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        #[cfg(feature = "ibm")]
195        let is_available = device.is_available().await?;
196        #[cfg(not(feature = "ibm"))]
197        let is_available = device.is_available()?;
198
199        if !is_available {
200            return Err(DeviceError::DeviceNotInitialized(
201                "Quantum device not available".to_string(),
202            ));
203        }
204
205        self.hardware_manager.initialize().await?;
206        self.is_connected = true;
207        Ok(())
208    }
209
210    /// Disconnect from hardware
211    pub async fn disconnect(&mut self) -> DeviceResult<()> {
212        self.hardware_manager.shutdown().await?;
213        self.is_connected = false;
214        Ok(())
215    }
216
217    /// Train a quantum machine learning model
218    pub async fn train_model(
219        &mut self,
220        model_type: QMLModelType,
221        training_data: training::TrainingData,
222        validation_data: Option<training::TrainingData>,
223    ) -> DeviceResult<training::TrainingResult> {
224        if !self.is_connected {
225            return Err(DeviceError::DeviceNotInitialized(
226                "QML accelerator not connected".to_string(),
227            ));
228        }
229
230        let mut trainer = QuantumTrainer::new(self.device.clone(), &self.config, model_type)?;
231
232        let result = trainer
233            .train(training_data, validation_data, &mut self.training_history)
234            .await?;
235
236        // Register the trained model
237        self.model_registry
238            .register_model(result.model_id.clone(), result.model.clone())?;
239
240        Ok(result)
241    }
242
243    /// Perform inference with a trained model
244    pub async fn inference(
245        &self,
246        model_id: &str,
247        input_data: InferenceData,
248    ) -> DeviceResult<InferenceResult> {
249        if !self.is_connected {
250            return Err(DeviceError::DeviceNotInitialized(
251                "QML accelerator not connected".to_string(),
252            ));
253        }
254
255        let model = self.model_registry.get_model(model_id)?;
256        let inference_engine = QuantumInferenceEngine::new(self.device.clone(), &self.config)?;
257
258        inference_engine.inference(model, input_data).await
259    }
260
261    /// Optimize quantum circuit parameters
262    pub async fn optimize_parameters(
263        &mut self,
264        initial_parameters: Vec<f64>,
265        objective_function: Box<dyn ObjectiveFunction + Send + Sync>,
266    ) -> DeviceResult<OptimizationResult> {
267        let mut optimizer =
268            create_gradient_optimizer(self.device.clone(), OptimizerType::Adam, 0.01);
269
270        optimizer.optimize(initial_parameters, objective_function)
271    }
272
273    /// Compute gradients using quantum methods
274    pub async fn compute_gradients(
275        &self,
276        circuit: ParameterizedQuantumCircuit,
277        parameters: Vec<f64>,
278    ) -> DeviceResult<Vec<f64>> {
279        let gradient_calculator =
280            QuantumGradientCalculator::new(self.device.clone(), GradientConfig::default())?;
281
282        gradient_calculator
283            .compute_gradients(circuit, parameters)
284            .await
285    }
286
287    /// Get training statistics
288    pub fn get_training_statistics(&self) -> TrainingStatistics {
289        TrainingStatistics::from_history(&self.training_history)
290    }
291
292    /// Export trained model
293    pub async fn export_model(
294        &self,
295        model_id: &str,
296        format: ModelExportFormat,
297    ) -> DeviceResult<Vec<u8>> {
298        let model = self.model_registry.get_model(model_id)?;
299        model.export(format).await
300    }
301
302    /// Import trained model
303    pub async fn import_model(
304        &mut self,
305        model_data: Vec<u8>,
306        format: ModelExportFormat,
307    ) -> DeviceResult<String> {
308        let model = QMLModel::import(model_data, format).await?;
309        let model_id = format!("imported_model_{}", uuid::Uuid::new_v4());
310
311        self.model_registry
312            .register_model(model_id.clone(), model)?;
313        Ok(model_id)
314    }
315
316    /// Get hardware acceleration metrics
317    pub async fn get_acceleration_metrics(&self) -> HardwareAccelerationMetrics {
318        self.hardware_manager.get_metrics().await
319    }
320
321    /// Benchmark quantum vs classical performance
322    pub async fn benchmark_performance(
323        &self,
324        model_type: QMLModelType,
325        problem_size: usize,
326    ) -> DeviceResult<PerformanceBenchmark> {
327        let benchmark_engine = PerformanceBenchmarkEngine::new(self.device.clone(), &self.config)?;
328
329        benchmark_engine.benchmark(model_type, problem_size).await
330    }
331
332    /// Get QML accelerator diagnostics
333    pub async fn get_diagnostics(&self) -> QMLDiagnostics {
334        let device = self.device.read().await;
335        #[cfg(feature = "ibm")]
336        let device_props = device.properties().await.unwrap_or_default();
337        #[cfg(not(feature = "ibm"))]
338        let device_props = device.properties().unwrap_or_default();
339
340        QMLDiagnostics {
341            is_connected: self.is_connected,
342            total_models: self.model_registry.model_count(),
343            training_epochs_completed: self.training_history.len(),
344            hardware_acceleration_enabled: self.config.enable_hardware_acceleration,
345            active_model_count: self.model_registry.active_model_count(),
346            average_training_time: self.calculate_average_training_time(),
347            quantum_advantage_ratio: self.hardware_manager.get_quantum_advantage_ratio().await,
348            device_properties: device_props,
349        }
350    }
351
352    fn calculate_average_training_time(&self) -> Duration {
353        if self.training_history.is_empty() {
354            return Duration::from_secs(0);
355        }
356
357        let total_time: Duration = self
358            .training_history
359            .iter()
360            .map(|epoch| epoch.execution_time)
361            .sum();
362
363        total_time / self.training_history.len() as u32
364    }
365}
366
367/// Inference data structure
368#[derive(Debug, Clone, Serialize, Deserialize)]
369pub struct InferenceData {
370    pub features: Vec<f64>,
371    pub metadata: HashMap<String, String>,
372}
373
374/// Inference result
375#[derive(Debug, Clone, Serialize, Deserialize)]
376pub struct InferenceResult {
377    pub prediction: f64,
378    pub confidence: Option<f64>,
379    pub quantum_fidelity: Option<f64>,
380    pub execution_time: Duration,
381    pub metadata: HashMap<String, String>,
382}
383
384/// QML model representation
385#[derive(Debug, Clone, Serialize, Deserialize)]
386pub struct QMLModel {
387    pub model_type: QMLModelType,
388    pub parameters: Vec<f64>,
389    pub circuit_structure: CircuitStructure,
390    pub training_metadata: HashMap<String, String>,
391    pub performance_metrics: HashMap<String, f64>,
392}
393
394impl QMLModel {
395    pub async fn export(&self, format: ModelExportFormat) -> DeviceResult<Vec<u8>> {
396        match format {
397            ModelExportFormat::JSON => serde_json::to_vec(self)
398                .map_err(|e| DeviceError::InvalidInput(format!("JSON export error: {e}"))),
399            ModelExportFormat::Binary => {
400                oxicode::serde::encode_to_vec(self, oxicode::config::standard())
401                    .map_err(|e| DeviceError::InvalidInput(format!("Binary export error: {e:?}")))
402            }
403            ModelExportFormat::ONNX => {
404                // Placeholder for ONNX export
405                Err(DeviceError::InvalidInput(
406                    "ONNX export not yet implemented".to_string(),
407                ))
408            }
409        }
410    }
411
412    pub async fn import(data: Vec<u8>, format: ModelExportFormat) -> DeviceResult<Self> {
413        match format {
414            ModelExportFormat::JSON => serde_json::from_slice(&data)
415                .map_err(|e| DeviceError::InvalidInput(format!("JSON import error: {e}"))),
416            ModelExportFormat::Binary => {
417                oxicode::serde::decode_from_slice(&data, oxicode::config::standard())
418                    .map(|(v, _consumed)| v)
419                    .map_err(|e| DeviceError::InvalidInput(format!("Binary import error: {e:?}")))
420            }
421            ModelExportFormat::ONNX => Err(DeviceError::InvalidInput(
422                "ONNX import not yet implemented".to_string(),
423            )),
424        }
425    }
426}
427
428/// Model export formats
429#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
430pub enum ModelExportFormat {
431    JSON,
432    Binary,
433    ONNX,
434}
435
436/// Circuit structure representation
437#[derive(Debug, Clone, Serialize, Deserialize)]
438pub struct CircuitStructure {
439    pub num_qubits: usize,
440    pub depth: usize,
441    pub gate_types: Vec<String>,
442    pub parameter_count: usize,
443    pub entangling_gates: usize,
444}
445
446/// Training statistics
447#[derive(Debug, Clone, Serialize, Deserialize)]
448pub struct TrainingStatistics {
449    pub total_epochs: usize,
450    pub final_loss: f64,
451    pub best_loss: f64,
452    pub average_loss: f64,
453    pub convergence_epoch: Option<usize>,
454    pub total_training_time: Duration,
455    pub average_epoch_time: Duration,
456}
457
458impl TrainingStatistics {
459    pub fn from_history(history: &[TrainingEpoch]) -> Self {
460        if history.is_empty() {
461            return Self {
462                total_epochs: 0,
463                final_loss: 0.0,
464                best_loss: f64::INFINITY,
465                average_loss: 0.0,
466                convergence_epoch: None,
467                total_training_time: Duration::from_secs(0),
468                average_epoch_time: Duration::from_secs(0),
469            };
470        }
471
472        let total_epochs = history.len();
473        // Safe to use expect here since we already verified history is not empty above
474        let final_loss = history
475            .last()
476            .expect("history should not be empty after early return check")
477            .loss;
478        let best_loss = history.iter().map(|e| e.loss).fold(f64::INFINITY, f64::min);
479        let average_loss = history.iter().map(|e| e.loss).sum::<f64>() / total_epochs as f64;
480        let total_training_time = history.iter().map(|e| e.execution_time).sum();
481        let average_epoch_time = total_training_time / total_epochs as u32;
482
483        Self {
484            total_epochs,
485            final_loss,
486            best_loss,
487            average_loss,
488            convergence_epoch: None, // Could implement convergence detection
489            total_training_time,
490            average_epoch_time,
491        }
492    }
493}
494
495/// QML diagnostics
496#[derive(Debug, Clone, Serialize, Deserialize)]
497pub struct QMLDiagnostics {
498    pub is_connected: bool,
499    pub total_models: usize,
500    pub training_epochs_completed: usize,
501    pub hardware_acceleration_enabled: bool,
502    pub active_model_count: usize,
503    pub average_training_time: Duration,
504    pub quantum_advantage_ratio: f64,
505    pub device_properties: HashMap<String, String>,
506}
507
508/// Model registry for managing trained models
509pub struct ModelRegistry {
510    models: HashMap<String, QMLModel>,
511    active_models: HashMap<String, bool>,
512}
513
514impl Default for ModelRegistry {
515    fn default() -> Self {
516        Self::new()
517    }
518}
519
520impl ModelRegistry {
521    pub fn new() -> Self {
522        Self {
523            models: HashMap::new(),
524            active_models: HashMap::new(),
525        }
526    }
527
528    pub fn register_model(&mut self, id: String, model: QMLModel) -> DeviceResult<()> {
529        self.models.insert(id.clone(), model);
530        self.active_models.insert(id, true);
531        Ok(())
532    }
533
534    pub fn get_model(&self, id: &str) -> DeviceResult<&QMLModel> {
535        self.models
536            .get(id)
537            .ok_or_else(|| DeviceError::InvalidInput(format!("Model {id} not found")))
538    }
539
540    pub fn model_count(&self) -> usize {
541        self.models.len()
542    }
543
544    pub fn active_model_count(&self) -> usize {
545        self.active_models
546            .values()
547            .filter(|&&active| active)
548            .count()
549    }
550
551    pub fn deactivate_model(&mut self, id: &str) -> DeviceResult<()> {
552        if self.active_models.contains_key(id) {
553            self.active_models.insert(id.to_string(), false);
554            Ok(())
555        } else {
556            Err(DeviceError::InvalidInput(format!("Model {id} not found")))
557        }
558    }
559}
560
561/// Create a VQC (Variational Quantum Classifier) accelerator
562pub fn create_vqc_accelerator(
563    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
564    num_qubits: usize,
565) -> DeviceResult<QMLAccelerator> {
566    let config = QMLConfig {
567        max_qubits: num_qubits,
568        optimizer: OptimizerType::Adam,
569        gradient_method: GradientMethod::ParameterShift,
570        ..Default::default()
571    };
572
573    QMLAccelerator::new(device, config)
574}
575
576/// Create a QAOA accelerator
577pub fn create_qaoa_accelerator(
578    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
579    problem_size: usize,
580) -> DeviceResult<QMLAccelerator> {
581    let config = QMLConfig {
582        max_qubits: problem_size,
583        optimizer: OptimizerType::COBYLA,
584        gradient_method: GradientMethod::FiniteDifference,
585        max_circuit_depth: 50,
586        ..Default::default()
587    };
588
589    QMLAccelerator::new(device, config)
590}
591
592#[cfg(test)]
593mod tests {
594    use super::*;
595    use crate::test_utils::*;
596
597    #[tokio::test]
598    async fn test_qml_accelerator_creation() {
599        let device = create_mock_quantum_device();
600        let accelerator = QMLAccelerator::new(device, QMLConfig::default())
601            .expect("QML accelerator creation should succeed with mock device");
602
603        assert_eq!(accelerator.config.max_qubits, 20);
604        assert!(!accelerator.is_connected);
605    }
606
607    #[tokio::test]
608    async fn test_model_registry() {
609        let mut registry = ModelRegistry::new();
610        assert_eq!(registry.model_count(), 0);
611
612        let model = QMLModel {
613            model_type: QMLModelType::VQC,
614            parameters: vec![0.1, 0.2, 0.3],
615            circuit_structure: CircuitStructure {
616                num_qubits: 4,
617                depth: 10,
618                gate_types: vec!["RY".to_string(), "CNOT".to_string()],
619                parameter_count: 8,
620                entangling_gates: 4,
621            },
622            training_metadata: HashMap::new(),
623            performance_metrics: HashMap::new(),
624        };
625
626        registry
627            .register_model("test_model".to_string(), model)
628            .expect("registering model should succeed");
629        assert_eq!(registry.model_count(), 1);
630        assert_eq!(registry.active_model_count(), 1);
631
632        let retrieved = registry
633            .get_model("test_model")
634            .expect("retrieving registered model should succeed");
635        assert_eq!(retrieved.model_type, QMLModelType::VQC);
636    }
637
638    #[test]
639    fn test_training_statistics() {
640        let history = vec![
641            TrainingEpoch {
642                epoch: 0,
643                loss: 1.0,
644                accuracy: Some(0.5),
645                parameters: vec![0.1],
646                gradient_norm: 0.5,
647                learning_rate: 0.01,
648                execution_time: Duration::from_millis(100),
649                quantum_fidelity: Some(0.95),
650                classical_preprocessing_time: Duration::from_millis(10),
651                quantum_execution_time: Duration::from_millis(90),
652            },
653            TrainingEpoch {
654                epoch: 1,
655                loss: 0.5,
656                accuracy: Some(0.7),
657                parameters: vec![0.2],
658                gradient_norm: 0.3,
659                learning_rate: 0.01,
660                execution_time: Duration::from_millis(120),
661                quantum_fidelity: Some(0.96),
662                classical_preprocessing_time: Duration::from_millis(15),
663                quantum_execution_time: Duration::from_millis(105),
664            },
665        ];
666
667        let stats = TrainingStatistics::from_history(&history);
668        assert_eq!(stats.total_epochs, 2);
669        assert_eq!(stats.final_loss, 0.5);
670        assert_eq!(stats.best_loss, 0.5);
671        assert_eq!(stats.average_loss, 0.75);
672    }
673}
quantrs2_device/quantum_ml/mod.rs

quantrs2_device/quantum_ml/
mod.rs
