quantrs2_device/quantum_ml_integration/
functions.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5use quantrs2_circuit::prelude::*;
6use quantrs2_core::{
7    error::{QuantRS2Error, QuantRS2Result},
8    gate::GateOp,
9    qubit::QubitId,
10};
11use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2};
12use scirs2_core::Complex64;
13use serde::{Deserialize, Serialize};
14use std::collections::{HashMap, VecDeque};
15use std::sync::{Arc, Mutex, RwLock};
16use std::time::{Duration, Instant, SystemTime};
17use tokio::sync::mpsc;
18
19// Import types from sibling modules (now merged into types)
20use super::types::*;
21
22#[cfg(feature = "scirs2")]
23use scirs2_linalg::{det, eig, inv, matrix_norm, qr, svd};
24#[cfg(feature = "scirs2")]
25use scirs2_optimize::{differential_evolution, minimize, OptimizeResult};
26#[cfg(feature = "scirs2")]
27use scirs2_stats::{corrcoef, mean, pearsonr, spearmanr, std};
28#[cfg(not(feature = "scirs2"))]
29mod fallback_scirs2 {
30    use scirs2_core::ndarray::{Array1, Array2};
31    pub fn mean(_data: &Array1<f64>) -> Result<f64, String> {
32        Ok(0.0)
33    }
34    pub fn std(_data: &Array1<f64>, _ddof: i32) -> Result<f64, String> {
35        Ok(1.0)
36    }
37    pub struct OptimizeResult {
38        pub x: Array1<f64>,
39        pub fun: f64,
40        pub success: bool,
41    }
42    pub fn minimize(
43        _func: fn(&Array1<f64>) -> f64,
44        _x0: &Array1<f64>,
45    ) -> Result<OptimizeResult, String> {
46        Ok(OptimizeResult {
47            x: Array1::zeros(2),
48            fun: 0.0,
49            success: true,
50        })
51    }
52}
53use crate::{
54    backend_traits::{query_backend_capabilities, BackendCapabilities},
55    calibration::{CalibrationManager, DeviceCalibration},
56    circuit_integration::{ExecutionResult, UniversalCircuitInterface},
57    topology::HardwareTopology,
58    vqa_support::{VQAConfig, VQAExecutor, VQAResult},
59    DeviceError, DeviceResult,
60};
61#[cfg(not(feature = "scirs2"))]
62use fallback_scirs2::*;
63/// QML optimizer trait
64pub trait QMLOptimizer: Send + Sync {
65    /// Compute gradients
66    fn compute_gradients(&self, model: &QMLModel, data: &QMLDataBatch)
67        -> DeviceResult<Array1<f64>>;
68    /// Update parameters
69    fn update_parameters(
70        &mut self,
71        model: &mut QMLModel,
72        gradients: &Array1<f64>,
73    ) -> DeviceResult<()>;
74    /// Get optimizer state
75    fn get_state(&self) -> OptimizerState;
76    /// Set optimizer state
77    fn set_state(&mut self, state: OptimizerState) -> DeviceResult<()>;
78}
79/// Anomaly detector trait
80pub trait AnomalyDetector: Send + Sync {
81    /// Detect anomalies in data
82    fn detect(&self, data: &[(Instant, f64)]) -> Vec<DetectedAnomaly>;
83    /// Update detection model
84    fn update(&mut self, data: &[(Instant, f64)]);
85    /// Get detection threshold
86    fn threshold(&self) -> f64;
87    /// Set detection threshold
88    fn set_threshold(&mut self, threshold: f64);
89}
90/// Notification channel trait
91pub trait NotificationChannel: Send + Sync {
92    /// Send notification
93    fn send_notification(&self, alert: &ActiveAlert) -> DeviceResult<()>;
94    /// Channel type
95    fn channel_type(&self) -> QMLAlertChannel;
96}
97/// QML data source trait
98pub trait QMLDataSource: Send + Sync {
99    /// Load data
100    fn load_data(&self, config: &HashMap<String, String>) -> DeviceResult<QMLDataset>;
101    /// Data source info
102    fn info(&self) -> DataSourceInfo;
103}
104/// QML data processor trait
105pub trait QMLDataProcessor: Send + Sync {
106    /// Process data
107    fn process(&self, data: &QMLDataset) -> DeviceResult<QMLDataset>;
108    /// Processor info
109    fn info(&self) -> DataProcessorInfo;
110}
111/// Framework bridge implementation trait
112pub trait FrameworkBridgeImpl: Send + Sync {
113    /// Convert from framework format
114    fn from_framework(&self, data: &[u8]) -> DeviceResult<QMLModel>;
115    /// Convert to framework format
116    fn to_framework(&self, model: &QMLModel) -> DeviceResult<Vec<u8>>;
117    /// Execute in framework
118    fn execute(&self, model: &QMLModel, data: &QMLDataBatch) -> DeviceResult<Array1<f64>>;
119    /// Get framework info
120    fn info(&self) -> FrameworkInfo;
121}
122/// Create a default QML integration hub
123pub fn create_qml_integration_hub() -> DeviceResult<QuantumMLIntegrationHub> {
124    QuantumMLIntegrationHub::new(QMLIntegrationConfig::default())
125}
126/// Create a high-performance QML configuration
127pub fn create_high_performance_qml_config() -> QMLIntegrationConfig {
128    QMLIntegrationConfig {
129        enable_qnn: true,
130        enable_hybrid_training: true,
131        enable_autodiff: true,
132        enabled_frameworks: vec![
133            MLFramework::TensorFlow,
134            MLFramework::PyTorch,
135            MLFramework::PennyLane,
136            MLFramework::JAX,
137        ],
138        training_config: QMLTrainingConfig {
139            max_epochs: 500,
140            learning_rate: 0.001,
141            batch_size: 64,
142            early_stopping: EarlyStoppingConfig {
143                enabled: true,
144                patience: 20,
145                min_delta: 1e-6,
146                monitor_metric: "val_loss".to_string(),
147                mode: ImprovementMode::Minimize,
148            },
149            gradient_method: GradientMethod::Adjoint,
150            loss_function: LossFunction::MeanSquaredError,
151            regularization: RegularizationConfig {
152                l1_lambda: 0.001,
153                l2_lambda: 0.01,
154                dropout_rate: 0.2,
155                quantum_noise: 0.01,
156                parameter_constraints: ParameterConstraints {
157                    min_value: Some(-std::f64::consts::PI),
158                    max_value: Some(std::f64::consts::PI),
159                    enforce_unitarity: true,
160                    enforce_hermiticity: false,
161                    custom_constraints: Vec::new(),
162                },
163            },
164            validation_config: ValidationConfig {
165                validation_split: 0.15,
166                cv_folds: Some(5),
167                validation_frequency: 1,
168                enable_test_evaluation: true,
169            },
170        },
171        optimization_config: QMLOptimizationConfig {
172            optimizer_type: OptimizerType::Adam,
173            optimizer_params: [
174                ("beta1".to_string(), 0.9),
175                ("beta2".to_string(), 0.999),
176                ("epsilon".to_string(), 1e-8),
177            ]
178            .iter()
179            .cloned()
180            .collect(),
181            enable_parameter_sharing: true,
182            circuit_optimization: CircuitOptimizationConfig {
183                enable_gate_fusion: true,
184                enable_compression: true,
185                max_depth: Some(100),
186                allowed_gates: None,
187                topology_aware: true,
188            },
189            hardware_aware: true,
190            multi_objective: MultiObjectiveConfig {
191                enabled: true,
192                objective_weights: [
193                    ("accuracy".to_string(), 0.4),
194                    ("speed".to_string(), 0.3),
195                    ("resource_efficiency".to_string(), 0.2),
196                    ("cost".to_string(), 0.1),
197                ]
198                .iter()
199                .cloned()
200                .collect(),
201                pareto_exploration: true,
202                constraint_handling: ConstraintHandling::Adaptive,
203            },
204        },
205        resource_config: QMLResourceConfig {
206            max_circuits_per_step: 5000,
207            memory_limit_mb: 32768,
208            parallel_config: ParallelExecutionConfig {
209                enable_parallel_circuits: true,
210                max_workers: 16,
211                batch_processing: BatchProcessingConfig {
212                    dynamic_batch_size: true,
213                    min_batch_size: 16,
214                    max_batch_size: 512,
215                    adaptation_strategy: BatchAdaptationStrategy::Performance,
216                },
217                load_balancing: crate::quantum_ml_integration::LoadBalancingStrategy::Performance,
218            },
219            caching_strategy: CachingStrategy::Adaptive,
220            resource_priorities: ResourcePriorities {
221                weights: [
222                    ("quantum".to_string(), 0.5),
223                    ("classical".to_string(), 0.25),
224                    ("memory".to_string(), 0.15),
225                    ("network".to_string(), 0.1),
226                ]
227                .iter()
228                .cloned()
229                .collect(),
230                dynamic_adjustment: true,
231                performance_reallocation: true,
232            },
233        },
234        monitoring_config: QMLMonitoringConfig {
235            enable_monitoring: true,
236            collection_frequency: Duration::from_secs(10),
237            performance_tracking: PerformanceTrackingConfig {
238                track_training_metrics: true,
239                track_inference_metrics: true,
240                track_circuit_metrics: true,
241                aggregation_window: Duration::from_secs(60),
242                enable_trend_analysis: true,
243            },
244            resource_monitoring: ResourceMonitoringConfig {
245                monitor_quantum_resources: true,
246                monitor_classical_resources: true,
247                monitor_memory: true,
248                monitor_network: true,
249                usage_thresholds: [
250                    ("cpu".to_string(), 0.9),
251                    ("memory".to_string(), 0.9),
252                    ("quantum".to_string(), 0.95),
253                ]
254                .iter()
255                .cloned()
256                .collect(),
257            },
258            alert_config: AlertConfig {
259                enabled: true,
260                thresholds: [
261                    ("error_rate".to_string(), 0.05),
262                    ("resource_usage".to_string(), 0.95),
263                    ("cost_spike".to_string(), 3.0),
264                ]
265                .iter()
266                .cloned()
267                .collect(),
268                channels: vec![QMLAlertChannel::Log, QMLAlertChannel::Email],
269                escalation: AlertEscalation {
270                    enabled: true,
271                    levels: vec![
272                        EscalationLevel {
273                            name: "Warning".to_string(),
274                            threshold_multiplier: 1.0,
275                            channels: vec![QMLAlertChannel::Log],
276                            actions: vec![EscalationAction::Notify],
277                        },
278                        EscalationLevel {
279                            name: "Critical".to_string(),
280                            threshold_multiplier: 2.0,
281                            channels: vec![QMLAlertChannel::Log, QMLAlertChannel::Email],
282                            actions: vec![EscalationAction::Notify, EscalationAction::Throttle],
283                        },
284                        EscalationLevel {
285                            name: "Emergency".to_string(),
286                            threshold_multiplier: 5.0,
287                            channels: vec![
288                                QMLAlertChannel::Log,
289                                QMLAlertChannel::Email,
290                                QMLAlertChannel::SMS,
291                            ],
292                            actions: vec![EscalationAction::Notify, EscalationAction::Pause],
293                        },
294                    ],
295                    timeouts: [
296                        ("warning".to_string(), Duration::from_secs(180)),
297                        ("critical".to_string(), Duration::from_secs(60)),
298                        ("emergency".to_string(), Duration::from_secs(30)),
299                    ]
300                    .iter()
301                    .cloned()
302                    .collect(),
303                },
304            },
305        },
306    }
307}
308#[cfg(test)]
309mod tests {
310    use super::*;
311    #[test]
312    fn test_qml_config_default() {
313        let config = QMLIntegrationConfig::default();
314        assert!(config.enable_qnn);
315        assert!(config.enable_hybrid_training);
316        assert!(config.enable_autodiff);
317        assert!(!config.enabled_frameworks.is_empty());
318    }
319    #[test]
320    fn test_qml_hub_creation() {
321        let config = QMLIntegrationConfig::default();
322        let hub = QuantumMLIntegrationHub::new(config);
323        assert!(hub.is_ok());
324    }
325    #[test]
326    fn test_high_performance_config() {
327        let config = create_high_performance_qml_config();
328        assert_eq!(config.training_config.max_epochs, 500);
329        assert_eq!(config.resource_config.max_circuits_per_step, 5000);
330        assert!(config.optimization_config.multi_objective.enabled);
331    }
332    #[test]
333    fn test_training_priority_ordering() {
334        assert!(TrainingPriority::Low < TrainingPriority::Normal);
335        assert!(TrainingPriority::Normal < TrainingPriority::High);
336        assert!(TrainingPriority::High < TrainingPriority::Critical);
337    }
338    #[test]
339    fn test_qml_model_type_serialization() {
340        let model_type = QMLModelType::QuantumNeuralNetwork;
341        let serialized =
342            serde_json::to_string(&model_type).expect("QMLModelType serialization should succeed");
343        let deserialized: QMLModelType =
344            serde_json::from_str(&serialized).expect("QMLModelType deserialization should succeed");
345        assert_eq!(model_type, deserialized);
346    }
347    #[tokio::test]
348    async fn test_qml_hub_model_registration() {
349        let hub = create_qml_integration_hub()
350            .expect("QML integration hub creation should succeed with default config");
351        let model = QMLModel {
352            model_id: "test_model".to_string(),
353            model_type: QMLModelType::QuantumClassifier,
354            architecture: QMLArchitecture {
355                num_qubits: 4,
356                layers: Vec::new(),
357                measurement_strategy: MeasurementStrategy::Computational,
358                entanglement_pattern: EntanglementPattern::Linear,
359                classical_components: Vec::new(),
360            },
361            parameters: QMLParameters {
362                quantum_params: Array1::zeros(10),
363                classical_params: Array1::zeros(5),
364                parameter_bounds: Vec::new(),
365                trainable_mask: Array1::from_elem(15, true),
366                gradients: None,
367                parameter_history: VecDeque::new(),
368            },
369            training_state: QMLTrainingState {
370                current_epoch: 0,
371                training_loss: 1.0,
372                validation_loss: None,
373                learning_rate: 0.01,
374                optimizer_state: OptimizerState {
375                    optimizer_type: OptimizerType::Adam,
376                    momentum: None,
377                    velocity: None,
378                    second_moment: None,
379                    accumulated_gradients: None,
380                    step_count: 0,
381                },
382                training_history: TrainingHistory {
383                    loss_history: Vec::new(),
384                    val_loss_history: Vec::new(),
385                    metric_history: HashMap::new(),
386                    lr_history: Vec::new(),
387                    gradient_norm_history: Vec::new(),
388                    parameter_norm_history: Vec::new(),
389                },
390                early_stopping_state: EarlyStoppingState {
391                    best_metric: f64::INFINITY,
392                    patience_counter: 0,
393                    best_parameters: None,
394                    should_stop: false,
395                },
396            },
397            performance_metrics: QMLPerformanceMetrics {
398                training_metrics: HashMap::new(),
399                validation_metrics: HashMap::new(),
400                test_metrics: HashMap::new(),
401                circuit_metrics: CircuitExecutionMetrics {
402                    avg_circuit_depth: 10.0,
403                    total_gate_count: 100,
404                    avg_execution_time: Duration::from_millis(100),
405                    circuit_fidelity: 0.95,
406                    shot_efficiency: 0.9,
407                },
408                resource_metrics: ResourceUtilizationMetrics {
409                    quantum_usage: 0.8,
410                    classical_usage: 0.6,
411                    memory_usage: 0.4,
412                    network_usage: 0.2,
413                    cost_efficiency: 0.7,
414                },
415                convergence_metrics: ConvergenceMetrics {
416                    convergence_rate: 0.1,
417                    stability: 0.9,
418                    plateau_detected: false,
419                    oscillation: 0.1,
420                    final_gradient_norm: 0.01,
421                },
422            },
423            metadata: QMLModelMetadata {
424                created_at: SystemTime::now(),
425                updated_at: SystemTime::now(),
426                version: "1.0.0".to_string(),
427                author: "test".to_string(),
428                description: "Test QML model".to_string(),
429                tags: vec!["test".to_string()],
430                framework: MLFramework::Custom("test".to_string()),
431                hardware_requirements: crate::quantum_ml_integration::types::HardwareRequirements {
432                    min_qubits: 4,
433                    required_gates: vec!["H".to_string(), "CNOT".to_string()],
434                    connectivity_requirements: ConnectivityRequirements {
435                        connectivity_graph: vec![(0, 1), (1, 2), (2, 3)],
436                        min_connectivity: 2,
437                        topology_constraints: vec![TopologyConstraint::Linear],
438                    },
439                    performance_requirements: PerformanceRequirements {
440                        min_gate_fidelity: 0.95,
441                        max_execution_time: Duration::from_secs(60),
442                        min_coherence_time: Duration::from_micros(100),
443                        max_error_rate: 0.01,
444                    },
445                },
446            },
447        };
448        let result = hub.register_model(model);
449        assert!(result.is_ok());
450    }
451}
quantrs2_device/quantum_ml_integration/functions.rs

quantrs2_device/quantum_ml_integration/
functions.rs
