use quantrs2_circuit::prelude::*;
use quantrs2_core::{
error::{QuantRS2Error, QuantRS2Result},
gate::GateOp,
qubit::QubitId,
};
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::mpsc;
use super::types::*;
#[cfg(feature = "scirs2")]
use scirs2_linalg::{det, eig, inv, matrix_norm, qr, svd};
#[cfg(feature = "scirs2")]
use scirs2_optimize::{differential_evolution, minimize, OptimizeResult};
#[cfg(feature = "scirs2")]
use scirs2_stats::{corrcoef, mean, pearsonr, spearmanr, std};
#[cfg(not(feature = "scirs2"))]
mod fallback_scirs2 {
use scirs2_core::ndarray::{Array1, Array2};
pub fn mean(_data: &Array1<f64>) -> Result<f64, String> {
Ok(0.0)
}
pub fn std(_data: &Array1<f64>, _ddof: i32) -> Result<f64, String> {
Ok(1.0)
}
pub struct OptimizeResult {
pub x: Array1<f64>,
pub fun: f64,
pub success: bool,
}
pub fn minimize(
_func: fn(&Array1<f64>) -> f64,
_x0: &Array1<f64>,
) -> Result<OptimizeResult, String> {
Ok(OptimizeResult {
x: Array1::zeros(2),
fun: 0.0,
success: true,
})
}
}
use crate::{
backend_traits::{query_backend_capabilities, BackendCapabilities},
calibration::{CalibrationManager, DeviceCalibration},
circuit_integration::{ExecutionResult, UniversalCircuitInterface},
topology::HardwareTopology,
vqa_support::{VQAConfig, VQAExecutor, VQAResult},
DeviceError, DeviceResult,
};
#[cfg(not(feature = "scirs2"))]
use fallback_scirs2::*;
pub trait QMLOptimizer: Send + Sync {
fn compute_gradients(&self, model: &QMLModel, data: &QMLDataBatch)
-> DeviceResult<Array1<f64>>;
fn update_parameters(
&mut self,
model: &mut QMLModel,
gradients: &Array1<f64>,
) -> DeviceResult<()>;
fn get_state(&self) -> OptimizerState;
fn set_state(&mut self, state: OptimizerState) -> DeviceResult<()>;
}
pub trait AnomalyDetector: Send + Sync {
fn detect(&self, data: &[(Instant, f64)]) -> Vec<DetectedAnomaly>;
fn update(&mut self, data: &[(Instant, f64)]);
fn threshold(&self) -> f64;
fn set_threshold(&mut self, threshold: f64);
}
pub trait NotificationChannel: Send + Sync {
fn send_notification(&self, alert: &ActiveAlert) -> DeviceResult<()>;
fn channel_type(&self) -> QMLAlertChannel;
}
pub trait QMLDataSource: Send + Sync {
fn load_data(&self, config: &HashMap<String, String>) -> DeviceResult<QMLDataset>;
fn info(&self) -> DataSourceInfo;
}
pub trait QMLDataProcessor: Send + Sync {
fn process(&self, data: &QMLDataset) -> DeviceResult<QMLDataset>;
fn info(&self) -> DataProcessorInfo;
}
pub trait FrameworkBridgeImpl: Send + Sync {
fn from_framework(&self, data: &[u8]) -> DeviceResult<QMLModel>;
fn to_framework(&self, model: &QMLModel) -> DeviceResult<Vec<u8>>;
fn execute(&self, model: &QMLModel, data: &QMLDataBatch) -> DeviceResult<Array1<f64>>;
fn info(&self) -> FrameworkInfo;
}
pub fn create_qml_integration_hub() -> DeviceResult<QuantumMLIntegrationHub> {
QuantumMLIntegrationHub::new(QMLIntegrationConfig::default())
}
pub fn create_high_performance_qml_config() -> QMLIntegrationConfig {
QMLIntegrationConfig {
enable_qnn: true,
enable_hybrid_training: true,
enable_autodiff: true,
enabled_frameworks: vec![
MLFramework::TensorFlow,
MLFramework::PyTorch,
MLFramework::PennyLane,
MLFramework::JAX,
],
training_config: QMLTrainingConfig {
max_epochs: 500,
learning_rate: 0.001,
batch_size: 64,
early_stopping: EarlyStoppingConfig {
enabled: true,
patience: 20,
min_delta: 1e-6,
monitor_metric: "val_loss".to_string(),
mode: ImprovementMode::Minimize,
},
gradient_method: GradientMethod::Adjoint,
loss_function: LossFunction::MeanSquaredError,
regularization: RegularizationConfig {
l1_lambda: 0.001,
l2_lambda: 0.01,
dropout_rate: 0.2,
quantum_noise: 0.01,
parameter_constraints: ParameterConstraints {
min_value: Some(-std::f64::consts::PI),
max_value: Some(std::f64::consts::PI),
enforce_unitarity: true,
enforce_hermiticity: false,
custom_constraints: Vec::new(),
},
},
validation_config: ValidationConfig {
validation_split: 0.15,
cv_folds: Some(5),
validation_frequency: 1,
enable_test_evaluation: true,
},
},
optimization_config: QMLOptimizationConfig {
optimizer_type: OptimizerType::Adam,
optimizer_params: [
("beta1".to_string(), 0.9),
("beta2".to_string(), 0.999),
("epsilon".to_string(), 1e-8),
]
.iter()
.cloned()
.collect(),
enable_parameter_sharing: true,
circuit_optimization: CircuitOptimizationConfig {
enable_gate_fusion: true,
enable_compression: true,
max_depth: Some(100),
allowed_gates: None,
topology_aware: true,
},
hardware_aware: true,
multi_objective: MultiObjectiveConfig {
enabled: true,
objective_weights: [
("accuracy".to_string(), 0.4),
("speed".to_string(), 0.3),
("resource_efficiency".to_string(), 0.2),
("cost".to_string(), 0.1),
]
.iter()
.cloned()
.collect(),
pareto_exploration: true,
constraint_handling: ConstraintHandling::Adaptive,
},
},
resource_config: QMLResourceConfig {
max_circuits_per_step: 5000,
memory_limit_mb: 32768,
parallel_config: ParallelExecutionConfig {
enable_parallel_circuits: true,
max_workers: 16,
batch_processing: BatchProcessingConfig {
dynamic_batch_size: true,
min_batch_size: 16,
max_batch_size: 512,
adaptation_strategy: BatchAdaptationStrategy::Performance,
},
load_balancing: crate::quantum_ml_integration::LoadBalancingStrategy::Performance,
},
caching_strategy: CachingStrategy::Adaptive,
resource_priorities: ResourcePriorities {
weights: [
("quantum".to_string(), 0.5),
("classical".to_string(), 0.25),
("memory".to_string(), 0.15),
("network".to_string(), 0.1),
]
.iter()
.cloned()
.collect(),
dynamic_adjustment: true,
performance_reallocation: true,
},
},
monitoring_config: QMLMonitoringConfig {
enable_monitoring: true,
collection_frequency: Duration::from_secs(10),
performance_tracking: PerformanceTrackingConfig {
track_training_metrics: true,
track_inference_metrics: true,
track_circuit_metrics: true,
aggregation_window: Duration::from_secs(60),
enable_trend_analysis: true,
},
resource_monitoring: ResourceMonitoringConfig {
monitor_quantum_resources: true,
monitor_classical_resources: true,
monitor_memory: true,
monitor_network: true,
usage_thresholds: [
("cpu".to_string(), 0.9),
("memory".to_string(), 0.9),
("quantum".to_string(), 0.95),
]
.iter()
.cloned()
.collect(),
},
alert_config: AlertConfig {
enabled: true,
thresholds: [
("error_rate".to_string(), 0.05),
("resource_usage".to_string(), 0.95),
("cost_spike".to_string(), 3.0),
]
.iter()
.cloned()
.collect(),
channels: vec![QMLAlertChannel::Log, QMLAlertChannel::Email],
escalation: AlertEscalation {
enabled: true,
levels: vec![
EscalationLevel {
name: "Warning".to_string(),
threshold_multiplier: 1.0,
channels: vec![QMLAlertChannel::Log],
actions: vec![EscalationAction::Notify],
},
EscalationLevel {
name: "Critical".to_string(),
threshold_multiplier: 2.0,
channels: vec![QMLAlertChannel::Log, QMLAlertChannel::Email],
actions: vec![EscalationAction::Notify, EscalationAction::Throttle],
},
EscalationLevel {
name: "Emergency".to_string(),
threshold_multiplier: 5.0,
channels: vec![
QMLAlertChannel::Log,
QMLAlertChannel::Email,
QMLAlertChannel::SMS,
],
actions: vec![EscalationAction::Notify, EscalationAction::Pause],
},
],
timeouts: [
("warning".to_string(), Duration::from_secs(180)),
("critical".to_string(), Duration::from_secs(60)),
("emergency".to_string(), Duration::from_secs(30)),
]
.iter()
.cloned()
.collect(),
},
},
},
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_qml_config_default() {
let config = QMLIntegrationConfig::default();
assert!(config.enable_qnn);
assert!(config.enable_hybrid_training);
assert!(config.enable_autodiff);
assert!(!config.enabled_frameworks.is_empty());
}
#[test]
fn test_qml_hub_creation() {
let config = QMLIntegrationConfig::default();
let hub = QuantumMLIntegrationHub::new(config);
assert!(hub.is_ok());
}
#[test]
fn test_high_performance_config() {
let config = create_high_performance_qml_config();
assert_eq!(config.training_config.max_epochs, 500);
assert_eq!(config.resource_config.max_circuits_per_step, 5000);
assert!(config.optimization_config.multi_objective.enabled);
}
#[test]
fn test_training_priority_ordering() {
assert!(TrainingPriority::Low < TrainingPriority::Normal);
assert!(TrainingPriority::Normal < TrainingPriority::High);
assert!(TrainingPriority::High < TrainingPriority::Critical);
}
#[test]
fn test_qml_model_type_serialization() {
let model_type = QMLModelType::QuantumNeuralNetwork;
let serialized =
serde_json::to_string(&model_type).expect("QMLModelType serialization should succeed");
let deserialized: QMLModelType =
serde_json::from_str(&serialized).expect("QMLModelType deserialization should succeed");
assert_eq!(model_type, deserialized);
}
#[tokio::test]
async fn test_qml_hub_model_registration() {
let hub = create_qml_integration_hub()
.expect("QML integration hub creation should succeed with default config");
let model = QMLModel {
model_id: "test_model".to_string(),
model_type: QMLModelType::QuantumClassifier,
architecture: QMLArchitecture {
num_qubits: 4,
layers: Vec::new(),
measurement_strategy: MeasurementStrategy::Computational,
entanglement_pattern: EntanglementPattern::Linear,
classical_components: Vec::new(),
},
parameters: QMLParameters {
quantum_params: Array1::zeros(10),
classical_params: Array1::zeros(5),
parameter_bounds: Vec::new(),
trainable_mask: Array1::from_elem(15, true),
gradients: None,
parameter_history: VecDeque::new(),
},
training_state: QMLTrainingState {
current_epoch: 0,
training_loss: 1.0,
validation_loss: None,
learning_rate: 0.01,
optimizer_state: OptimizerState {
optimizer_type: OptimizerType::Adam,
momentum: None,
velocity: None,
second_moment: None,
accumulated_gradients: None,
step_count: 0,
},
training_history: TrainingHistory {
loss_history: Vec::new(),
val_loss_history: Vec::new(),
metric_history: HashMap::new(),
lr_history: Vec::new(),
gradient_norm_history: Vec::new(),
parameter_norm_history: Vec::new(),
},
early_stopping_state: EarlyStoppingState {
best_metric: f64::INFINITY,
patience_counter: 0,
best_parameters: None,
should_stop: false,
},
},
performance_metrics: QMLPerformanceMetrics {
training_metrics: HashMap::new(),
validation_metrics: HashMap::new(),
test_metrics: HashMap::new(),
circuit_metrics: CircuitExecutionMetrics {
avg_circuit_depth: 10.0,
total_gate_count: 100,
avg_execution_time: Duration::from_millis(100),
circuit_fidelity: 0.95,
shot_efficiency: 0.9,
},
resource_metrics: ResourceUtilizationMetrics {
quantum_usage: 0.8,
classical_usage: 0.6,
memory_usage: 0.4,
network_usage: 0.2,
cost_efficiency: 0.7,
},
convergence_metrics: ConvergenceMetrics {
convergence_rate: 0.1,
stability: 0.9,
plateau_detected: false,
oscillation: 0.1,
final_gradient_norm: 0.01,
},
},
metadata: QMLModelMetadata {
created_at: SystemTime::now(),
updated_at: SystemTime::now(),
version: "1.0.0".to_string(),
author: "test".to_string(),
description: "Test QML model".to_string(),
tags: vec!["test".to_string()],
framework: MLFramework::Custom("test".to_string()),
hardware_requirements: crate::quantum_ml_integration::types::HardwareRequirements {
min_qubits: 4,
required_gates: vec!["H".to_string(), "CNOT".to_string()],
connectivity_requirements: ConnectivityRequirements {
connectivity_graph: vec![(0, 1), (1, 2), (2, 3)],
min_connectivity: 2,
topology_constraints: vec![TopologyConstraint::Linear],
},
performance_requirements: PerformanceRequirements {
min_gate_fidelity: 0.95,
max_execution_time: Duration::from_secs(60),
min_coherence_time: Duration::from_micros(100),
max_error_rate: 0.01,
},
},
},
};
let result = hub.register_model(model);
assert!(result.is_ok());
}
}