.push(generator.generate_sinusoid_task(30));
226        }
227    }
228
229    println!("\n   Meta-training on mixed task distribution...");
230    let mut optimizer = Adam::new(0.0005);
231    meta_learner.meta_train(&tasks, &mut optimizer, 50, 4)?;
232
233    if let Some(lr) = meta_learner.per_param_lr() {
234        println!("\n   Learned per-parameter learning rates:");
235        println!(
236            "   - Min LR: {:.4}",
237            lr.iter().copied().fold(f64::INFINITY, f64::min)
238        );
239        println!(
240            "   - Max LR: {:.4}",
241            lr.iter().copied().fold(f64::NEG_INFINITY, f64::max)
242        );
243        println!("   - Mean LR: {:.4}", lr.mean().unwrap());
244    }
245
246    Ok(())
247}
248
249/// ANIL demonstration
250fn anil_demo() -> Result<()> {
251    let layers = vec![
252        QNNLayerType::EncodingLayer { num_features: 6 },
253        QNNLayerType::VariationalLayer { num_params: 12 },
254        QNNLayerType::EntanglementLayer {
255            connectivity: "circular".to_string(),
256        },
257        QNNLayerType::VariationalLayer { num_params: 12 },
258        QNNLayerType::VariationalLayer { num_params: 6 }, // Final layer (adapted)
259        QNNLayerType::MeasurementLayer {
260            measurement_basis: "computational".to_string(),
261        },
262    ];
263
264    let qnn = QuantumNeuralNetwork::new(layers, 4, 6, 2)?;
265
266    let algorithm = MetaLearningAlgorithm::ANIL {
267        inner_steps: 10,
268        inner_lr: 0.1,
269    };
270
271    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
272
273    println!("   Created ANIL (Almost No Inner Loop) learner:");
274    println!("   - Only adapts final layer during inner loop");
275    println!("   - More parameter efficient than MAML");
276    println!("   - Inner steps: 10");
277
278    // Generate binary classification tasks
279    let generator = TaskGenerator::new(6, 2);
280    let tasks: Vec<MetaTask> = (0..15)
281        .map(|_| generator.generate_rotation_task(40))
282        .collect();
283
284    println!("\n   Meta-training on binary classification tasks...");
285    let mut optimizer = Adam::new(0.001);
286    meta_learner.meta_train(&tasks, &mut optimizer, 40, 5)?;
287
288    println!("   ANIL reduces computational cost while maintaining performance");
289
290    Ok(())
291}
292
293/// Continual meta-learning demonstration
294fn continual_meta_learning_demo() -> Result<()> {
295    let layers = vec![
296        QNNLayerType::EncodingLayer { num_features: 4 },
297        QNNLayerType::VariationalLayer { num_params: 8 },
298        QNNLayerType::MeasurementLayer {
299            measurement_basis: "computational".to_string(),
300        },
301    ];
302
303    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 2)?;
304
305    let algorithm = MetaLearningAlgorithm::Reptile {
306        inner_steps: 5,
307        inner_lr: 0.05,
308    };
309
310    let meta_learner = QuantumMetaLearner::new(algorithm, qnn);
311    let mut continual_learner = ContinualMetaLearner::new(
312        meta_learner,
313        10,  // memory capacity
314        0.3, // replay ratio
315    );
316
317    println!("   Created Continual Meta-Learner:");
318    println!("   - Memory capacity: 10 tasks");
319    println!("   - Replay ratio: 30%");
320
321    // Generate sequence of tasks
322    let generator = TaskGenerator::new(4, 2);
323
324    println!("\n   Learning sequence of tasks...");
325    for i in 0..20 {
326        let task = if i < 10 {
327            generator.generate_rotation_task(30)
328        } else {
329            generator.generate_sinusoid_task(30)
330        };
331
332        continual_learner.learn_task(task)?;
333
334        if i % 5 == 4 {
335            println!(
336                "   Learned {} tasks, memory contains {} unique tasks",
337                i + 1,
338                continual_learner.memory_buffer_len()
339            );
340        }
341    }
342
343    println!("\n   Continual learning prevents catastrophic forgetting");
344
345    Ok(())
346}
347
348/// Task distribution analysis
349fn task_distribution_demo() -> Result<()> {
350    println!("   Analyzing task distributions...\n");
351
352    let generator = TaskGenerator::new(4, 3);
353
354    // Generate multiple tasks and analyze their properties
355    let mut rotation_tasks = Vec::new();
356    let mut sinusoid_tasks = Vec::new();
357
358    for _ in 0..50 {
359        rotation_tasks.push(generator.generate_rotation_task(20));
360        sinusoid_tasks.push(generator.generate_sinusoid_task(20));
361    }
362
363    // Analyze rotation tasks
364    println!("   Rotation Task Distribution:");
365    let angles: Vec<f64> = rotation_tasks
366        .iter()
367        .filter_map(|t| t.metadata.get("rotation_angle").copied())
368        .collect();
369
370    if !angles.is_empty() {
371        let mean_angle = angles.iter().sum::<f64>() / angles.len() as f64;
372        println!("   - Mean rotation angle: {mean_angle:.2} rad");
373        println!(
374            "   - Angle range: [{:.2}, {:.2}] rad",
375            angles.iter().copied().fold(f64::INFINITY, f64::min),
376            angles.iter().copied().fold(f64::NEG_INFINITY, f64::max)
377        );
378    }
379
380    // Analyze sinusoid tasks
381    println!("\n   Sinusoid Task Distribution:");
382    let amplitudes: Vec<f64> = sinusoid_tasks
383        .iter()
384        .filter_map(|t| t.metadata.get("amplitude").copied())
385        .collect();
386
387    if !amplitudes.is_empty() {
388        let mean_amp = amplitudes.iter().sum::<f64>() / amplitudes.len() as f64;
389        println!("   - Mean amplitude: {mean_amp:.2}");
390        println!(
391            "   - Amplitude range: [{:.2}, {:.2}]",
392            amplitudes.iter().copied().fold(f64::INFINITY, f64::min),
393            amplitudes.iter().copied().fold(f64::NEG_INFINITY, f64::max)
394        );
395    }
396
397    // Compare task complexities
398    println!("\n   Task Complexity Comparison:");
399    println!(
400        "   - Rotation tasks: {} training samples each",
401        rotation_tasks[0].train_data.len()
402    );
403    println!(
404        "   - Sinusoid tasks: {} training samples each",
405        sinusoid_tasks[0].train_data.len()
406    );
407    println!("   - Both use binary classification for simplicity");
408
409    Ok(())
410}
Source

pub fn generate_sinusoid_task(&self, num_samples: usize) -> MetaTask

Generate sinusoid regression task

Examples found in repository?
examples/quantum_meta_learning.rs (line 132)
107fn reptile_demo() -> Result<()> {
108    let layers = vec![
109        QNNLayerType::EncodingLayer { num_features: 2 },
110        QNNLayerType::VariationalLayer { num_params: 8 },
111        QNNLayerType::MeasurementLayer {
112            measurement_basis: "Pauli-Z".to_string(),
113        },
114    ];
115
116    let qnn = QuantumNeuralNetwork::new(layers, 4, 2, 2)?;
117
118    let algorithm = MetaLearningAlgorithm::Reptile {
119        inner_steps: 10,
120        inner_lr: 0.1,
121    };
122
123    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
124
125    println!("   Created Reptile meta-learner:");
126    println!("   - Inner steps: 10");
127    println!("   - Inner learning rate: 0.1");
128
129    // Generate sinusoid tasks
130    let generator = TaskGenerator::new(2, 2);
131    let tasks: Vec<MetaTask> = (0..15)
132        .map(|_| generator.generate_sinusoid_task(40))
133        .collect();
134
135    println!("\n   Meta-training on 15 sinusoid tasks...");
136    let mut optimizer = Adam::new(0.001);
137    meta_learner.meta_train(&tasks, &mut optimizer, 30, 3)?;
138
139    println!("   Reptile training complete");
140
141    // Analyze task similarities
142    println!("\n   Task parameter statistics:");
143    for (i, task) in tasks.iter().take(3).enumerate() {
144        if let Some(amplitude) = task.metadata.get("amplitude") {
145            if let Some(phase) = task.metadata.get("phase") {
146                println!("   Task {i}: amplitude={amplitude:.2}, phase={phase:.2}");
147            }
148        }
149    }
150
151    Ok(())
152}
153
154/// `ProtoMAML` demonstration
155fn protomaml_demo() -> Result<()> {
156    let layers = vec![
157        QNNLayerType::EncodingLayer { num_features: 8 },
158        QNNLayerType::VariationalLayer { num_params: 16 },
159        QNNLayerType::EntanglementLayer {
160            connectivity: "full".to_string(),
161        },
162        QNNLayerType::MeasurementLayer {
163            measurement_basis: "computational".to_string(),
164        },
165    ];
166
167    let qnn = QuantumNeuralNetwork::new(layers, 4, 8, 16)?;
168
169    let algorithm = MetaLearningAlgorithm::ProtoMAML {
170        inner_steps: 5,
171        inner_lr: 0.01,
172        proto_weight: 0.5, // Weight for prototype regularization
173    };
174
175    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
176
177    println!("   Created ProtoMAML meta-learner:");
178    println!("   - Combines MAML with prototypical networks");
179    println!("   - Prototype weight: 0.5");
180
181    // Generate classification tasks
182    let generator = TaskGenerator::new(8, 4);
183    let tasks: Vec<MetaTask> = (0..10)
184        .map(|_| generator.generate_rotation_task(50))
185        .collect();
186
187    println!("\n   Meta-training on 4-way classification tasks...");
188    let mut optimizer = Adam::new(0.001);
189    meta_learner.meta_train(&tasks, &mut optimizer, 40, 2)?;
190
191    println!("   ProtoMAML leverages both gradient-based and metric-based learning");
192
193    Ok(())
194}
195
196/// Meta-SGD demonstration
197fn metasgd_demo() -> Result<()> {
198    let layers = vec![
199        QNNLayerType::EncodingLayer { num_features: 4 },
200        QNNLayerType::VariationalLayer { num_params: 12 },
201        QNNLayerType::MeasurementLayer {
202            measurement_basis: "Pauli-XYZ".to_string(),
203        },
204    ];
205
206    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 3)?;
207
208    let algorithm = MetaLearningAlgorithm::MetaSGD { inner_steps: 3 };
209
210    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
211
212    println!("   Created Meta-SGD learner:");
213    println!("   - Learns per-parameter learning rates");
214    println!("   - Inner steps: 3");
215
216    // Generate diverse tasks
217    let generator = TaskGenerator::new(4, 3);
218    let mut tasks = Vec::new();
219
220    // Mix different task types
221    for i in 0..12 {
222        if i % 2 == 0 {
223            tasks.push(generator.generate_rotation_task(30));
224        } else {
225            tasks.push(generator.generate_sinusoid_task(30));
226        }
227    }
228
229    println!("\n   Meta-training on mixed task distribution...");
230    let mut optimizer = Adam::new(0.0005);
231    meta_learner.meta_train(&tasks, &mut optimizer, 50, 4)?;
232
233    if let Some(lr) = meta_learner.per_param_lr() {
234        println!("\n   Learned per-parameter learning rates:");
235        println!(
236            "   - Min LR: {:.4}",
237            lr.iter().copied().fold(f64::INFINITY, f64::min)
238        );
239        println!(
240            "   - Max LR: {:.4}",
241            lr.iter().copied().fold(f64::NEG_INFINITY, f64::max)
242        );
243        println!("   - Mean LR: {:.4}", lr.mean().unwrap());
244    }
245
246    Ok(())
247}
248
249/// ANIL demonstration
250fn anil_demo() -> Result<()> {
251    let layers = vec![
252        QNNLayerType::EncodingLayer { num_features: 6 },
253        QNNLayerType::VariationalLayer { num_params: 12 },
254        QNNLayerType::EntanglementLayer {
255            connectivity: "circular".to_string(),
256        },
257        QNNLayerType::VariationalLayer { num_params: 12 },
258        QNNLayerType::VariationalLayer { num_params: 6 }, // Final layer (adapted)
259        QNNLayerType::MeasurementLayer {
260            measurement_basis: "computational".to_string(),
261        },
262    ];
263
264    let qnn = QuantumNeuralNetwork::new(layers, 4, 6, 2)?;
265
266    let algorithm = MetaLearningAlgorithm::ANIL {
267        inner_steps: 10,
268        inner_lr: 0.1,
269    };
270
271    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
272
273    println!("   Created ANIL (Almost No Inner Loop) learner:");
274    println!("   - Only adapts final layer during inner loop");
275    println!("   - More parameter efficient than MAML");
276    println!("   - Inner steps: 10");
277
278    // Generate binary classification tasks
279    let generator = TaskGenerator::new(6, 2);
280    let tasks: Vec<MetaTask> = (0..15)
281        .map(|_| generator.generate_rotation_task(40))
282        .collect();
283
284    println!("\n   Meta-training on binary classification tasks...");
285    let mut optimizer = Adam::new(0.001);
286    meta_learner.meta_train(&tasks, &mut optimizer, 40, 5)?;
287
288    println!("   ANIL reduces computational cost while maintaining performance");
289
290    Ok(())
291}
292
293/// Continual meta-learning demonstration
294fn continual_meta_learning_demo() -> Result<()> {
295    let layers = vec![
296        QNNLayerType::EncodingLayer { num_features: 4 },
297        QNNLayerType::VariationalLayer { num_params: 8 },
298        QNNLayerType::MeasurementLayer {
299            measurement_basis: "computational".to_string(),
300        },
301    ];
302
303    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 2)?;
304
305    let algorithm = MetaLearningAlgorithm::Reptile {
306        inner_steps: 5,
307        inner_lr: 0.05,
308    };
309
310    let meta_learner = QuantumMetaLearner::new(algorithm, qnn);
311    let mut continual_learner = ContinualMetaLearner::new(
312        meta_learner,
313        10,  // memory capacity
314        0.3, // replay ratio
315    );
316
317    println!("   Created Continual Meta-Learner:");
318    println!("   - Memory capacity: 10 tasks");
319    println!("   - Replay ratio: 30%");
320
321    // Generate sequence of tasks
322    let generator = TaskGenerator::new(4, 2);
323
324    println!("\n   Learning sequence of tasks...");
325    for i in 0..20 {
326        let task = if i < 10 {
327            generator.generate_rotation_task(30)
328        } else {
329            generator.generate_sinusoid_task(30)
330        };
331
332        continual_learner.learn_task(task)?;
333
334        if i % 5 == 4 {
335            println!(
336                "   Learned {} tasks, memory contains {} unique tasks",
337                i + 1,
338                continual_learner.memory_buffer_len()
339            );
340        }
341    }
342
343    println!("\n   Continual learning prevents catastrophic forgetting");
344
345    Ok(())
346}
347
348/// Task distribution analysis
349fn task_distribution_demo() -> Result<()> {
350    println!("   Analyzing task distributions...\n");
351
352    let generator = TaskGenerator::new(4, 3);
353
354    // Generate multiple tasks and analyze their properties
355    let mut rotation_tasks = Vec::new();
356    let mut sinusoid_tasks = Vec::new();
357
358    for _ in 0..50 {
359        rotation_tasks.push(generator.generate_rotation_task(20));
360        sinusoid_tasks.push(generator.generate_sinusoid_task(20));
361    }
362
363    // Analyze rotation tasks
364    println!("   Rotation Task Distribution:");
365    let angles: Vec<f64> = rotation_tasks
366        .iter()
367        .filter_map(|t| t.metadata.get("rotation_angle").copied())
368        .collect();
369
370    if !angles.is_empty() {
371        let mean_angle = angles.iter().sum::<f64>() / angles.len() as f64;
372        println!("   - Mean rotation angle: {mean_angle:.2} rad");
373        println!(
374            "   - Angle range: [{:.2}, {:.2}] rad",
375            angles.iter().copied().fold(f64::INFINITY, f64::min),
376            angles.iter().copied().fold(f64::NEG_INFINITY, f64::max)
377        );
378    }
379
380    // Analyze sinusoid tasks
381    println!("\n   Sinusoid Task Distribution:");
382    let amplitudes: Vec<f64> = sinusoid_tasks
383        .iter()
384        .filter_map(|t| t.metadata.get("amplitude").copied())
385        .collect();
386
387    if !amplitudes.is_empty() {
388        let mean_amp = amplitudes.iter().sum::<f64>() / amplitudes.len() as f64;
389        println!("   - Mean amplitude: {mean_amp:.2}");
390        println!(
391            "   - Amplitude range: [{:.2}, {:.2}]",
392            amplitudes.iter().copied().fold(f64::INFINITY, f64::min),
393            amplitudes.iter().copied().fold(f64::NEG_INFINITY, f64::max)
394        );
395    }
396
397    // Compare task complexities
398    println!("\n   Task Complexity Comparison:");
399    println!(
400        "   - Rotation tasks: {} training samples each",
401        rotation_tasks[0].train_data.len()
402    );
403    println!(
404        "   - Sinusoid tasks: {} training samples each",
405        sinusoid_tasks[0].train_data.len()
406    );
407    println!("   - Both use binary classification for simplicity");
408
409    Ok(())
410}
Source

pub fn generate_rotation_task(&self, num_samples: usize) -> MetaTask

Generate classification task with rotated features

Examples found in repository?
examples/quantum_meta_learning.rs (line 81)
48fn maml_demo() -> Result<()> {
49    // Create quantum model
50    let layers = vec![
51        QNNLayerType::EncodingLayer { num_features: 4 },
52        QNNLayerType::VariationalLayer { num_params: 12 },
53        QNNLayerType::EntanglementLayer {
54            connectivity: "circular".to_string(),
55        },
56        QNNLayerType::VariationalLayer { num_params: 12 },
57        QNNLayerType::MeasurementLayer {
58            measurement_basis: "computational".to_string(),
59        },
60    ];
61
62    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 3)?;
63
64    // Create MAML learner
65    let algorithm = MetaLearningAlgorithm::MAML {
66        inner_steps: 5,
67        inner_lr: 0.01,
68        first_order: true, // Use first-order approximation for efficiency
69    };
70
71    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
72
73    println!("   Created MAML meta-learner:");
74    println!("   - Inner steps: 5");
75    println!("   - Inner learning rate: 0.01");
76    println!("   - Using first-order approximation");
77
78    // Generate tasks
79    let generator = TaskGenerator::new(4, 3);
80    let tasks: Vec<MetaTask> = (0..20)
81        .map(|_| generator.generate_rotation_task(30))
82        .collect();
83
84    // Meta-train
85    println!("\n   Meta-training on 20 rotation tasks...");
86    let mut optimizer = Adam::new(0.001);
87    meta_learner.meta_train(&tasks, &mut optimizer, 50, 5)?;
88
89    // Test adaptation
90    let test_task = generator.generate_rotation_task(20);
91    println!("\n   Testing adaptation to new task...");
92
93    let adapted_params = meta_learner.adapt_to_task(&test_task)?;
94    println!("   Successfully adapted to new task");
95    println!(
96        "   Parameter adaptation magnitude: {:.4}",
97        (&adapted_params - meta_learner.meta_params())
98            .mapv(f64::abs)
99            .mean()
100            .unwrap()
101    );
102
103    Ok(())
104}
105
106/// Reptile algorithm demonstration
107fn reptile_demo() -> Result<()> {
108    let layers = vec![
109        QNNLayerType::EncodingLayer { num_features: 2 },
110        QNNLayerType::VariationalLayer { num_params: 8 },
111        QNNLayerType::MeasurementLayer {
112            measurement_basis: "Pauli-Z".to_string(),
113        },
114    ];
115
116    let qnn = QuantumNeuralNetwork::new(layers, 4, 2, 2)?;
117
118    let algorithm = MetaLearningAlgorithm::Reptile {
119        inner_steps: 10,
120        inner_lr: 0.1,
121    };
122
123    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
124
125    println!("   Created Reptile meta-learner:");
126    println!("   - Inner steps: 10");
127    println!("   - Inner learning rate: 0.1");
128
129    // Generate sinusoid tasks
130    let generator = TaskGenerator::new(2, 2);
131    let tasks: Vec<MetaTask> = (0..15)
132        .map(|_| generator.generate_sinusoid_task(40))
133        .collect();
134
135    println!("\n   Meta-training on 15 sinusoid tasks...");
136    let mut optimizer = Adam::new(0.001);
137    meta_learner.meta_train(&tasks, &mut optimizer, 30, 3)?;
138
139    println!("   Reptile training complete");
140
141    // Analyze task similarities
142    println!("\n   Task parameter statistics:");
143    for (i, task) in tasks.iter().take(3).enumerate() {
144        if let Some(amplitude) = task.metadata.get("amplitude") {
145            if let Some(phase) = task.metadata.get("phase") {
146                println!("   Task {i}: amplitude={amplitude:.2}, phase={phase:.2}");
147            }
148        }
149    }
150
151    Ok(())
152}
153
154/// `ProtoMAML` demonstration
155fn protomaml_demo() -> Result<()> {
156    let layers = vec![
157        QNNLayerType::EncodingLayer { num_features: 8 },
158        QNNLayerType::VariationalLayer { num_params: 16 },
159        QNNLayerType::EntanglementLayer {
160            connectivity: "full".to_string(),
161        },
162        QNNLayerType::MeasurementLayer {
163            measurement_basis: "computational".to_string(),
164        },
165    ];
166
167    let qnn = QuantumNeuralNetwork::new(layers, 4, 8, 16)?;
168
169    let algorithm = MetaLearningAlgorithm::ProtoMAML {
170        inner_steps: 5,
171        inner_lr: 0.01,
172        proto_weight: 0.5, // Weight for prototype regularization
173    };
174
175    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
176
177    println!("   Created ProtoMAML meta-learner:");
178    println!("   - Combines MAML with prototypical networks");
179    println!("   - Prototype weight: 0.5");
180
181    // Generate classification tasks
182    let generator = TaskGenerator::new(8, 4);
183    let tasks: Vec<MetaTask> = (0..10)
184        .map(|_| generator.generate_rotation_task(50))
185        .collect();
186
187    println!("\n   Meta-training on 4-way classification tasks...");
188    let mut optimizer = Adam::new(0.001);
189    meta_learner.meta_train(&tasks, &mut optimizer, 40, 2)?;
190
191    println!("   ProtoMAML leverages both gradient-based and metric-based learning");
192
193    Ok(())
194}
195
196/// Meta-SGD demonstration
197fn metasgd_demo() -> Result<()> {
198    let layers = vec![
199        QNNLayerType::EncodingLayer { num_features: 4 },
200        QNNLayerType::VariationalLayer { num_params: 12 },
201        QNNLayerType::MeasurementLayer {
202            measurement_basis: "Pauli-XYZ".to_string(),
203        },
204    ];
205
206    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 3)?;
207
208    let algorithm = MetaLearningAlgorithm::MetaSGD { inner_steps: 3 };
209
210    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
211
212    println!("   Created Meta-SGD learner:");
213    println!("   - Learns per-parameter learning rates");
214    println!("   - Inner steps: 3");
215
216    // Generate diverse tasks
217    let generator = TaskGenerator::new(4, 3);
218    let mut tasks = Vec::new();
219
220    // Mix different task types
221    for i in 0..12 {
222        if i % 2 == 0 {
223            tasks.push(generator.generate_rotation_task(30));
224        } else {
225            tasks.push(generator.generate_sinusoid_task(30));
226        }
227    }
228
229    println!("\n   Meta-training on mixed task distribution...");
230    let mut optimizer = Adam::new(0.0005);
231    meta_learner.meta_train(&tasks, &mut optimizer, 50, 4)?;
232
233    if let Some(lr) = meta_learner.per_param_lr() {
234        println!("\n   Learned per-parameter learning rates:");
235        println!(
236            "   - Min LR: {:.4}",
237            lr.iter().copied().fold(f64::INFINITY, f64::min)
238        );
239        println!(
240            "   - Max LR: {:.4}",
241            lr.iter().copied().fold(f64::NEG_INFINITY, f64::max)
242        );
243        println!("   - Mean LR: {:.4}", lr.mean().unwrap());
244    }
245
246    Ok(())
247}
248
249/// ANIL demonstration
250fn anil_demo() -> Result<()> {
251    let layers = vec![
252        QNNLayerType::EncodingLayer { num_features: 6 },
253        QNNLayerType::VariationalLayer { num_params: 12 },
254        QNNLayerType::EntanglementLayer {
255            connectivity: "circular".to_string(),
256        },
257        QNNLayerType::VariationalLayer { num_params: 12 },
258        QNNLayerType::VariationalLayer { num_params: 6 }, // Final layer (adapted)
259        QNNLayerType::MeasurementLayer {
260            measurement_basis: "computational".to_string(),
261        },
262    ];
263
264    let qnn = QuantumNeuralNetwork::new(layers, 4, 6, 2)?;
265
266    let algorithm = MetaLearningAlgorithm::ANIL {
267        inner_steps: 10,
268        inner_lr: 0.1,
269    };
270
271    let mut meta_learner = QuantumMetaLearner::new(algorithm, qnn);
272
273    println!("   Created ANIL (Almost No Inner Loop) learner:");
274    println!("   - Only adapts final layer during inner loop");
275    println!("   - More parameter efficient than MAML");
276    println!("   - Inner steps: 10");
277
278    // Generate binary classification tasks
279    let generator = TaskGenerator::new(6, 2);
280    let tasks: Vec<MetaTask> = (0..15)
281        .map(|_| generator.generate_rotation_task(40))
282        .collect();
283
284    println!("\n   Meta-training on binary classification tasks...");
285    let mut optimizer = Adam::new(0.001);
286    meta_learner.meta_train(&tasks, &mut optimizer, 40, 5)?;
287
288    println!("   ANIL reduces computational cost while maintaining performance");
289
290    Ok(())
291}
292
293/// Continual meta-learning demonstration
294fn continual_meta_learning_demo() -> Result<()> {
295    let layers = vec![
296        QNNLayerType::EncodingLayer { num_features: 4 },
297        QNNLayerType::VariationalLayer { num_params: 8 },
298        QNNLayerType::MeasurementLayer {
299            measurement_basis: "computational".to_string(),
300        },
301    ];
302
303    let qnn = QuantumNeuralNetwork::new(layers, 4, 4, 2)?;
304
305    let algorithm = MetaLearningAlgorithm::Reptile {
306        inner_steps: 5,
307        inner_lr: 0.05,
308    };
309
310    let meta_learner = QuantumMetaLearner::new(algorithm, qnn);
311    let mut continual_learner = ContinualMetaLearner::new(
312        meta_learner,
313        10,  // memory capacity
314        0.3, // replay ratio
315    );
316
317    println!("   Created Continual Meta-Learner:");
318    println!("   - Memory capacity: 10 tasks");
319    println!("   - Replay ratio: 30%");
320
321    // Generate sequence of tasks
322    let generator = TaskGenerator::new(4, 2);
323
324    println!("\n   Learning sequence of tasks...");
325    for i in 0..20 {
326        let task = if i < 10 {
327            generator.generate_rotation_task(30)
328        } else {
329            generator.generate_sinusoid_task(30)
330        };
331
332        continual_learner.learn_task(task)?;
333
334        if i % 5 == 4 {
335            println!(
336                "   Learned {} tasks, memory contains {} unique tasks",
337                i + 1,
338                continual_learner.memory_buffer_len()
339            );
340        }
341    }
342
343    println!("\n   Continual learning prevents catastrophic forgetting");
344
345    Ok(())
346}
347
348/// Task distribution analysis
349fn task_distribution_demo() -> Result<()> {
350    println!("   Analyzing task distributions...\n");
351
352    let generator = TaskGenerator::new(4, 3);
353
354    // Generate multiple tasks and analyze their properties
355    let mut rotation_tasks = Vec::new();
356    let mut sinusoid_tasks = Vec::new();
357
358    for _ in 0..50 {
359        rotation_tasks.push(generator.generate_rotation_task(20));
360        sinusoid_tasks.push(generator.generate_sinusoid_task(20));
361    }
362
363    // Analyze rotation tasks
364    println!("   Rotation Task Distribution:");
365    let angles: Vec<f64> = rotation_tasks
366        .iter()
367        .filter_map(|t| t.metadata.get("rotation_angle").copied())
368        .collect();
369
370    if !angles.is_empty() {
371        let mean_angle = angles.iter().sum::<f64>() / angles.len() as f64;
372        println!("   - Mean rotation angle: {mean_angle:.2} rad");
373        println!(
374            "   - Angle range: [{:.2}, {:.2}] rad",
375            angles.iter().copied().fold(f64::INFINITY, f64::min),
376            angles.iter().copied().fold(f64::NEG_INFINITY, f64::max)
377        );
378    }
379
380    // Analyze sinusoid tasks
381    println!("\n   Sinusoid Task Distribution:");
382    let amplitudes: Vec<f64> = sinusoid_tasks
383        .iter()
384        .filter_map(|t| t.metadata.get("amplitude").copied())
385        .collect();
386
387    if !amplitudes.is_empty() {
388        let mean_amp = amplitudes.iter().sum::<f64>() / amplitudes.len() as f64;
389        println!("   - Mean amplitude: {mean_amp:.2}");
390        println!(
391            "   - Amplitude range: [{:.2}, {:.2}]",
392            amplitudes.iter().copied().fold(f64::INFINITY, f64::min),
393            amplitudes.iter().copied().fold(f64::NEG_INFINITY, f64::max)
394        );
395    }
396
397    // Compare task complexities
398    println!("\n   Task Complexity Comparison:");
399    println!(
400        "   - Rotation tasks: {} training samples each",
401        rotation_tasks[0].train_data.len()
402    );
403    println!(
404        "   - Sinusoid tasks: {} training samples each",
405        sinusoid_tasks[0].train_data.len()
406    );
407    println!("   - Both use binary classification for simplicity");
408
409    Ok(())
410}

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