quantrs2_device/quantum_ml/
mod.rs

1//! Quantum Machine Learning Accelerators
2//!
3//! This module provides quantum machine learning acceleration capabilities,
4//! integrating variational quantum algorithms, quantum neural networks,
5//! and hybrid quantum-classical optimization routines.
6
7use crate::{CircuitExecutor, CircuitResult, DeviceError, DeviceResult, QuantumDevice};
8use serde::{Deserialize, Serialize};
9use std::collections::HashMap;
10use std::sync::Arc;
11use std::time::Duration;
12use tokio::sync::RwLock;
13
14pub mod classical_integration;
15pub mod gradients;
16pub mod hardware_acceleration;
17pub mod inference;
18pub mod optimization;
19pub mod quantum_neural_networks;
20pub mod training;
21pub mod variational_algorithms;
22
23pub use classical_integration::*;
24pub use gradients::*;
25pub use hardware_acceleration::*;
26pub use inference::*;
27pub use optimization::*;
28pub use quantum_neural_networks::*;
29pub use training::*;
30pub use variational_algorithms::*;
31
32/// Quantum Machine Learning Accelerator
33pub struct QMLAccelerator {
34    /// Quantum device backend
35    pub device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
36    /// QML configuration
37    pub config: QMLConfig,
38    /// Training history
39    pub training_history: Vec<TrainingEpoch>,
40    /// Model registry
41    pub model_registry: ModelRegistry,
42    /// Hardware acceleration manager
43    pub hardware_manager: HardwareAccelerationManager,
44    /// Connection status
45    pub is_connected: bool,
46}
47
48/// Configuration for QML accelerator
49#[derive(Debug, Clone, Serialize, Deserialize)]
50pub struct QMLConfig {
51    /// Maximum number of qubits
52    pub max_qubits: usize,
53    /// Optimization algorithm
54    pub optimizer: OptimizerType,
55    /// Learning rate
56    pub learning_rate: f64,
57    /// Maximum training epochs
58    pub max_epochs: usize,
59    /// Convergence tolerance
60    pub convergence_tolerance: f64,
61    /// Batch size for hybrid training
62    pub batch_size: usize,
63    /// Enable hardware acceleration
64    pub enable_hardware_acceleration: bool,
65    /// Gradient computation method
66    pub gradient_method: GradientMethod,
67    /// Noise resilience level
68    pub noise_resilience: NoiseResilienceLevel,
69    /// Circuit depth limit
70    pub max_circuit_depth: usize,
71    /// Parameter update frequency
72    pub parameter_update_frequency: usize,
73}
74
75impl Default for QMLConfig {
76    fn default() -> Self {
77        Self {
78            max_qubits: 20,
79            optimizer: OptimizerType::Adam,
80            learning_rate: 0.01,
81            max_epochs: 1000,
82            convergence_tolerance: 1e-6,
83            batch_size: 32,
84            enable_hardware_acceleration: true,
85            gradient_method: GradientMethod::ParameterShift,
86            noise_resilience: NoiseResilienceLevel::Medium,
87            max_circuit_depth: 100,
88            parameter_update_frequency: 10,
89        }
90    }
91}
92
93/// Types of optimizers for QML
94#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
95pub enum OptimizerType {
96    /// Gradient descent
97    GradientDescent,
98    /// Adam optimizer
99    Adam,
100    /// AdaGrad optimizer
101    AdaGrad,
102    /// RMSprop optimizer
103    RMSprop,
104    /// Simultaneous Perturbation Stochastic Approximation
105    SPSA,
106    /// Quantum Natural Gradient
107    QuantumNaturalGradient,
108    /// Nelder-Mead
109    NelderMead,
110    /// COBYLA (Constrained Optimization BY Linear Approximation)
111    COBYLA,
112}
113
114/// Gradient computation methods
115#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
116pub enum GradientMethod {
117    /// Parameter shift rule
118    ParameterShift,
119    /// Finite differences
120    FiniteDifference,
121    /// Linear combination of unitaries
122    LinearCombination,
123    /// Quantum natural gradient
124    QuantumNaturalGradient,
125    /// Adjoint method
126    Adjoint,
127}
128
129/// Noise resilience levels
130#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
131pub enum NoiseResilienceLevel {
132    Low,
133    Medium,
134    High,
135    Adaptive,
136}
137
138/// Training epoch information
139#[derive(Debug, Clone, Serialize, Deserialize)]
140pub struct TrainingEpoch {
141    pub epoch: usize,
142    pub loss: f64,
143    pub accuracy: Option<f64>,
144    pub parameters: Vec<f64>,
145    pub gradient_norm: f64,
146    pub learning_rate: f64,
147    pub execution_time: Duration,
148    pub quantum_fidelity: Option<f64>,
149    pub classical_preprocessing_time: Duration,
150    pub quantum_execution_time: Duration,
151}
152
153/// QML model types
154#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
155pub enum QMLModelType {
156    /// Variational Quantum Classifier
157    VQC,
158    /// Quantum Neural Network
159    QNN,
160    /// Quantum Approximate Optimization Algorithm
161    QAOA,
162    /// Variational Quantum Eigensolver
163    VQE,
164    /// Quantum Generative Adversarial Network
165    QGAN,
166    /// Quantum Convolutional Neural Network
167    QCNN,
168    /// Hybrid Classical-Quantum Network
169    HybridNetwork,
170}
171
172impl QMLAccelerator {
173    /// Create a new QML accelerator
174    pub fn new(
175        device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
176        config: QMLConfig,
177    ) -> DeviceResult<Self> {
178        let model_registry = ModelRegistry::new();
179        let hardware_manager = HardwareAccelerationManager::new(&config)?;
180
181        Ok(Self {
182            device,
183            config,
184            training_history: Vec::new(),
185            model_registry,
186            hardware_manager,
187            is_connected: false,
188        })
189    }
190
191    /// Connect to quantum hardware
192    pub async fn connect(&mut self) -> DeviceResult<()> {
193        let device = self.device.read().await;
194        if !device.is_available().await? {
195            return Err(DeviceError::DeviceNotInitialized(
196                "Quantum device not available".to_string(),
197            ));
198        }
199
200        self.hardware_manager.initialize().await?;
201        self.is_connected = true;
202        Ok(())
203    }
204
205    /// Disconnect from hardware
206    pub async fn disconnect(&mut self) -> DeviceResult<()> {
207        self.hardware_manager.shutdown().await?;
208        self.is_connected = false;
209        Ok(())
210    }
211
212    /// Train a quantum machine learning model
213    pub async fn train_model(
214        &mut self,
215        model_type: QMLModelType,
216        training_data: training::TrainingData,
217        validation_data: Option<training::TrainingData>,
218    ) -> DeviceResult<training::TrainingResult> {
219        if !self.is_connected {
220            return Err(DeviceError::DeviceNotInitialized(
221                "QML accelerator not connected".to_string(),
222            ));
223        }
224
225        let mut trainer = QuantumTrainer::new(self.device.clone(), &self.config, model_type)?;
226
227        let result = trainer
228            .train(training_data, validation_data, &mut self.training_history)
229            .await?;
230
231        // Register the trained model
232        self.model_registry
233            .register_model(result.model_id.clone(), result.model.clone())?;
234
235        Ok(result)
236    }
237
238    /// Perform inference with a trained model
239    pub async fn inference(
240        &self,
241        model_id: &str,
242        input_data: InferenceData,
243    ) -> DeviceResult<InferenceResult> {
244        if !self.is_connected {
245            return Err(DeviceError::DeviceNotInitialized(
246                "QML accelerator not connected".to_string(),
247            ));
248        }
249
250        let model = self.model_registry.get_model(model_id)?;
251        let inference_engine = QuantumInferenceEngine::new(self.device.clone(), &self.config)?;
252
253        inference_engine.inference(model, input_data).await
254    }
255
256    /// Optimize quantum circuit parameters
257    pub async fn optimize_parameters(
258        &mut self,
259        initial_parameters: Vec<f64>,
260        objective_function: Box<dyn ObjectiveFunction + Send + Sync>,
261    ) -> DeviceResult<OptimizationResult> {
262        let mut optimizer =
263            create_gradient_optimizer(self.device.clone(), OptimizerType::Adam, 0.01);
264
265        optimizer.optimize(initial_parameters, objective_function)
266    }
267
268    /// Compute gradients using quantum methods
269    pub async fn compute_gradients(
270        &self,
271        circuit: ParameterizedQuantumCircuit,
272        parameters: Vec<f64>,
273    ) -> DeviceResult<Vec<f64>> {
274        let gradient_calculator =
275            QuantumGradientCalculator::new(self.device.clone(), GradientConfig::default())?;
276
277        gradient_calculator
278            .compute_gradients(circuit, parameters)
279            .await
280    }
281
282    /// Get training statistics
283    pub fn get_training_statistics(&self) -> TrainingStatistics {
284        TrainingStatistics::from_history(&self.training_history)
285    }
286
287    /// Export trained model
288    pub async fn export_model(
289        &self,
290        model_id: &str,
291        format: ModelExportFormat,
292    ) -> DeviceResult<Vec<u8>> {
293        let model = self.model_registry.get_model(model_id)?;
294        model.export(format).await
295    }
296
297    /// Import trained model
298    pub async fn import_model(
299        &mut self,
300        model_data: Vec<u8>,
301        format: ModelExportFormat,
302    ) -> DeviceResult<String> {
303        let model = QMLModel::import(model_data, format).await?;
304        let model_id = format!("imported_model_{}", uuid::Uuid::new_v4());
305
306        self.model_registry
307            .register_model(model_id.clone(), model)?;
308        Ok(model_id)
309    }
310
311    /// Get hardware acceleration metrics
312    pub async fn get_acceleration_metrics(&self) -> HardwareAccelerationMetrics {
313        self.hardware_manager.get_metrics().await
314    }
315
316    /// Benchmark quantum vs classical performance
317    pub async fn benchmark_performance(
318        &self,
319        model_type: QMLModelType,
320        problem_size: usize,
321    ) -> DeviceResult<PerformanceBenchmark> {
322        let benchmark_engine = PerformanceBenchmarkEngine::new(self.device.clone(), &self.config)?;
323
324        benchmark_engine.benchmark(model_type, problem_size).await
325    }
326
327    /// Get QML accelerator diagnostics
328    pub async fn get_diagnostics(&self) -> QMLDiagnostics {
329        let device = self.device.read().await;
330        let device_props = device.properties().await.unwrap_or_default();
331
332        QMLDiagnostics {
333            is_connected: self.is_connected,
334            total_models: self.model_registry.model_count(),
335            training_epochs_completed: self.training_history.len(),
336            hardware_acceleration_enabled: self.config.enable_hardware_acceleration,
337            active_model_count: self.model_registry.active_model_count(),
338            average_training_time: self.calculate_average_training_time(),
339            quantum_advantage_ratio: self.hardware_manager.get_quantum_advantage_ratio().await,
340            device_properties: device_props,
341        }
342    }
343
344    fn calculate_average_training_time(&self) -> Duration {
345        if self.training_history.is_empty() {
346            return Duration::from_secs(0);
347        }
348
349        let total_time: Duration = self
350            .training_history
351            .iter()
352            .map(|epoch| epoch.execution_time)
353            .sum();
354
355        total_time / self.training_history.len() as u32
356    }
357}
358
359/// Inference data structure
360#[derive(Debug, Clone, Serialize, Deserialize)]
361pub struct InferenceData {
362    pub features: Vec<f64>,
363    pub metadata: HashMap<String, String>,
364}
365
366/// Inference result
367#[derive(Debug, Clone, Serialize, Deserialize)]
368pub struct InferenceResult {
369    pub prediction: f64,
370    pub confidence: Option<f64>,
371    pub quantum_fidelity: Option<f64>,
372    pub execution_time: Duration,
373    pub metadata: HashMap<String, String>,
374}
375
376/// QML model representation
377#[derive(Debug, Clone, Serialize, Deserialize)]
378pub struct QMLModel {
379    pub model_type: QMLModelType,
380    pub parameters: Vec<f64>,
381    pub circuit_structure: CircuitStructure,
382    pub training_metadata: HashMap<String, String>,
383    pub performance_metrics: HashMap<String, f64>,
384}
385
386impl QMLModel {
387    pub async fn export(&self, format: ModelExportFormat) -> DeviceResult<Vec<u8>> {
388        match format {
389            ModelExportFormat::JSON => serde_json::to_vec(self)
390                .map_err(|e| DeviceError::InvalidInput(format!("JSON export error: {e}"))),
391            ModelExportFormat::Binary => {
392                bincode::serde::encode_to_vec(self, bincode::config::standard())
393                    .map_err(|e| DeviceError::InvalidInput(format!("Binary export error: {e:?}")))
394            }
395            ModelExportFormat::ONNX => {
396                // Placeholder for ONNX export
397                Err(DeviceError::InvalidInput(
398                    "ONNX export not yet implemented".to_string(),
399                ))
400            }
401        }
402    }
403
404    pub async fn import(data: Vec<u8>, format: ModelExportFormat) -> DeviceResult<Self> {
405        match format {
406            ModelExportFormat::JSON => serde_json::from_slice(&data)
407                .map_err(|e| DeviceError::InvalidInput(format!("JSON import error: {e}"))),
408            ModelExportFormat::Binary => {
409                bincode::serde::decode_from_slice(&data, bincode::config::standard())
410                    .map(|(v, _consumed)| v)
411                    .map_err(|e| DeviceError::InvalidInput(format!("Binary import error: {e:?}")))
412            }
413            ModelExportFormat::ONNX => Err(DeviceError::InvalidInput(
414                "ONNX import not yet implemented".to_string(),
415            )),
416        }
417    }
418}
419
420/// Model export formats
421#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
422pub enum ModelExportFormat {
423    JSON,
424    Binary,
425    ONNX,
426}
427
428/// Circuit structure representation
429#[derive(Debug, Clone, Serialize, Deserialize)]
430pub struct CircuitStructure {
431    pub num_qubits: usize,
432    pub depth: usize,
433    pub gate_types: Vec<String>,
434    pub parameter_count: usize,
435    pub entangling_gates: usize,
436}
437
438/// Training statistics
439#[derive(Debug, Clone, Serialize, Deserialize)]
440pub struct TrainingStatistics {
441    pub total_epochs: usize,
442    pub final_loss: f64,
443    pub best_loss: f64,
444    pub average_loss: f64,
445    pub convergence_epoch: Option<usize>,
446    pub total_training_time: Duration,
447    pub average_epoch_time: Duration,
448}
449
450impl TrainingStatistics {
451    pub fn from_history(history: &[TrainingEpoch]) -> Self {
452        if history.is_empty() {
453            return Self {
454                total_epochs: 0,
455                final_loss: 0.0,
456                best_loss: f64::INFINITY,
457                average_loss: 0.0,
458                convergence_epoch: None,
459                total_training_time: Duration::from_secs(0),
460                average_epoch_time: Duration::from_secs(0),
461            };
462        }
463
464        let total_epochs = history.len();
465        // Safe to use expect here since we already verified history is not empty above
466        let final_loss = history
467            .last()
468            .expect("history should not be empty after early return check")
469            .loss;
470        let best_loss = history.iter().map(|e| e.loss).fold(f64::INFINITY, f64::min);
471        let average_loss = history.iter().map(|e| e.loss).sum::<f64>() / total_epochs as f64;
472        let total_training_time = history.iter().map(|e| e.execution_time).sum();
473        let average_epoch_time = total_training_time / total_epochs as u32;
474
475        Self {
476            total_epochs,
477            final_loss,
478            best_loss,
479            average_loss,
480            convergence_epoch: None, // Could implement convergence detection
481            total_training_time,
482            average_epoch_time,
483        }
484    }
485}
486
487/// QML diagnostics
488#[derive(Debug, Clone, Serialize, Deserialize)]
489pub struct QMLDiagnostics {
490    pub is_connected: bool,
491    pub total_models: usize,
492    pub training_epochs_completed: usize,
493    pub hardware_acceleration_enabled: bool,
494    pub active_model_count: usize,
495    pub average_training_time: Duration,
496    pub quantum_advantage_ratio: f64,
497    pub device_properties: HashMap<String, String>,
498}
499
500/// Model registry for managing trained models
501pub struct ModelRegistry {
502    models: HashMap<String, QMLModel>,
503    active_models: HashMap<String, bool>,
504}
505
506impl Default for ModelRegistry {
507    fn default() -> Self {
508        Self::new()
509    }
510}
511
512impl ModelRegistry {
513    pub fn new() -> Self {
514        Self {
515            models: HashMap::new(),
516            active_models: HashMap::new(),
517        }
518    }
519
520    pub fn register_model(&mut self, id: String, model: QMLModel) -> DeviceResult<()> {
521        self.models.insert(id.clone(), model);
522        self.active_models.insert(id, true);
523        Ok(())
524    }
525
526    pub fn get_model(&self, id: &str) -> DeviceResult<&QMLModel> {
527        self.models
528            .get(id)
529            .ok_or_else(|| DeviceError::InvalidInput(format!("Model {id} not found")))
530    }
531
532    pub fn model_count(&self) -> usize {
533        self.models.len()
534    }
535
536    pub fn active_model_count(&self) -> usize {
537        self.active_models
538            .values()
539            .filter(|&&active| active)
540            .count()
541    }
542
543    pub fn deactivate_model(&mut self, id: &str) -> DeviceResult<()> {
544        if self.active_models.contains_key(id) {
545            self.active_models.insert(id.to_string(), false);
546            Ok(())
547        } else {
548            Err(DeviceError::InvalidInput(format!("Model {id} not found")))
549        }
550    }
551}
552
553/// Create a VQC (Variational Quantum Classifier) accelerator
554pub fn create_vqc_accelerator(
555    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
556    num_qubits: usize,
557) -> DeviceResult<QMLAccelerator> {
558    let config = QMLConfig {
559        max_qubits: num_qubits,
560        optimizer: OptimizerType::Adam,
561        gradient_method: GradientMethod::ParameterShift,
562        ..Default::default()
563    };
564
565    QMLAccelerator::new(device, config)
566}
567
568/// Create a QAOA accelerator
569pub fn create_qaoa_accelerator(
570    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
571    problem_size: usize,
572) -> DeviceResult<QMLAccelerator> {
573    let config = QMLConfig {
574        max_qubits: problem_size,
575        optimizer: OptimizerType::COBYLA,
576        gradient_method: GradientMethod::FiniteDifference,
577        max_circuit_depth: 50,
578        ..Default::default()
579    };
580
581    QMLAccelerator::new(device, config)
582}
583
584#[cfg(test)]
585mod tests {
586    use super::*;
587    use crate::test_utils::*;
588
589    #[tokio::test]
590    async fn test_qml_accelerator_creation() {
591        let device = create_mock_quantum_device();
592        let accelerator = QMLAccelerator::new(device, QMLConfig::default())
593            .expect("QML accelerator creation should succeed with mock device");
594
595        assert_eq!(accelerator.config.max_qubits, 20);
596        assert!(!accelerator.is_connected);
597    }
598
599    #[tokio::test]
600    async fn test_model_registry() {
601        let mut registry = ModelRegistry::new();
602        assert_eq!(registry.model_count(), 0);
603
604        let model = QMLModel {
605            model_type: QMLModelType::VQC,
606            parameters: vec![0.1, 0.2, 0.3],
607            circuit_structure: CircuitStructure {
608                num_qubits: 4,
609                depth: 10,
610                gate_types: vec!["RY".to_string(), "CNOT".to_string()],
611                parameter_count: 8,
612                entangling_gates: 4,
613            },
614            training_metadata: HashMap::new(),
615            performance_metrics: HashMap::new(),
616        };
617
618        registry
619            .register_model("test_model".to_string(), model)
620            .expect("registering model should succeed");
621        assert_eq!(registry.model_count(), 1);
622        assert_eq!(registry.active_model_count(), 1);
623
624        let retrieved = registry
625            .get_model("test_model")
626            .expect("retrieving registered model should succeed");
627        assert_eq!(retrieved.model_type, QMLModelType::VQC);
628    }
629
630    #[test]
631    fn test_training_statistics() {
632        let history = vec![
633            TrainingEpoch {
634                epoch: 0,
635                loss: 1.0,
636                accuracy: Some(0.5),
637                parameters: vec![0.1],
638                gradient_norm: 0.5,
639                learning_rate: 0.01,
640                execution_time: Duration::from_millis(100),
641                quantum_fidelity: Some(0.95),
642                classical_preprocessing_time: Duration::from_millis(10),
643                quantum_execution_time: Duration::from_millis(90),
644            },
645            TrainingEpoch {
646                epoch: 1,
647                loss: 0.5,
648                accuracy: Some(0.7),
649                parameters: vec![0.2],
650                gradient_norm: 0.3,
651                learning_rate: 0.01,
652                execution_time: Duration::from_millis(120),
653                quantum_fidelity: Some(0.96),
654                classical_preprocessing_time: Duration::from_millis(15),
655                quantum_execution_time: Duration::from_millis(105),
656            },
657        ];
658
659        let stats = TrainingStatistics::from_history(&history);
660        assert_eq!(stats.total_epochs, 2);
661        assert_eq!(stats.final_loss, 0.5);
662        assert_eq!(stats.best_loss, 0.5);
663        assert_eq!(stats.average_loss, 0.75);
664    }
665}
quantrs2_device/quantum_ml/mod.rs

quantrs2_device/quantum_ml/
mod.rs
