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 circuit_simulation;
15pub mod classical_integration;
16pub mod gradients;
17pub mod hardware_acceleration;
18pub mod inference;
19pub mod optimization;
20pub mod quantum_neural_networks;
21pub mod training;
22pub mod variational_algorithms;
23
24pub use classical_integration::*;
25pub use gradients::*;
26pub use hardware_acceleration::*;
27pub use inference::*;
28pub use optimization::*;
29pub use quantum_neural_networks::*;
30pub use training::*;
31pub use variational_algorithms::*;
32
33/// Quantum Machine Learning Accelerator
34pub struct QMLAccelerator {
35    /// Quantum device backend
36    pub device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
37    /// QML configuration
38    pub config: QMLConfig,
39    /// Training history
40    pub training_history: Vec<TrainingEpoch>,
41    /// Model registry
42    pub model_registry: ModelRegistry,
43    /// Hardware acceleration manager
44    pub hardware_manager: HardwareAccelerationManager,
45    /// Connection status
46    pub is_connected: bool,
47}
48
49/// Configuration for QML accelerator
50#[derive(Debug, Clone, Serialize, Deserialize)]
51pub struct QMLConfig {
52    /// Maximum number of qubits
53    pub max_qubits: usize,
54    /// Optimization algorithm
55    pub optimizer: OptimizerType,
56    /// Learning rate
57    pub learning_rate: f64,
58    /// Maximum training epochs
59    pub max_epochs: usize,
60    /// Convergence tolerance
61    pub convergence_tolerance: f64,
62    /// Batch size for hybrid training
63    pub batch_size: usize,
64    /// Enable hardware acceleration
65    pub enable_hardware_acceleration: bool,
66    /// Gradient computation method
67    pub gradient_method: GradientMethod,
68    /// Noise resilience level
69    pub noise_resilience: NoiseResilienceLevel,
70    /// Circuit depth limit
71    pub max_circuit_depth: usize,
72    /// Parameter update frequency
73    pub parameter_update_frequency: usize,
74}
75
76impl Default for QMLConfig {
77    fn default() -> Self {
78        Self {
79            max_qubits: 20,
80            optimizer: OptimizerType::Adam,
81            learning_rate: 0.01,
82            max_epochs: 1000,
83            convergence_tolerance: 1e-6,
84            batch_size: 32,
85            enable_hardware_acceleration: true,
86            gradient_method: GradientMethod::ParameterShift,
87            noise_resilience: NoiseResilienceLevel::Medium,
88            max_circuit_depth: 100,
89            parameter_update_frequency: 10,
90        }
91    }
92}
93
94/// Types of optimizers for QML
95#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
96pub enum OptimizerType {
97    /// Gradient descent
98    GradientDescent,
99    /// Adam optimizer
100    Adam,
101    /// AdaGrad optimizer
102    AdaGrad,
103    /// RMSprop optimizer
104    RMSprop,
105    /// Simultaneous Perturbation Stochastic Approximation
106    SPSA,
107    /// Quantum Natural Gradient
108    QuantumNaturalGradient,
109    /// Nelder-Mead
110    NelderMead,
111    /// COBYLA (Constrained Optimization BY Linear Approximation)
112    COBYLA,
113}
114
115/// Gradient computation methods
116#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
117pub enum GradientMethod {
118    /// Parameter shift rule
119    ParameterShift,
120    /// Finite differences
121    FiniteDifference,
122    /// Linear combination of unitaries
123    LinearCombination,
124    /// Quantum natural gradient
125    QuantumNaturalGradient,
126    /// Adjoint method
127    Adjoint,
128}
129
130/// Noise resilience levels
131#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
132pub enum NoiseResilienceLevel {
133    Low,
134    Medium,
135    High,
136    Adaptive,
137}
138
139/// Training epoch information
140#[derive(Debug, Clone, Serialize, Deserialize)]
141pub struct TrainingEpoch {
142    pub epoch: usize,
143    pub loss: f64,
144    pub accuracy: Option<f64>,
145    pub parameters: Vec<f64>,
146    pub gradient_norm: f64,
147    pub learning_rate: f64,
148    pub execution_time: Duration,
149    pub quantum_fidelity: Option<f64>,
150    pub classical_preprocessing_time: Duration,
151    pub quantum_execution_time: Duration,
152}
153
154/// QML model types
155#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
156pub enum QMLModelType {
157    /// Variational Quantum Classifier
158    VQC,
159    /// Quantum Neural Network
160    QNN,
161    /// Quantum Approximate Optimization Algorithm
162    QAOA,
163    /// Variational Quantum Eigensolver
164    VQE,
165    /// Quantum Generative Adversarial Network
166    QGAN,
167    /// Quantum Convolutional Neural Network
168    QCNN,
169    /// Hybrid Classical-Quantum Network
170    HybridNetwork,
171}
172
173impl QMLAccelerator {
174    /// Create a new QML accelerator
175    pub fn new(
176        device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
177        config: QMLConfig,
178    ) -> DeviceResult<Self> {
179        let model_registry = ModelRegistry::new();
180        let hardware_manager = HardwareAccelerationManager::new(&config)?;
181
182        Ok(Self {
183            device,
184            config,
185            training_history: Vec::new(),
186            model_registry,
187            hardware_manager,
188            is_connected: false,
189        })
190    }
191
192    /// Connect to quantum hardware
193    pub async fn connect(&mut self) -> DeviceResult<()> {
194        let device = self.device.read().await;
195        #[cfg(feature = "_async_device")]
196        let is_available = device.is_available().await?;
197        #[cfg(not(feature = "_async_device"))]
198        let is_available = device.is_available()?;
199
200        if !is_available {
201            return Err(DeviceError::DeviceNotInitialized(
202                "Quantum device not available".to_string(),
203            ));
204        }
205
206        self.hardware_manager.initialize().await?;
207        self.is_connected = true;
208        Ok(())
209    }
210
211    /// Disconnect from hardware
212    pub async fn disconnect(&mut self) -> DeviceResult<()> {
213        self.hardware_manager.shutdown().await?;
214        self.is_connected = false;
215        Ok(())
216    }
217
218    /// Train a quantum machine learning model
219    pub async fn train_model(
220        &mut self,
221        model_type: QMLModelType,
222        training_data: training::TrainingData,
223        validation_data: Option<training::TrainingData>,
224    ) -> DeviceResult<training::TrainingResult> {
225        if !self.is_connected {
226            return Err(DeviceError::DeviceNotInitialized(
227                "QML accelerator not connected".to_string(),
228            ));
229        }
230
231        let mut trainer = QuantumTrainer::new(self.device.clone(), &self.config, model_type)?;
232
233        let result = trainer
234            .train(training_data, validation_data, &mut self.training_history)
235            .await?;
236
237        // Register the trained model
238        self.model_registry
239            .register_model(result.model_id.clone(), result.model.clone())?;
240
241        Ok(result)
242    }
243
244    /// Perform inference with a trained model
245    pub async fn inference(
246        &self,
247        model_id: &str,
248        input_data: InferenceData,
249    ) -> DeviceResult<InferenceResult> {
250        if !self.is_connected {
251            return Err(DeviceError::DeviceNotInitialized(
252                "QML accelerator not connected".to_string(),
253            ));
254        }
255
256        let model = self.model_registry.get_model(model_id)?;
257        let inference_engine = QuantumInferenceEngine::new(self.device.clone(), &self.config)?;
258
259        inference_engine.inference(model, input_data).await
260    }
261
262    /// Optimize quantum circuit parameters
263    pub async fn optimize_parameters(
264        &mut self,
265        initial_parameters: Vec<f64>,
266        objective_function: Box<dyn ObjectiveFunction + Send + Sync>,
267    ) -> DeviceResult<OptimizationResult> {
268        let mut optimizer =
269            create_gradient_optimizer(self.device.clone(), OptimizerType::Adam, 0.01);
270
271        optimizer.optimize(initial_parameters, objective_function)
272    }
273
274    /// Compute gradients using quantum methods
275    pub async fn compute_gradients(
276        &self,
277        circuit: ParameterizedQuantumCircuit,
278        parameters: Vec<f64>,
279    ) -> DeviceResult<Vec<f64>> {
280        let gradient_calculator =
281            QuantumGradientCalculator::new(self.device.clone(), GradientConfig::default())?;
282
283        gradient_calculator
284            .compute_gradients(circuit, parameters)
285            .await
286    }
287
288    /// Get training statistics
289    pub fn get_training_statistics(&self) -> TrainingStatistics {
290        TrainingStatistics::from_history(&self.training_history)
291    }
292
293    /// Export trained model
294    pub async fn export_model(
295        &self,
296        model_id: &str,
297        format: ModelExportFormat,
298    ) -> DeviceResult<Vec<u8>> {
299        let model = self.model_registry.get_model(model_id)?;
300        model.export(format).await
301    }
302
303    /// Import trained model
304    pub async fn import_model(
305        &mut self,
306        model_data: Vec<u8>,
307        format: ModelExportFormat,
308    ) -> DeviceResult<String> {
309        let model = QMLModel::import(model_data, format).await?;
310        let model_id = format!("imported_model_{}", uuid::Uuid::new_v4());
311
312        self.model_registry
313            .register_model(model_id.clone(), model)?;
314        Ok(model_id)
315    }
316
317    /// Get hardware acceleration metrics
318    pub async fn get_acceleration_metrics(&self) -> HardwareAccelerationMetrics {
319        self.hardware_manager.get_metrics().await
320    }
321
322    /// Benchmark quantum vs classical performance
323    pub async fn benchmark_performance(
324        &self,
325        model_type: QMLModelType,
326        problem_size: usize,
327    ) -> DeviceResult<PerformanceBenchmark> {
328        let benchmark_engine = PerformanceBenchmarkEngine::new(self.device.clone(), &self.config)?;
329
330        benchmark_engine.benchmark(model_type, problem_size).await
331    }
332
333    /// Get QML accelerator diagnostics
334    pub async fn get_diagnostics(&self) -> QMLDiagnostics {
335        let device = self.device.read().await;
336        #[cfg(feature = "_async_device")]
337        let device_props = device.properties().await.unwrap_or_default();
338        #[cfg(not(feature = "_async_device"))]
339        let device_props = device.properties().unwrap_or_default();
340
341        QMLDiagnostics {
342            is_connected: self.is_connected,
343            total_models: self.model_registry.model_count(),
344            training_epochs_completed: self.training_history.len(),
345            hardware_acceleration_enabled: self.config.enable_hardware_acceleration,
346            active_model_count: self.model_registry.active_model_count(),
347            average_training_time: self.calculate_average_training_time(),
348            quantum_advantage_ratio: self.hardware_manager.get_quantum_advantage_ratio().await,
349            device_properties: device_props,
350        }
351    }
352
353    fn calculate_average_training_time(&self) -> Duration {
354        if self.training_history.is_empty() {
355            return Duration::from_secs(0);
356        }
357
358        let total_time: Duration = self
359            .training_history
360            .iter()
361            .map(|epoch| epoch.execution_time)
362            .sum();
363
364        total_time / self.training_history.len() as u32
365    }
366}
367
368/// Inference data structure
369#[derive(Debug, Clone, Serialize, Deserialize)]
370pub struct InferenceData {
371    pub features: Vec<f64>,
372    pub metadata: HashMap<String, String>,
373}
374
375/// Inference result
376#[derive(Debug, Clone, Serialize, Deserialize)]
377pub struct InferenceResult {
378    pub prediction: f64,
379    pub confidence: Option<f64>,
380    pub quantum_fidelity: Option<f64>,
381    pub execution_time: Duration,
382    pub metadata: HashMap<String, String>,
383}
384
385/// QML model representation
386#[derive(Debug, Clone, Serialize, Deserialize)]
387pub struct QMLModel {
388    pub model_type: QMLModelType,
389    pub parameters: Vec<f64>,
390    pub circuit_structure: CircuitStructure,
391    pub training_metadata: HashMap<String, String>,
392    pub performance_metrics: HashMap<String, f64>,
393}
394
395impl QMLModel {
396    pub async fn export(&self, format: ModelExportFormat) -> DeviceResult<Vec<u8>> {
397        match format {
398            ModelExportFormat::JSON => serde_json::to_vec(self)
399                .map_err(|e| DeviceError::InvalidInput(format!("JSON export error: {e}"))),
400            ModelExportFormat::Binary => {
401                oxicode::serde::encode_to_vec(self, oxicode::config::standard())
402                    .map_err(|e| DeviceError::InvalidInput(format!("Binary export error: {e:?}")))
403            }
404            ModelExportFormat::ONNX => {
405                // Placeholder for ONNX export
406                Err(DeviceError::InvalidInput(
407                    "ONNX export not yet implemented".to_string(),
408                ))
409            }
410        }
411    }
412
413    pub async fn import(data: Vec<u8>, format: ModelExportFormat) -> DeviceResult<Self> {
414        match format {
415            ModelExportFormat::JSON => serde_json::from_slice(&data)
416                .map_err(|e| DeviceError::InvalidInput(format!("JSON import error: {e}"))),
417            ModelExportFormat::Binary => {
418                oxicode::serde::decode_from_slice(&data, oxicode::config::standard())
419                    .map(|(v, _consumed)| v)
420                    .map_err(|e| DeviceError::InvalidInput(format!("Binary import error: {e:?}")))
421            }
422            ModelExportFormat::ONNX => Err(DeviceError::InvalidInput(
423                "ONNX import not yet implemented".to_string(),
424            )),
425        }
426    }
427}
428
429/// Model export formats
430#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
431pub enum ModelExportFormat {
432    JSON,
433    Binary,
434    ONNX,
435}
436
437/// Circuit structure representation
438#[derive(Debug, Clone, Serialize, Deserialize)]
439pub struct CircuitStructure {
440    pub num_qubits: usize,
441    pub depth: usize,
442    pub gate_types: Vec<String>,
443    pub parameter_count: usize,
444    pub entangling_gates: usize,
445}
446
447/// Training statistics
448#[derive(Debug, Clone, Serialize, Deserialize)]
449pub struct TrainingStatistics {
450    pub total_epochs: usize,
451    pub final_loss: f64,
452    pub best_loss: f64,
453    pub average_loss: f64,
454    pub convergence_epoch: Option<usize>,
455    pub total_training_time: Duration,
456    pub average_epoch_time: Duration,
457}
458
459impl TrainingStatistics {
460    pub fn from_history(history: &[TrainingEpoch]) -> Self {
461        if history.is_empty() {
462            return Self {
463                total_epochs: 0,
464                final_loss: 0.0,
465                best_loss: f64::INFINITY,
466                average_loss: 0.0,
467                convergence_epoch: None,
468                total_training_time: Duration::from_secs(0),
469                average_epoch_time: Duration::from_secs(0),
470            };
471        }
472
473        let total_epochs = history.len();
474        // Safe to use expect here since we already verified history is not empty above
475        let final_loss = history
476            .last()
477            .expect("history should not be empty after early return check")
478            .loss;
479        let best_loss = history.iter().map(|e| e.loss).fold(f64::INFINITY, f64::min);
480        let average_loss = history.iter().map(|e| e.loss).sum::<f64>() / total_epochs as f64;
481        let total_training_time = history.iter().map(|e| e.execution_time).sum();
482        let average_epoch_time = total_training_time / total_epochs as u32;
483
484        Self {
485            total_epochs,
486            final_loss,
487            best_loss,
488            average_loss,
489            convergence_epoch: None, // Could implement convergence detection
490            total_training_time,
491            average_epoch_time,
492        }
493    }
494}
495
496/// QML diagnostics
497#[derive(Debug, Clone, Serialize, Deserialize)]
498pub struct QMLDiagnostics {
499    pub is_connected: bool,
500    pub total_models: usize,
501    pub training_epochs_completed: usize,
502    pub hardware_acceleration_enabled: bool,
503    pub active_model_count: usize,
504    pub average_training_time: Duration,
505    pub quantum_advantage_ratio: f64,
506    pub device_properties: HashMap<String, String>,
507}
508
509/// Model registry for managing trained models
510pub struct ModelRegistry {
511    models: HashMap<String, QMLModel>,
512    active_models: HashMap<String, bool>,
513}
514
515impl Default for ModelRegistry {
516    fn default() -> Self {
517        Self::new()
518    }
519}
520
521impl ModelRegistry {
522    pub fn new() -> Self {
523        Self {
524            models: HashMap::new(),
525            active_models: HashMap::new(),
526        }
527    }
528
529    pub fn register_model(&mut self, id: String, model: QMLModel) -> DeviceResult<()> {
530        self.models.insert(id.clone(), model);
531        self.active_models.insert(id, true);
532        Ok(())
533    }
534
535    pub fn get_model(&self, id: &str) -> DeviceResult<&QMLModel> {
536        self.models
537            .get(id)
538            .ok_or_else(|| DeviceError::InvalidInput(format!("Model {id} not found")))
539    }
540
541    pub fn model_count(&self) -> usize {
542        self.models.len()
543    }
544
545    pub fn active_model_count(&self) -> usize {
546        self.active_models
547            .values()
548            .filter(|&&active| active)
549            .count()
550    }
551
552    pub fn deactivate_model(&mut self, id: &str) -> DeviceResult<()> {
553        if self.active_models.contains_key(id) {
554            self.active_models.insert(id.to_string(), false);
555            Ok(())
556        } else {
557            Err(DeviceError::InvalidInput(format!("Model {id} not found")))
558        }
559    }
560}
561
562/// Create a VQC (Variational Quantum Classifier) accelerator
563pub fn create_vqc_accelerator(
564    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
565    num_qubits: usize,
566) -> DeviceResult<QMLAccelerator> {
567    let config = QMLConfig {
568        max_qubits: num_qubits,
569        optimizer: OptimizerType::Adam,
570        gradient_method: GradientMethod::ParameterShift,
571        ..Default::default()
572    };
573
574    QMLAccelerator::new(device, config)
575}
576
577/// Create a QAOA accelerator
578pub fn create_qaoa_accelerator(
579    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
580    problem_size: usize,
581) -> DeviceResult<QMLAccelerator> {
582    let config = QMLConfig {
583        max_qubits: problem_size,
584        optimizer: OptimizerType::COBYLA,
585        gradient_method: GradientMethod::FiniteDifference,
586        max_circuit_depth: 50,
587        ..Default::default()
588    };
589
590    QMLAccelerator::new(device, config)
591}
592
593#[cfg(test)]
594mod tests {
595    use super::*;
596    use crate::test_utils::*;
597
598    #[tokio::test]
599    async fn test_qml_accelerator_creation() {
600        let device = create_mock_quantum_device();
601        let accelerator = QMLAccelerator::new(device, QMLConfig::default())
602            .expect("QML accelerator creation should succeed with mock device");
603
604        assert_eq!(accelerator.config.max_qubits, 20);
605        assert!(!accelerator.is_connected);
606    }
607
608    #[tokio::test]
609    async fn test_model_registry() {
610        let mut registry = ModelRegistry::new();
611        assert_eq!(registry.model_count(), 0);
612
613        let model = QMLModel {
614            model_type: QMLModelType::VQC,
615            parameters: vec![0.1, 0.2, 0.3],
616            circuit_structure: CircuitStructure {
617                num_qubits: 4,
618                depth: 10,
619                gate_types: vec!["RY".to_string(), "CNOT".to_string()],
620                parameter_count: 8,
621                entangling_gates: 4,
622            },
623            training_metadata: HashMap::new(),
624            performance_metrics: HashMap::new(),
625        };
626
627        registry
628            .register_model("test_model".to_string(), model)
629            .expect("registering model should succeed");
630        assert_eq!(registry.model_count(), 1);
631        assert_eq!(registry.active_model_count(), 1);
632
633        let retrieved = registry
634            .get_model("test_model")
635            .expect("retrieving registered model should succeed");
636        assert_eq!(retrieved.model_type, QMLModelType::VQC);
637    }
638
639    #[test]
640    fn test_training_statistics() {
641        let history = vec![
642            TrainingEpoch {
643                epoch: 0,
644                loss: 1.0,
645                accuracy: Some(0.5),
646                parameters: vec![0.1],
647                gradient_norm: 0.5,
648                learning_rate: 0.01,
649                execution_time: Duration::from_millis(100),
650                quantum_fidelity: Some(0.95),
651                classical_preprocessing_time: Duration::from_millis(10),
652                quantum_execution_time: Duration::from_millis(90),
653            },
654            TrainingEpoch {
655                epoch: 1,
656                loss: 0.5,
657                accuracy: Some(0.7),
658                parameters: vec![0.2],
659                gradient_norm: 0.3,
660                learning_rate: 0.01,
661                execution_time: Duration::from_millis(120),
662                quantum_fidelity: Some(0.96),
663                classical_preprocessing_time: Duration::from_millis(15),
664                quantum_execution_time: Duration::from_millis(105),
665            },
666        ];
667
668        let stats = TrainingStatistics::from_history(&history);
669        assert_eq!(stats.total_epochs, 2);
670        assert_eq!(stats.final_loss, 0.5);
671        assert_eq!(stats.best_loss, 0.5);
672        assert_eq!(stats.average_loss, 0.75);
673    }
674}
quantrs2_device/quantum_ml/mod.rs

quantrs2_device/quantum_ml/
mod.rs
