quantrs2_ml/automl/search/
model_selector.rs

1//! Quantum Model Selector
2//!
3//! This module provides model selection functionality for quantum ML algorithms.
4
5use crate::automl::config::{AlgorithmSearchSpace, MLTaskType};
6use crate::automl::pipeline::QuantumMLPipeline;
7use crate::error::Result;
8use std::collections::HashMap;
9
10/// Quantum model selector
11#[derive(Debug, Clone)]
12pub struct QuantumModelSelector {
13    /// Model candidates
14    model_candidates: Vec<ModelCandidate>,
15
16    /// Selection strategy
17    selection_strategy: ModelSelectionStrategy,
18
19    /// Performance estimator
20    performance_estimator: ModelPerformanceEstimator,
21}
22
23/// Model candidate
24#[derive(Debug, Clone)]
25pub struct ModelCandidate {
26    /// Model type
27    pub model_type: ModelType,
28
29    /// Model configuration
30    pub configuration: ModelConfiguration,
31
32    /// Estimated performance
33    pub estimated_performance: f64,
34
35    /// Resource requirements
36    pub resource_requirements: ResourceRequirements,
37}
38
39/// Model types
40#[derive(Debug, Clone)]
41pub enum ModelType {
42    QuantumNeuralNetwork,
43    QuantumSupportVectorMachine,
44    QuantumClustering,
45    QuantumDimensionalityReduction,
46    QuantumTimeSeries,
47    QuantumAnomalyDetection,
48    EnsembleModel,
49}
50
51/// Model configuration
52#[derive(Debug, Clone)]
53pub struct ModelConfiguration {
54    /// Architecture configuration
55    pub architecture: ArchitectureConfiguration,
56
57    /// Hyperparameters
58    pub hyperparameters: HashMap<String, f64>,
59
60    /// Preprocessing configuration
61    pub preprocessing: PreprocessorConfig,
62}
63
64/// Architecture configuration
65#[derive(Debug, Clone)]
66pub struct ArchitectureConfiguration {
67    /// Network layers
68    pub layers: Vec<LayerConfig>,
69
70    /// Quantum circuit configuration
71    pub quantum_config: QuantumCircuitConfig,
72
73    /// Hybrid configuration
74    pub hybrid_config: Option<HybridConfiguration>,
75}
76
77/// Layer configuration
78#[derive(Debug, Clone)]
79pub struct LayerConfig {
80    /// Layer type
81    pub layer_type: String,
82
83    /// Layer size
84    pub size: usize,
85
86    /// Activation function
87    pub activation: String,
88}
89
90/// Quantum circuit configuration
91#[derive(Debug, Clone)]
92pub struct QuantumCircuitConfig {
93    /// Number of qubits
94    pub num_qubits: usize,
95
96    /// Circuit depth
97    pub depth: usize,
98
99    /// Gate sequence
100    pub gates: Vec<String>,
101
102    /// Entanglement pattern
103    pub entanglement: String,
104}
105
106/// Hybrid configuration
107#[derive(Debug, Clone)]
108pub struct HybridConfiguration {
109    /// Quantum-classical split
110    pub quantum_classical_split: f64,
111
112    /// Interface method
113    pub interface_method: String,
114
115    /// Synchronization strategy
116    pub synchronization_strategy: String,
117}
118
119/// Preprocessor configuration
120#[derive(Debug, Clone)]
121pub struct PreprocessorConfig {
122    /// Scaling method
123    pub scaling: String,
124
125    /// Feature selection
126    pub feature_selection: Option<String>,
127
128    /// Quantum encoding
129    pub quantum_encoding: String,
130}
131
132/// Resource requirements
133#[derive(Debug, Clone)]
134pub struct ResourceRequirements {
135    /// Computational complexity
136    pub computational_complexity: f64,
137
138    /// Memory requirements
139    pub memory_requirements: f64,
140
141    /// Quantum resource requirements
142    pub quantum_requirements: QuantumResourceRequirements,
143
144    /// Training time estimate
145    pub training_time_estimate: f64,
146}
147
148/// Quantum resource requirements
149#[derive(Debug, Clone)]
150pub struct QuantumResourceRequirements {
151    /// Required qubits
152    pub required_qubits: usize,
153
154    /// Required circuit depth
155    pub required_circuit_depth: usize,
156
157    /// Required coherence time
158    pub required_coherence_time: f64,
159
160    /// Required gate fidelity
161    pub required_gate_fidelity: f64,
162}
163
164/// Model selection strategy
165#[derive(Debug, Clone)]
166pub enum ModelSelectionStrategy {
167    BestPerformance,
168    ParetoOptimal,
169    ResourceConstrained,
170    QuantumAdvantage,
171    EnsembleBased,
172    MetaLearning,
173}
174
175/// Model performance estimator
176#[derive(Debug, Clone)]
177pub struct ModelPerformanceEstimator {
178    /// Estimation method
179    method: PerformanceEstimationMethod,
180
181    /// Historical performance data
182    performance_database: HashMap<String, f64>,
183}
184
185/// Performance estimation methods
186#[derive(Debug, Clone)]
187pub enum PerformanceEstimationMethod {
188    HistoricalData,
189    MetaLearning,
190    TheoreticalAnalysis,
191    QuickValidation,
192}
193
194impl QuantumModelSelector {
195    /// Create a new model selector
196    pub fn new(algorithm_space: &AlgorithmSearchSpace) -> Self {
197        let mut model_candidates = Vec::new();
198
199        // Add quantum neural networks if enabled
200        if algorithm_space.quantum_neural_networks {
201            model_candidates.push(ModelCandidate {
202                model_type: ModelType::QuantumNeuralNetwork,
203                configuration: ModelConfiguration::default_qnn(),
204                estimated_performance: 0.8,
205                resource_requirements: ResourceRequirements::moderate(),
206            });
207        }
208
209        // Add quantum SVM if enabled
210        if algorithm_space.quantum_svm {
211            model_candidates.push(ModelCandidate {
212                model_type: ModelType::QuantumSupportVectorMachine,
213                configuration: ModelConfiguration::default_qsvm(),
214                estimated_performance: 0.75,
215                resource_requirements: ResourceRequirements::low(),
216            });
217        }
218
219        // Add other quantum algorithms
220        if algorithm_space.quantum_clustering {
221            model_candidates.push(ModelCandidate {
222                model_type: ModelType::QuantumClustering,
223                configuration: ModelConfiguration::default_clustering(),
224                estimated_performance: 0.7,
225                resource_requirements: ResourceRequirements::moderate(),
226            });
227        }
228
229        Self {
230            model_candidates,
231            selection_strategy: ModelSelectionStrategy::BestPerformance,
232            performance_estimator: ModelPerformanceEstimator::new(),
233        }
234    }
235
236    /// Select the best model for a given task
237    pub fn select_model(&self, task_type: &MLTaskType) -> Result<ModelCandidate> {
238        let suitable_candidates = self.filter_candidates_by_task(task_type);
239
240        if suitable_candidates.is_empty() {
241            return Err(crate::error::MLError::InvalidParameter(
242                "No suitable model candidates found".to_string(),
243            ));
244        }
245
246        match self.selection_strategy {
247            ModelSelectionStrategy::BestPerformance => Ok(suitable_candidates
248                .into_iter()
249                .max_by(|a, b| {
250                    a.estimated_performance
251                        .partial_cmp(&b.estimated_performance)
252                        .unwrap()
253                })
254                .unwrap()
255                .clone()),
256            ModelSelectionStrategy::ResourceConstrained => Ok(suitable_candidates
257                .into_iter()
258                .min_by(|a, b| {
259                    a.resource_requirements
260                        .computational_complexity
261                        .partial_cmp(&b.resource_requirements.computational_complexity)
262                        .unwrap()
263                })
264                .unwrap()
265                .clone()),
266            _ => {
267                // Default to best performance
268                Ok(suitable_candidates
269                    .into_iter()
270                    .max_by(|a, b| {
271                        a.estimated_performance
272                            .partial_cmp(&b.estimated_performance)
273                            .unwrap()
274                    })
275                    .unwrap()
276                    .clone())
277            }
278        }
279    }
280
281    /// Get all available model candidates
282    pub fn get_candidates(&self) -> &[ModelCandidate] {
283        &self.model_candidates
284    }
285
286    /// Update model performance estimates
287    pub fn update_performance_estimates(&mut self, performance_data: HashMap<String, f64>) {
288        self.performance_estimator
289            .performance_database
290            .extend(performance_data);
291    }
292
293    // Private methods
294
295    fn filter_candidates_by_task(&self, task_type: &MLTaskType) -> Vec<&ModelCandidate> {
296        self.model_candidates
297            .iter()
298            .filter(|candidate| self.is_suitable_for_task(&candidate.model_type, task_type))
299            .collect()
300    }
301
302    fn is_suitable_for_task(&self, model_type: &ModelType, task_type: &MLTaskType) -> bool {
303        match (model_type, task_type) {
304            (ModelType::QuantumNeuralNetwork, _) => true, // QNNs are versatile
305            (ModelType::QuantumSupportVectorMachine, MLTaskType::BinaryClassification) => true,
306            (ModelType::QuantumSupportVectorMachine, MLTaskType::MultiClassification { .. }) => {
307                true
308            }
309            (ModelType::QuantumClustering, MLTaskType::Clustering { .. }) => true,
310            (
311                ModelType::QuantumDimensionalityReduction,
312                MLTaskType::DimensionalityReduction { .. },
313            ) => true,
314            (ModelType::QuantumTimeSeries, MLTaskType::TimeSeriesForecasting { .. }) => true,
315            (ModelType::QuantumAnomalyDetection, MLTaskType::AnomalyDetection) => true,
316            (ModelType::EnsembleModel, _) => true, // Ensembles are always suitable
317            _ => false,
318        }
319    }
320}
321
322impl ModelConfiguration {
323    fn default_qnn() -> Self {
324        Self {
325            architecture: ArchitectureConfiguration {
326                layers: vec![
327                    LayerConfig {
328                        layer_type: "quantum".to_string(),
329                        size: 4,
330                        activation: "none".to_string(),
331                    },
332                    LayerConfig {
333                        layer_type: "classical".to_string(),
334                        size: 10,
335                        activation: "relu".to_string(),
336                    },
337                ],
338                quantum_config: QuantumCircuitConfig {
339                    num_qubits: 4,
340                    depth: 3,
341                    gates: vec!["RY".to_string(), "CNOT".to_string()],
342                    entanglement: "linear".to_string(),
343                },
344                hybrid_config: Some(HybridConfiguration {
345                    quantum_classical_split: 0.5,
346                    interface_method: "measurement".to_string(),
347                    synchronization_strategy: "sequential".to_string(),
348                }),
349            },
350            hyperparameters: {
351                let mut params = HashMap::new();
352                params.insert("learning_rate".to_string(), 0.01);
353                params.insert("batch_size".to_string(), 32.0);
354                params
355            },
356            preprocessing: PreprocessorConfig {
357                scaling: "standard".to_string(),
358                feature_selection: None,
359                quantum_encoding: "angle".to_string(),
360            },
361        }
362    }
363
364    fn default_qsvm() -> Self {
365        Self {
366            architecture: ArchitectureConfiguration {
367                layers: vec![],
368                quantum_config: QuantumCircuitConfig {
369                    num_qubits: 8,
370                    depth: 2,
371                    gates: vec!["H".to_string(), "CNOT".to_string()],
372                    entanglement: "full".to_string(),
373                },
374                hybrid_config: None,
375            },
376            hyperparameters: {
377                let mut params = HashMap::new();
378                params.insert("C".to_string(), 1.0);
379                params.insert("gamma".to_string(), 0.1);
380                params
381            },
382            preprocessing: PreprocessorConfig {
383                scaling: "minmax".to_string(),
384                feature_selection: Some("variance".to_string()),
385                quantum_encoding: "amplitude".to_string(),
386            },
387        }
388    }
389
390    fn default_clustering() -> Self {
391        Self {
392            architecture: ArchitectureConfiguration {
393                layers: vec![],
394                quantum_config: QuantumCircuitConfig {
395                    num_qubits: 6,
396                    depth: 4,
397                    gates: vec!["RX".to_string(), "RZ".to_string(), "CNOT".to_string()],
398                    entanglement: "circular".to_string(),
399                },
400                hybrid_config: None,
401            },
402            hyperparameters: {
403                let mut params = HashMap::new();
404                params.insert("num_clusters".to_string(), 3.0);
405                params.insert("max_iter".to_string(), 100.0);
406                params
407            },
408            preprocessing: PreprocessorConfig {
409                scaling: "robust".to_string(),
410                feature_selection: None,
411                quantum_encoding: "basis".to_string(),
412            },
413        }
414    }
415}
416
417impl ResourceRequirements {
418    fn low() -> Self {
419        Self {
420            computational_complexity: 1.0,
421            memory_requirements: 100.0, // MB
422            quantum_requirements: QuantumResourceRequirements {
423                required_qubits: 4,
424                required_circuit_depth: 10,
425                required_coherence_time: 50.0,
426                required_gate_fidelity: 0.99,
427            },
428            training_time_estimate: 300.0, // seconds
429        }
430    }
431
432    fn moderate() -> Self {
433        Self {
434            computational_complexity: 5.0,
435            memory_requirements: 500.0, // MB
436            quantum_requirements: QuantumResourceRequirements {
437                required_qubits: 8,
438                required_circuit_depth: 20,
439                required_coherence_time: 100.0,
440                required_gate_fidelity: 0.995,
441            },
442            training_time_estimate: 900.0, // seconds
443        }
444    }
445
446    fn high() -> Self {
447        Self {
448            computational_complexity: 10.0,
449            memory_requirements: 2000.0, // MB
450            quantum_requirements: QuantumResourceRequirements {
451                required_qubits: 16,
452                required_circuit_depth: 50,
453                required_coherence_time: 200.0,
454                required_gate_fidelity: 0.999,
455            },
456            training_time_estimate: 3600.0, // seconds
457        }
458    }
459}
460
461impl ModelPerformanceEstimator {
462    fn new() -> Self {
463        Self {
464            method: PerformanceEstimationMethod::HistoricalData,
465            performance_database: HashMap::new(),
466        }
467    }
468}
quantrs2_ml/automl/search/model_selector.rs

quantrs2_ml/automl/search/
model_selector.rs
