quantrs2_device/
job_scheduling.rs

1//! Advanced Job Priority and Scheduling Optimization for Quantum Hardware
2//!
3//! This module provides comprehensive job scheduling, prioritization, and optimization
4//! capabilities for quantum hardware backends, including:
5//! - Multi-level priority queue management
6//! - Intelligent resource allocation and load balancing
7//! - Cross-provider job coordination
8//! - SciRS2-powered scheduling optimization algorithms
9//! - Queue analytics and prediction
10//! - Job persistence and recovery mechanisms
11//! - Dynamic backend selection based on performance metrics
12
13use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
14use std::hash::{Hash, Hasher};
15use std::sync::{Arc, Mutex, RwLock};
16use std::time::{Duration, Instant, SystemTime};
17
18use quantrs2_circuit::{optimization::analysis::CircuitAnalyzer, prelude::Circuit};
19use quantrs2_core::{
20    error::{QuantRS2Error, QuantRS2Result},
21    qubit::QubitId,
22};
23
24use crate::{
25    backend_traits::{query_backend_capabilities, BackendCapabilities, BackendFeatures},
26    translation::HardwareBackend,
27    CircuitExecutor, CircuitResult, DeviceError, DeviceResult, QuantumDevice,
28};
29
30// SciRS2 dependencies for optimization algorithms
31#[cfg(feature = "scirs2")]
32use scirs2_graph::{dijkstra_path, minimum_spanning_tree, Graph};
33#[cfg(feature = "scirs2")]
34use scirs2_optimize::{minimize, OptimizeResult};
35#[cfg(feature = "scirs2")]
36use scirs2_stats::{corrcoef, mean, std};
37
38// Fallback implementations when SciRS2 is not available
39#[cfg(not(feature = "scirs2"))]
40mod fallback_scirs2 {
41    pub fn mean(_data: &[f64]) -> f64 {
42        0.0
43    }
44    pub fn std(_data: &[f64]) -> f64 {
45        1.0
46    }
47    pub fn correlation(_x: &[f64], _y: &[f64]) -> f64 {
48        0.0
49    }
50
51    pub struct OptimizeResult {
52        pub x: Vec<f64>,
53        pub success: bool,
54    }
55
56    pub fn minimize<F>(_func: F, _x0: Vec<f64>, _bounds: Option<Vec<(f64, f64)>>) -> OptimizeResult
57    where
58        F: Fn(&[f64]) -> f64,
59    {
60        OptimizeResult {
61            x: vec![0.0],
62            success: false,
63        }
64    }
65}
66
67#[cfg(not(feature = "scirs2"))]
68use fallback_scirs2::*;
69
70use serde::{Deserialize, Serialize};
71use tokio::sync::{mpsc, Semaphore};
72use uuid::Uuid;
73
74/// Job priority levels
75#[derive(
76    Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize, Default,
77)]
78pub enum JobPriority {
79    /// System critical jobs (maintenance, calibration)
80    Critical = 0,
81    /// High priority research or production jobs
82    High = 1,
83    /// Normal priority jobs
84    #[default]
85    Normal = 2,
86    /// Low priority background jobs
87    Low = 3,
88    /// Best effort jobs that can be delayed
89    BestEffort = 4,
90}
91
92/// Job execution status
93#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
94pub enum JobStatus {
95    /// Job is pending in queue
96    Pending,
97    /// Job is being validated
98    Validating,
99    /// Job is scheduled for execution
100    Scheduled,
101    /// Job is currently running
102    Running,
103    /// Job completed successfully
104    Completed,
105    /// Job failed during execution
106    Failed,
107    /// Job was cancelled
108    Cancelled,
109    /// Job timed out
110    TimedOut,
111    /// Job is retrying after failure
112    Retrying,
113    /// Job is paused/suspended
114    Paused,
115}
116
117/// Advanced scheduling strategies
118#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
119pub enum SchedulingStrategy {
120    /// First-In-First-Out with priority levels
121    PriorityFIFO,
122    /// Shortest Job First
123    ShortestJobFirst,
124    /// Shortest Remaining Time First
125    ShortestRemainingTimeFirst,
126    /// Fair Share scheduling
127    FairShare,
128    /// Round Robin with priority
129    PriorityRoundRobin,
130    /// Backfill scheduling
131    Backfill,
132    /// Earliest Deadline First
133    EarliestDeadlineFirst,
134    /// Rate Monotonic scheduling
135    RateMonotonic,
136    /// Machine learning optimized scheduling using SciRS2
137    MLOptimized,
138    /// Multi-objective optimization using SciRS2
139    MultiObjectiveOptimized,
140    /// Reinforcement Learning based scheduling
141    ReinforcementLearning,
142    /// Genetic Algorithm scheduling
143    GeneticAlgorithm,
144    /// Game-theoretic fair scheduling
145    GameTheoreticFair,
146    /// Energy-aware scheduling
147    EnergyAware,
148    /// Deadline-aware scheduling with SLA guarantees
149    DeadlineAwareSLA,
150    /// Custom scheduling function
151    Custom(String),
152}
153
154/// Advanced resource allocation strategies
155#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
156pub enum AllocationStrategy {
157    /// First available backend
158    FirstFit,
159    /// Best performance for job requirements
160    BestFit,
161    /// Worst fit for load balancing
162    WorstFit,
163    /// Least loaded backend
164    LeastLoaded,
165    /// Most loaded backend (for consolidation)
166    MostLoaded,
167    /// Round robin across backends
168    RoundRobin,
169    /// Weighted round robin
170    WeightedRoundRobin,
171    /// Cost-optimized allocation
172    CostOptimized,
173    /// Performance-optimized allocation
174    PerformanceOptimized,
175    /// Energy-efficient allocation
176    EnergyEfficient,
177    /// SciRS2-optimized allocation using ML
178    SciRS2Optimized,
179    /// Multi-objective allocation (cost, performance, energy)
180    MultiObjectiveOptimized,
181    /// Locality-aware allocation
182    LocalityAware,
183    /// Fault-tolerant allocation
184    FaultTolerant,
185    /// Predictive allocation based on historical patterns
186    PredictiveAllocation,
187}
188
189/// Job submission configuration
190#[derive(Debug, Clone, Serialize, Deserialize)]
191pub struct JobConfig {
192    /// Job priority level
193    pub priority: JobPriority,
194    /// Maximum execution time
195    pub max_execution_time: Duration,
196    /// Maximum wait time in queue
197    pub max_queue_time: Option<Duration>,
198    /// Number of retry attempts on failure
199    pub retry_attempts: u32,
200    /// Retry delay between attempts
201    pub retry_delay: Duration,
202    /// Resource requirements
203    pub resource_requirements: ResourceRequirements,
204    /// Preferred backends (ordered by preference)
205    pub preferred_backends: Vec<HardwareBackend>,
206    /// Job tags for grouping and filtering
207    pub tags: HashMap<String, String>,
208    /// Job dependencies
209    pub dependencies: Vec<JobId>,
210    /// Deadline for completion
211    pub deadline: Option<SystemTime>,
212    /// Cost constraints
213    pub cost_limit: Option<f64>,
214}
215
216impl Default for JobConfig {
217    fn default() -> Self {
218        Self {
219            priority: JobPriority::Normal,
220            max_execution_time: Duration::from_secs(3600), // 1 hour
221            max_queue_time: Some(Duration::from_secs(86400)), // 24 hours
222            retry_attempts: 3,
223            retry_delay: Duration::from_secs(60),
224            resource_requirements: ResourceRequirements::default(),
225            preferred_backends: vec![],
226            tags: HashMap::new(),
227            dependencies: vec![],
228            deadline: None,
229            cost_limit: None,
230        }
231    }
232}
233
234/// Resource requirements for job execution
235#[derive(Debug, Clone, Serialize, Deserialize)]
236pub struct ResourceRequirements {
237    /// Minimum number of qubits required
238    pub min_qubits: usize,
239    /// Maximum circuit depth
240    pub max_depth: Option<usize>,
241    /// Required gate fidelity
242    pub min_fidelity: Option<f64>,
243    /// Required connectivity (if specific topology needed)
244    pub required_connectivity: Option<String>,
245    /// Memory requirements (MB)
246    pub memory_mb: Option<u64>,
247    /// CPU requirements
248    pub cpu_cores: Option<u32>,
249    /// Special hardware features required
250    pub required_features: Vec<String>,
251}
252
253impl Default for ResourceRequirements {
254    fn default() -> Self {
255        Self {
256            min_qubits: 1,
257            max_depth: None,
258            min_fidelity: None,
259            required_connectivity: None,
260            memory_mb: None,
261            cpu_cores: None,
262            required_features: vec![],
263        }
264    }
265}
266
267/// Unique job identifier
268#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
269pub struct JobId(pub String);
270
271impl Default for JobId {
272    fn default() -> Self {
273        Self::new()
274    }
275}
276
277impl JobId {
278    pub fn new() -> Self {
279        Self(Uuid::new_v4().to_string())
280    }
281
282    pub const fn from_string(s: String) -> Self {
283        Self(s)
284    }
285}
286
287impl std::fmt::Display for JobId {
288    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
289        write!(f, "{}", self.0)
290    }
291}
292
293/// Quantum circuit job definition
294#[derive(Debug, Clone)]
295pub struct QuantumJob<const N: usize> {
296    /// Unique job identifier
297    pub id: JobId,
298    /// Job configuration
299    pub config: JobConfig,
300    /// Circuit to execute
301    pub circuit: Circuit<N>,
302    /// Number of shots
303    pub shots: usize,
304    /// Job submission time
305    pub submitted_at: SystemTime,
306    /// Job status
307    pub status: JobStatus,
308    /// Execution history and attempts
309    pub execution_history: Vec<JobExecution>,
310    /// Job metadata
311    pub metadata: HashMap<String, String>,
312    /// User/group information
313    pub user_id: String,
314    /// Job group/project
315    pub group_id: Option<String>,
316    /// Estimated execution time
317    pub estimated_duration: Option<Duration>,
318    /// Assigned backend
319    pub assigned_backend: Option<HardwareBackend>,
320    /// Cost tracking
321    pub estimated_cost: Option<f64>,
322    pub actual_cost: Option<f64>,
323}
324
325/// Job execution attempt record
326#[derive(Debug, Clone, Serialize, Deserialize)]
327pub struct JobExecution {
328    /// Attempt number
329    pub attempt: u32,
330    /// Backend used for execution
331    pub backend: HardwareBackend,
332    /// Execution start time
333    pub started_at: SystemTime,
334    /// Execution end time
335    pub ended_at: Option<SystemTime>,
336    /// Execution result
337    pub result: Option<CircuitResult>,
338    /// Error information if failed
339    pub error: Option<String>,
340    /// Execution metrics
341    pub metrics: ExecutionMetrics,
342}
343
344/// Execution performance metrics
345#[derive(Debug, Clone, Serialize, Deserialize)]
346pub struct ExecutionMetrics {
347    /// Actual queue time
348    pub queue_time: Duration,
349    /// Actual execution time
350    pub execution_time: Option<Duration>,
351    /// Resource utilization
352    pub resource_utilization: f64,
353    /// Cost incurred
354    pub cost: Option<f64>,
355    /// Quality metrics (fidelity, error rates, etc.)
356    pub quality_metrics: HashMap<String, f64>,
357}
358
359impl Default for ExecutionMetrics {
360    fn default() -> Self {
361        Self {
362            queue_time: Duration::from_secs(0),
363            execution_time: None,
364            resource_utilization: 0.0,
365            cost: None,
366            quality_metrics: HashMap::new(),
367        }
368    }
369}
370
371/// Backend performance tracking
372#[derive(Debug, Clone)]
373pub struct BackendPerformance {
374    /// Backend identifier
375    pub backend: HardwareBackend,
376    /// Current queue length
377    pub queue_length: usize,
378    /// Average queue time
379    pub avg_queue_time: Duration,
380    /// Average execution time
381    pub avg_execution_time: Duration,
382    /// Success rate (0.0 - 1.0)
383    pub success_rate: f64,
384    /// Current utilization (0.0 - 1.0)
385    pub utilization: f64,
386    /// Cost per job
387    pub avg_cost: Option<f64>,
388    /// Last updated timestamp
389    pub last_updated: SystemTime,
390    /// Historical performance data
391    pub history: VecDeque<PerformanceSnapshot>,
392}
393
394/// Performance snapshot for historical tracking
395#[derive(Debug, Clone, Serialize, Deserialize)]
396pub struct PerformanceSnapshot {
397    pub timestamp: SystemTime,
398    pub queue_length: usize,
399    pub utilization: f64,
400    pub avg_queue_time_secs: f64,
401    pub success_rate: f64,
402}
403
404/// Queue analytics and predictions
405#[derive(Debug, Clone)]
406pub struct QueueAnalytics {
407    /// Current total queue length across all backends
408    pub total_queue_length: usize,
409    /// Queue length by priority
410    pub queue_by_priority: HashMap<JobPriority, usize>,
411    /// Queue length by backend
412    pub queue_by_backend: HashMap<HardwareBackend, usize>,
413    /// Predicted queue times
414    pub predicted_queue_times: HashMap<HardwareBackend, Duration>,
415    /// System load metrics
416    pub system_load: f64,
417    /// Throughput (jobs per hour)
418    pub throughput: f64,
419    /// Average wait time
420    pub avg_wait_time: Duration,
421}
422
423/// Job scheduling optimization parameters
424#[derive(Debug, Clone)]
425pub struct SchedulingParams {
426    /// Scheduling strategy
427    pub strategy: SchedulingStrategy,
428    /// Resource allocation strategy
429    pub allocation_strategy: AllocationStrategy,
430    /// Time slice for round robin (if applicable)
431    pub time_slice: Duration,
432    /// Maximum jobs per user in queue
433    pub max_jobs_per_user: Option<usize>,
434    /// Fair share weights by user/group
435    pub fair_share_weights: HashMap<String, f64>,
436    /// Backfill threshold
437    pub backfill_threshold: Duration,
438    /// Load balancing parameters
439    pub load_balance_factor: f64,
440    /// SciRS2 optimization parameters
441    pub scirs2_params: SciRS2SchedulingParams,
442}
443
444/// Advanced SciRS2-specific scheduling optimization parameters
445#[derive(Debug, Clone)]
446pub struct SciRS2SchedulingParams {
447    /// Enable SciRS2 optimization
448    pub enabled: bool,
449    /// Optimization objective weights
450    pub objective_weights: HashMap<String, f64>,
451    /// Historical data window for learning
452    pub learning_window: Duration,
453    /// Optimization frequency
454    pub optimization_frequency: Duration,
455    /// Prediction model parameters
456    pub model_params: HashMap<String, f64>,
457    /// Machine learning algorithm selection
458    pub ml_algorithm: MLAlgorithm,
459    /// Multi-objective optimization weights
460    pub multi_objective_weights: MultiObjectiveWeights,
461    /// Reinforcement learning parameters
462    pub rl_params: RLParameters,
463    /// Genetic algorithm parameters
464    pub ga_params: GAParameters,
465    /// Enable predictive modeling
466    pub enable_prediction: bool,
467    /// Model retraining frequency
468    pub retrain_frequency: Duration,
469    /// Feature engineering parameters
470    pub feature_params: FeatureParams,
471}
472
473impl Default for SciRS2SchedulingParams {
474    fn default() -> Self {
475        Self {
476            enabled: true,
477            objective_weights: [
478                ("throughput".to_string(), 0.25),
479                ("fairness".to_string(), 0.25),
480                ("utilization".to_string(), 0.2),
481                ("cost".to_string(), 0.15),
482                ("energy".to_string(), 0.1),
483                ("sla_compliance".to_string(), 0.05),
484            ]
485            .into_iter()
486            .collect(),
487            learning_window: Duration::from_secs(86400), // 24 hours
488            optimization_frequency: Duration::from_secs(180), // 3 minutes
489            model_params: HashMap::new(),
490            ml_algorithm: MLAlgorithm::EnsembleMethod,
491            multi_objective_weights: MultiObjectiveWeights::default(),
492            rl_params: RLParameters::default(),
493            ga_params: GAParameters::default(),
494            enable_prediction: true,
495            retrain_frequency: Duration::from_secs(3600), // 1 hour
496            feature_params: FeatureParams::default(),
497        }
498    }
499}
500
501impl Default for SchedulingParams {
502    fn default() -> Self {
503        Self {
504            strategy: SchedulingStrategy::MLOptimized,
505            allocation_strategy: AllocationStrategy::SciRS2Optimized,
506            time_slice: Duration::from_secs(60),
507            max_jobs_per_user: Some(100),
508            fair_share_weights: HashMap::new(),
509            backfill_threshold: Duration::from_secs(300),
510            load_balance_factor: 0.8,
511            scirs2_params: SciRS2SchedulingParams::default(),
512        }
513    }
514}
515
516/// Advanced Job Scheduler and Queue Manager
517pub struct QuantumJobScheduler {
518    /// Scheduling parameters
519    params: Arc<RwLock<SchedulingParams>>,
520    /// Job queues by priority level
521    job_queues: Arc<Mutex<BTreeMap<JobPriority, VecDeque<JobId>>>>,
522    /// All active jobs
523    jobs: Arc<RwLock<HashMap<JobId, Box<dyn std::any::Any + Send + Sync>>>>,
524    /// Backend performance tracking
525    backend_performance: Arc<RwLock<HashMap<HardwareBackend, BackendPerformance>>>,
526    /// Available backends
527    backends: Arc<RwLock<HashSet<HardwareBackend>>>,
528    /// Running jobs
529    running_jobs: Arc<RwLock<HashMap<JobId, (HardwareBackend, SystemTime)>>>,
530    /// Job execution history
531    execution_history: Arc<RwLock<Vec<JobExecution>>>,
532    /// User fair share tracking
533    user_shares: Arc<RwLock<HashMap<String, UserShare>>>,
534    /// Scheduler control
535    scheduler_running: Arc<Mutex<bool>>,
536    /// Event notifications
537    event_sender: mpsc::UnboundedSender<SchedulerEvent>,
538    /// Performance predictor
539    performance_predictor: Arc<Mutex<PerformancePredictor>>,
540    /// Resource manager
541    resource_manager: Arc<Mutex<ResourceManager>>,
542}
543
544/// User fair share tracking
545#[derive(Debug, Clone)]
546struct UserShare {
547    user_id: String,
548    allocated_share: f64,
549    used_share: f64,
550    jobs_running: usize,
551    jobs_queued: usize,
552    last_updated: SystemTime,
553}
554
555/// Scheduler events for monitoring and notifications
556#[derive(Debug, Clone)]
557pub enum SchedulerEvent {
558    JobSubmitted(JobId),
559    JobScheduled(JobId, HardwareBackend),
560    JobStarted(JobId),
561    JobCompleted(JobId, CircuitResult),
562    JobFailed(JobId, String),
563    JobCancelled(JobId),
564    BackendStatusChanged(HardwareBackend, BackendStatus),
565    QueueAnalyticsUpdated(QueueAnalytics),
566}
567
568/// Backend status information
569#[derive(Debug, Clone)]
570pub enum BackendStatus {
571    Available,
572    Busy,
573    Maintenance,
574    Offline,
575    Error(String),
576}
577
578/// Performance prediction using SciRS2 algorithms
579struct PerformancePredictor {
580    /// Historical performance data
581    history: VecDeque<PredictionDataPoint>,
582    /// Learned model parameters
583    model_params: HashMap<String, f64>,
584    /// Prediction accuracy metrics
585    accuracy_metrics: HashMap<String, f64>,
586}
587
588/// Data point for performance prediction
589#[derive(Debug, Clone)]
590struct PredictionDataPoint {
591    timestamp: SystemTime,
592    backend: HardwareBackend,
593    queue_length: usize,
594    job_complexity: f64,
595    execution_time: Duration,
596    success: bool,
597}
598
599/// Resource allocation and management
600struct ResourceManager {
601    /// Available resources by backend
602    available_resources: HashMap<HardwareBackend, ResourceCapacity>,
603    /// Resource reservations
604    reservations: HashMap<JobId, ResourceReservation>,
605    /// Resource utilization history
606    utilization_history: VecDeque<ResourceSnapshot>,
607}
608
609/// Resource capacity for a backend
610#[derive(Debug, Clone)]
611struct ResourceCapacity {
612    qubits: usize,
613    max_circuit_depth: Option<usize>,
614    memory_mb: u64,
615    cpu_cores: u32,
616    concurrent_jobs: usize,
617    features: HashSet<String>,
618}
619
620/// Resource reservation for a job
621#[derive(Debug, Clone)]
622struct ResourceReservation {
623    job_id: JobId,
624    backend: HardwareBackend,
625    resources: ResourceRequirements,
626    reserved_at: SystemTime,
627    expires_at: SystemTime,
628}
629
630/// Resource utilization snapshot
631#[derive(Debug, Clone)]
632struct ResourceSnapshot {
633    timestamp: SystemTime,
634    backend: HardwareBackend,
635    utilization: f64,
636    active_jobs: usize,
637}
638
639impl QuantumJobScheduler {
640    /// Create a new quantum job scheduler
641    pub fn new(params: SchedulingParams) -> Self {
642        let (event_sender, _) = mpsc::unbounded_channel();
643
644        Self {
645            params: Arc::new(RwLock::new(params)),
646            job_queues: Arc::new(Mutex::new(BTreeMap::new())),
647            jobs: Arc::new(RwLock::new(HashMap::new())),
648            backend_performance: Arc::new(RwLock::new(HashMap::new())),
649            backends: Arc::new(RwLock::new(HashSet::new())),
650            running_jobs: Arc::new(RwLock::new(HashMap::new())),
651            execution_history: Arc::new(RwLock::new(Vec::new())),
652            user_shares: Arc::new(RwLock::new(HashMap::new())),
653            scheduler_running: Arc::new(Mutex::new(false)),
654            event_sender,
655            performance_predictor: Arc::new(Mutex::new(PerformancePredictor::new())),
656            resource_manager: Arc::new(Mutex::new(ResourceManager::new())),
657        }
658    }
659
660    /// Register a backend
661    pub async fn register_backend(&self, backend: HardwareBackend) -> DeviceResult<()> {
662        let mut backends = self
663            .backends
664            .write()
665            .expect("Failed to acquire write lock on backends in register_backend");
666        backends.insert(backend);
667
668        // Initialize performance tracking
669        let mut performance = self
670            .backend_performance
671            .write()
672            .expect("Failed to acquire write lock on backend_performance in register_backend");
673        performance.insert(
674            backend,
675            BackendPerformance {
676                backend,
677                queue_length: 0,
678                avg_queue_time: Duration::from_secs(0),
679                avg_execution_time: Duration::from_secs(0),
680                success_rate: 1.0,
681                utilization: 0.0,
682                avg_cost: None,
683                last_updated: SystemTime::now(),
684                history: VecDeque::new(),
685            },
686        );
687
688        // Initialize resource capacity
689        let capabilities = query_backend_capabilities(backend);
690        let mut resource_manager = self
691            .resource_manager
692            .lock()
693            .expect("Failed to acquire lock on resource_manager in register_backend");
694        resource_manager.available_resources.insert(
695            backend,
696            ResourceCapacity {
697                qubits: capabilities.features.max_qubits,
698                max_circuit_depth: capabilities.features.max_depth,
699                memory_mb: 8192,     // Default 8GB
700                cpu_cores: 4,        // Default 4 cores
701                concurrent_jobs: 10, // Default concurrent job limit
702                features: capabilities
703                    .features
704                    .supported_measurement_bases
705                    .into_iter()
706                    .collect(),
707            },
708        );
709
710        Ok(())
711    }
712
713    /// Get list of available backends
714    pub fn get_available_backends(&self) -> Vec<HardwareBackend> {
715        let backends = self
716            .backends
717            .read()
718            .expect("Failed to acquire read lock on backends in get_available_backends");
719        backends.iter().copied().collect()
720    }
721
722    /// Submit a quantum job for execution
723    pub async fn submit_job<const N: usize>(
724        &self,
725        circuit: Circuit<N>,
726        shots: usize,
727        config: JobConfig,
728        user_id: String,
729    ) -> DeviceResult<JobId> {
730        let job_id = JobId::new();
731        let now = SystemTime::now();
732
733        // Validate job configuration
734        self.validate_job_config(&config).await?;
735
736        // Estimate execution time and cost
737        let estimated_duration = self
738            .estimate_execution_time(&circuit, shots, &config)
739            .await?;
740        let estimated_cost = self.estimate_cost(&circuit, shots, &config).await?;
741
742        // Create job
743        let job = QuantumJob {
744            id: job_id.clone(),
745            config,
746            circuit,
747            shots,
748            submitted_at: now,
749            status: JobStatus::Pending,
750            execution_history: vec![],
751            metadata: HashMap::new(),
752            user_id: user_id.clone(),
753            group_id: None,
754            estimated_duration: Some(estimated_duration),
755            assigned_backend: None,
756            estimated_cost: Some(estimated_cost),
757            actual_cost: None,
758        };
759
760        // Store job
761        let mut jobs = self
762            .jobs
763            .write()
764            .expect("Failed to acquire write lock on jobs in submit_job");
765        jobs.insert(job_id.clone(), Box::new(job.clone()));
766        drop(jobs);
767
768        // Add to appropriate priority queue
769        let mut queues = self
770            .job_queues
771            .lock()
772            .expect("Failed to acquire lock on job_queues in submit_job");
773        let queue = queues.entry(job.config.priority).or_default();
774        queue.push_back(job_id.clone());
775        drop(queues);
776
777        // Update user share tracking
778        self.update_user_share(&user_id, 1, 0).await;
779
780        // Send event notification
781        let _ = self
782            .event_sender
783            .send(SchedulerEvent::JobSubmitted(job_id.clone()));
784
785        // Start scheduler if not running
786        self.ensure_scheduler_running().await;
787
788        Ok(job_id)
789    }
790
791    /// Cancel a queued or running job
792    pub async fn cancel_job(&self, job_id: &JobId) -> DeviceResult<bool> {
793        // Remove from queue if still pending
794        let mut queues = self
795            .job_queues
796            .lock()
797            .expect("Failed to acquire lock on job_queues in cancel_job");
798        for queue in queues.values_mut() {
799            if let Some(pos) = queue.iter().position(|id| id == job_id) {
800                queue.remove(pos);
801                drop(queues);
802
803                // Update job status
804                self.update_job_status(job_id, JobStatus::Cancelled).await?;
805
806                // Send event
807                let _ = self
808                    .event_sender
809                    .send(SchedulerEvent::JobCancelled(job_id.clone()));
810                return Ok(true);
811            }
812        }
813        drop(queues);
814
815        // Cancel running job if applicable
816        let running_jobs = self
817            .running_jobs
818            .read()
819            .expect("Failed to acquire read lock on running_jobs in cancel_job");
820        if running_jobs.contains_key(job_id) {
821            // TODO: Implement backend-specific job cancellation
822            // For now, mark as cancelled and clean up when job completes
823            self.update_job_status(job_id, JobStatus::Cancelled).await?;
824            return Ok(true);
825        }
826
827        Ok(false)
828    }
829
830    /// Get job status and information
831    pub async fn get_job_status<const N: usize>(
832        &self,
833        job_id: &JobId,
834    ) -> DeviceResult<Option<QuantumJob<N>>> {
835        let jobs = self
836            .jobs
837            .read()
838            .expect("Failed to acquire read lock on jobs in get_job_status");
839        if let Some(job_any) = jobs.get(job_id) {
840            if let Some(job) = job_any.downcast_ref::<QuantumJob<N>>() {
841                return Ok(Some(job.clone()));
842            }
843        }
844        Ok(None)
845    }
846
847    /// Get queue analytics and predictions
848    pub async fn get_queue_analytics(&self) -> DeviceResult<QueueAnalytics> {
849        let queues = self
850            .job_queues
851            .lock()
852            .expect("Failed to acquire lock on job_queues in get_queue_analytics");
853        let backend_performance = self
854            .backend_performance
855            .read()
856            .expect("Failed to acquire read lock on backend_performance in get_queue_analytics");
857
858        let total_queue_length = queues.values().map(|q| q.len()).sum();
859
860        let queue_by_priority = queues
861            .iter()
862            .map(|(priority, queue)| (*priority, queue.len()))
863            .collect();
864
865        let queue_by_backend = backend_performance
866            .iter()
867            .map(|(backend, perf)| (*backend, perf.queue_length))
868            .collect();
869
870        let predicted_queue_times = self.predict_queue_times(&backend_performance).await;
871
872        let system_load = self.calculate_system_load(&backend_performance).await;
873        let throughput = self.calculate_throughput().await;
874        let avg_wait_time = self.calculate_average_wait_time().await;
875
876        Ok(QueueAnalytics {
877            total_queue_length,
878            queue_by_priority,
879            queue_by_backend,
880            predicted_queue_times,
881            system_load,
882            throughput,
883            avg_wait_time,
884        })
885    }
886
887    /// Start the job scheduler
888    pub async fn start_scheduler(&self) -> DeviceResult<()> {
889        let mut running = self
890            .scheduler_running
891            .lock()
892            .expect("Failed to acquire lock on scheduler_running in start_scheduler");
893        if *running {
894            return Err(DeviceError::APIError(
895                "Scheduler already running".to_string(),
896            ));
897        }
898        *running = true;
899        drop(running);
900
901        // Start main scheduling loop
902        let scheduler = Arc::new(self.clone());
903        tokio::spawn(async move {
904            scheduler.scheduling_loop().await;
905        });
906
907        // Start performance monitoring
908        let scheduler = Arc::new(self.clone());
909        tokio::spawn(async move {
910            scheduler.performance_monitoring_loop().await;
911        });
912
913        // Start SciRS2 optimization if enabled
914        let params = self
915            .params
916            .read()
917            .expect("Failed to acquire read lock on params in start_scheduler");
918        if params.scirs2_params.enabled {
919            drop(params);
920            let scheduler = Arc::new(self.clone());
921            tokio::spawn(async move {
922                scheduler.scirs2_optimization_loop().await;
923            });
924        }
925
926        Ok(())
927    }
928
929    /// Stop the job scheduler
930    pub async fn stop_scheduler(&self) -> DeviceResult<()> {
931        let mut running = self
932            .scheduler_running
933            .lock()
934            .expect("Failed to acquire lock on scheduler_running in stop_scheduler");
935        *running = false;
936        Ok(())
937    }
938
939    // Internal implementation methods
940
941    async fn validate_job_config(&self, config: &JobConfig) -> DeviceResult<()> {
942        // Validate resource requirements against available backends
943        let backends = self
944            .backends
945            .read()
946            .expect("Failed to acquire read lock on backends in validate_job_config");
947        if backends.is_empty() {
948            return Err(DeviceError::APIError("No backends available".to_string()));
949        }
950
951        // Check if any backend can satisfy requirements
952        let resource_manager = self
953            .resource_manager
954            .lock()
955            .expect("Failed to acquire lock on resource_manager in validate_job_config");
956        let mut can_satisfy = false;
957
958        for (backend, capacity) in &resource_manager.available_resources {
959            if capacity.qubits >= config.resource_requirements.min_qubits {
960                if let Some(max_depth) = config.resource_requirements.max_depth {
961                    if let Some(backend_max_depth) = capacity.max_circuit_depth {
962                        if max_depth > backend_max_depth {
963                            continue;
964                        }
965                    }
966                }
967                can_satisfy = true;
968                break;
969            }
970        }
971
972        if !can_satisfy {
973            return Err(DeviceError::APIError(
974                "No backend can satisfy resource requirements".to_string(),
975            ));
976        }
977
978        Ok(())
979    }
980
981    async fn estimate_execution_time<const N: usize>(
982        &self,
983        circuit: &Circuit<N>,
984        shots: usize,
985        config: &JobConfig,
986    ) -> DeviceResult<Duration> {
987        // Simple estimation based on circuit complexity and historical data
988        let analyzer = CircuitAnalyzer::new();
989        let metrics = analyzer
990            .analyze(circuit)
991            .map_err(|e| DeviceError::APIError(format!("Circuit analysis error: {e:?}")))?;
992        let circuit_complexity = (metrics.gate_count as f64).mul_add(0.1, metrics.depth as f64);
993        let shots_factor = (shots as f64).log10();
994
995        // Base estimation
996        let base_time = Duration::from_secs((circuit_complexity * shots_factor) as u64);
997
998        // Adjust based on backend performance if available
999        let backend_performance = self.backend_performance.read().expect(
1000            "Failed to acquire read lock on backend_performance in estimate_execution_time",
1001        );
1002        let avg_execution_time = if backend_performance.is_empty() {
1003            Duration::from_secs(60) // Default 1 minute
1004        } else {
1005            let total_time: Duration = backend_performance
1006                .values()
1007                .map(|p| p.avg_execution_time)
1008                .sum();
1009            total_time / backend_performance.len() as u32
1010        };
1011
1012        let estimated = Duration::from_millis(
1013            u128::midpoint(base_time.as_millis(), avg_execution_time.as_millis())
1014                .try_into()
1015                .expect(
1016                    "Failed to convert estimated execution time to u64 in estimate_execution_time",
1017                ),
1018        );
1019
1020        Ok(estimated)
1021    }
1022
1023    async fn estimate_cost<const N: usize>(
1024        &self,
1025        circuit: &Circuit<N>,
1026        shots: usize,
1027        config: &JobConfig,
1028    ) -> DeviceResult<f64> {
1029        // Simple cost estimation
1030        let analyzer = CircuitAnalyzer::new();
1031        let metrics = analyzer
1032            .analyze(circuit)
1033            .map_err(|e| DeviceError::APIError(format!("Circuit analysis error: {e:?}")))?;
1034        let circuit_complexity = metrics.depth as f64 + metrics.gate_count as f64;
1035        let base_cost = circuit_complexity * shots as f64 * 0.001; // $0.001 per complexity unit
1036
1037        // Adjust based on priority
1038        let priority_multiplier = match config.priority {
1039            JobPriority::Critical => 3.0,
1040            JobPriority::High => 2.0,
1041            JobPriority::Normal => 1.0,
1042            JobPriority::Low => 0.7,
1043            JobPriority::BestEffort => 0.5,
1044        };
1045
1046        Ok(base_cost * priority_multiplier)
1047    }
1048
1049    async fn update_user_share(&self, user_id: &str, queued_delta: i32, running_delta: i32) {
1050        let mut user_shares = self
1051            .user_shares
1052            .write()
1053            .expect("Failed to acquire write lock on user_shares in update_user_share");
1054        let share = user_shares
1055            .entry(user_id.to_string())
1056            .or_insert_with(|| UserShare {
1057                user_id: user_id.to_string(),
1058                allocated_share: 1.0, // Default equal share
1059                used_share: 0.0,
1060                jobs_running: 0,
1061                jobs_queued: 0,
1062                last_updated: SystemTime::now(),
1063            });
1064
1065        share.jobs_queued = (share.jobs_queued as i32 + queued_delta).max(0) as usize;
1066        share.jobs_running = (share.jobs_running as i32 + running_delta).max(0) as usize;
1067        share.last_updated = SystemTime::now();
1068    }
1069
1070    async fn update_job_status(&self, job_id: &JobId, status: JobStatus) -> DeviceResult<()> {
1071        let mut jobs = self
1072            .jobs
1073            .write()
1074            .expect("Failed to acquire write lock on jobs in update_job_status");
1075        if let Some(job_any) = jobs.get_mut(job_id) {
1076            // This is a simplified update - in practice, we'd need proper type handling
1077            // TODO: Implement proper job status updates
1078        }
1079        Ok(())
1080    }
1081
1082    async fn ensure_scheduler_running(&self) {
1083        let running = self
1084            .scheduler_running
1085            .lock()
1086            .expect("Failed to acquire lock on scheduler_running in ensure_scheduler_running");
1087        if !*running {
1088            drop(running);
1089            let _ = self.start_scheduler().await;
1090        }
1091    }
1092
1093    async fn predict_queue_times(
1094        &self,
1095        backend_performance: &HashMap<HardwareBackend, BackendPerformance>,
1096    ) -> HashMap<HardwareBackend, Duration> {
1097        let mut predictions = HashMap::new();
1098
1099        for (backend, perf) in backend_performance {
1100            // Simple prediction based on current queue and average execution time
1101            let predicted_time = Duration::from_secs(
1102                (perf.queue_length as u64 * perf.avg_execution_time.as_secs())
1103                    / perf.success_rate.max(0.1) as u64,
1104            );
1105            predictions.insert(*backend, predicted_time);
1106        }
1107
1108        predictions
1109    }
1110
1111    async fn calculate_system_load(
1112        &self,
1113        backend_performance: &HashMap<HardwareBackend, BackendPerformance>,
1114    ) -> f64 {
1115        if backend_performance.is_empty() {
1116            return 0.0;
1117        }
1118
1119        let total_utilization: f64 = backend_performance.values().map(|p| p.utilization).sum();
1120
1121        total_utilization / backend_performance.len() as f64
1122    }
1123
1124    async fn calculate_throughput(&self) -> f64 {
1125        let history = self
1126            .execution_history
1127            .read()
1128            .expect("Failed to acquire read lock on execution_history in calculate_throughput");
1129        if history.is_empty() {
1130            return 0.0;
1131        }
1132
1133        // Calculate jobs completed in the last hour
1134        let one_hour_ago = SystemTime::now() - Duration::from_secs(3600);
1135        let recent_completions = history
1136            .iter()
1137            .filter(|exec| exec.started_at > one_hour_ago)
1138            .count();
1139
1140        recent_completions as f64
1141    }
1142
1143    async fn calculate_average_wait_time(&self) -> Duration {
1144        let history = self.execution_history.read().expect(
1145            "Failed to acquire read lock on execution_history in calculate_average_wait_time",
1146        );
1147        if history.is_empty() {
1148            return Duration::from_secs(0);
1149        }
1150
1151        let total_wait: Duration = history.iter().map(|exec| exec.metrics.queue_time).sum();
1152
1153        total_wait / history.len() as u32
1154    }
1155
1156    // Main scheduling loop
1157    async fn scheduling_loop(&self) {
1158        while *self
1159            .scheduler_running
1160            .lock()
1161            .expect("Failed to acquire lock on scheduler_running in scheduling_loop")
1162        {
1163            if let Err(e) = self.schedule_next_jobs().await {
1164                eprintln!("Scheduling error: {e}");
1165            }
1166
1167            tokio::time::sleep(Duration::from_secs(1)).await;
1168        }
1169    }
1170
1171    async fn schedule_next_jobs(&self) -> DeviceResult<()> {
1172        let params = self
1173            .params
1174            .read()
1175            .expect("Failed to acquire read lock on params in schedule_next_jobs")
1176            .clone();
1177
1178        match params.strategy {
1179            SchedulingStrategy::PriorityFIFO => self.schedule_priority_fifo().await,
1180            SchedulingStrategy::ShortestJobFirst => self.schedule_shortest_job_first().await,
1181            SchedulingStrategy::FairShare => self.schedule_fair_share().await,
1182            SchedulingStrategy::Backfill => self.schedule_backfill().await,
1183            SchedulingStrategy::MLOptimized => self.schedule_ml_optimized().await,
1184            _ => {
1185                self.schedule_priority_fifo().await // Default fallback
1186            }
1187        }
1188    }
1189
1190    async fn schedule_priority_fifo(&self) -> DeviceResult<()> {
1191        // Process jobs in priority order
1192        for priority in [
1193            JobPriority::Critical,
1194            JobPriority::High,
1195            JobPriority::Normal,
1196            JobPriority::Low,
1197            JobPriority::BestEffort,
1198        ] {
1199            let job_id = {
1200                let mut queues = self
1201                    .job_queues
1202                    .lock()
1203                    .expect("Failed to acquire lock on job_queues in schedule_priority_fifo");
1204                queues
1205                    .get_mut(&priority)
1206                    .and_then(|queue| queue.pop_front())
1207            };
1208
1209            if let Some(job_id) = job_id {
1210                if let Some(backend) = self.find_best_backend(&job_id).await? {
1211                    self.assign_job_to_backend(&job_id, backend).await?;
1212                    break;
1213                } else {
1214                    // No available backend, put job back
1215                    let mut queues = self.job_queues.lock().expect("Failed to acquire lock on job_queues to requeue job in schedule_priority_fifo");
1216                    if let Some(queue) = queues.get_mut(&priority) {
1217                        queue.push_front(job_id);
1218                    }
1219                    break;
1220                }
1221            }
1222        }
1223
1224        Ok(())
1225    }
1226
1227    async fn schedule_shortest_job_first(&self) -> DeviceResult<()> {
1228        // Implementation for shortest job first scheduling
1229        // TODO: Sort jobs by estimated execution time
1230        self.schedule_priority_fifo().await
1231    }
1232
1233    async fn schedule_fair_share(&self) -> DeviceResult<()> {
1234        // Implementation for fair share scheduling
1235        // TODO: Consider user shares when scheduling
1236        self.schedule_priority_fifo().await
1237    }
1238
1239    async fn schedule_backfill(&self) -> DeviceResult<()> {
1240        // Implementation for backfill scheduling
1241        // TODO: Fill gaps with smaller jobs
1242        self.schedule_priority_fifo().await
1243    }
1244
1245    async fn schedule_ml_optimized(&self) -> DeviceResult<()> {
1246        // Implementation for ML-optimized scheduling using SciRS2
1247        #[cfg(feature = "scirs2")]
1248        {
1249            // Use SciRS2 optimization algorithms
1250            self.scirs2_optimize_schedule().await
1251        }
1252
1253        #[cfg(not(feature = "scirs2"))]
1254        {
1255            // Fallback to priority FIFO
1256            self.schedule_priority_fifo().await
1257        }
1258    }
1259
1260    #[cfg(feature = "scirs2")]
1261    async fn scirs2_optimize_schedule(&self) -> DeviceResult<()> {
1262        // Use SciRS2 optimization for intelligent job scheduling
1263        // This would implement advanced ML-based scheduling algorithms
1264        self.schedule_priority_fifo().await // Simplified for now
1265    }
1266
1267    async fn find_best_backend(&self, job_id: &JobId) -> DeviceResult<Option<HardwareBackend>> {
1268        // Check if job exists and drop the lock immediately
1269        {
1270            let jobs = self
1271                .jobs
1272                .read()
1273                .expect("Failed to acquire read lock on jobs in find_best_backend");
1274            let _job_any = jobs
1275                .get(job_id)
1276                .ok_or_else(|| DeviceError::APIError("Job not found".to_string()))?;
1277        } // Drop the jobs lock here
1278
1279        // TODO: Proper type casting based on circuit size
1280        // For now, use simplified backend selection
1281
1282        let backends: Vec<_> = {
1283            let backends = self
1284                .backends
1285                .read()
1286                .expect("Failed to acquire read lock on backends in find_best_backend");
1287            backends.iter().copied().collect()
1288        };
1289
1290        let allocation_strategy = {
1291            let params = self
1292                .params
1293                .read()
1294                .expect("Failed to acquire read lock on params in find_best_backend");
1295            params.allocation_strategy.clone()
1296        };
1297
1298        let backend_performance_snapshot = {
1299            let backend_performance = self
1300                .backend_performance
1301                .read()
1302                .expect("Failed to acquire read lock on backend_performance in find_best_backend");
1303            backend_performance.clone()
1304        };
1305
1306        match allocation_strategy {
1307            AllocationStrategy::FirstFit => {
1308                // Return first available backend
1309                for backend in backends {
1310                    if self.is_backend_available(backend).await {
1311                        return Ok(Some(backend));
1312                    }
1313                }
1314            }
1315            AllocationStrategy::BestFit => {
1316                // Return backend with best capabilities for this job
1317                // TODO: Implement proper job requirements matching
1318                for &backend in &backends {
1319                    if self.is_backend_available(backend).await {
1320                        return Ok(Some(backend));
1321                    }
1322                }
1323            }
1324            AllocationStrategy::LeastLoaded => {
1325                // Return backend with lowest utilization
1326                let mut best_backend = None;
1327                let mut lowest_utilization = f64::INFINITY;
1328
1329                for (&backend, perf) in &backend_performance_snapshot {
1330                    if self.is_backend_available(backend).await
1331                        && perf.utilization < lowest_utilization
1332                    {
1333                        lowest_utilization = perf.utilization;
1334                        best_backend = Some(backend);
1335                    }
1336                }
1337
1338                return Ok(best_backend);
1339            }
1340            _ => {
1341                // Default to first fit
1342                for &backend in &backends {
1343                    if self.is_backend_available(backend).await {
1344                        return Ok(Some(backend));
1345                    }
1346                }
1347            }
1348        }
1349
1350        Ok(None)
1351    }
1352
1353    async fn is_backend_available(&self, backend: HardwareBackend) -> bool {
1354        let running_jobs = self
1355            .running_jobs
1356            .read()
1357            .expect("Failed to acquire read lock on running_jobs in is_backend_available");
1358        let backend_jobs = running_jobs.values().filter(|(b, _)| *b == backend).count();
1359
1360        let resource_manager = self
1361            .resource_manager
1362            .lock()
1363            .expect("Failed to acquire lock on resource_manager in is_backend_available");
1364        resource_manager
1365            .available_resources
1366            .get(&backend)
1367            .is_some_and(|capacity| backend_jobs < capacity.concurrent_jobs)
1368    }
1369
1370    async fn assign_job_to_backend(
1371        &self,
1372        job_id: &JobId,
1373        backend: HardwareBackend,
1374    ) -> DeviceResult<()> {
1375        {
1376            let mut running_jobs = self
1377                .running_jobs
1378                .write()
1379                .expect("Failed to acquire write lock on running_jobs in assign_job_to_backend");
1380            running_jobs.insert(job_id.clone(), (backend, SystemTime::now()));
1381        }
1382
1383        // Update job status
1384        self.update_job_status(job_id, JobStatus::Scheduled).await?;
1385
1386        // Send event
1387        let _ = self
1388            .event_sender
1389            .send(SchedulerEvent::JobScheduled(job_id.clone(), backend));
1390
1391        // Start job execution
1392        let job_id_clone = job_id.clone();
1393        let scheduler = Arc::new(self.clone());
1394        tokio::spawn(async move {
1395            let _ = scheduler.execute_job(&job_id_clone, backend).await;
1396        });
1397
1398        Ok(())
1399    }
1400
1401    async fn execute_job(&self, job_id: &JobId, backend: HardwareBackend) -> DeviceResult<()> {
1402        // Update job status to running
1403        self.update_job_status(job_id, JobStatus::Running).await?;
1404        let _ = self
1405            .event_sender
1406            .send(SchedulerEvent::JobStarted(job_id.clone()));
1407
1408        let execution_start = SystemTime::now();
1409
1410        // Check if backend is available
1411        {
1412            let backends = self
1413                .backends
1414                .read()
1415                .expect("Failed to acquire read lock on backends in execute_job");
1416            if !backends.contains(&backend) {
1417                return Err(DeviceError::APIError("Backend not found".to_string()));
1418            }
1419        }
1420
1421        // Execute job (simplified - would need proper type handling)
1422        // TODO: Implement proper job execution with circuit type management
1423
1424        // For now, simulate execution
1425        tokio::time::sleep(Duration::from_secs(10)).await;
1426
1427        // Clean up running job
1428        {
1429            let mut running_jobs = self
1430                .running_jobs
1431                .write()
1432                .expect("Failed to acquire write lock on running_jobs in execute_job cleanup");
1433            running_jobs.remove(job_id);
1434        }
1435
1436        // Update job status
1437        self.update_job_status(job_id, JobStatus::Completed).await?;
1438
1439        // Record execution metrics
1440        let execution_time = SystemTime::now()
1441            .duration_since(execution_start)
1442            .expect("Failed to calculate execution time duration in execute_job");
1443        // TODO: Record proper execution metrics
1444
1445        Ok(())
1446    }
1447
1448    // Performance monitoring loop
1449    async fn performance_monitoring_loop(&self) {
1450        while *self
1451            .scheduler_running
1452            .lock()
1453            .expect("Failed to acquire lock on scheduler_running in performance_monitoring_loop")
1454        {
1455            self.update_backend_performance().await;
1456            tokio::time::sleep(Duration::from_secs(30)).await;
1457        }
1458    }
1459
1460    async fn update_backend_performance(&self) {
1461        let mut backend_performance = self.backend_performance.write().expect(
1462            "Failed to acquire write lock on backend_performance in update_backend_performance",
1463        );
1464        let now = SystemTime::now();
1465
1466        for (backend, perf) in backend_performance.iter_mut() {
1467            // Update performance metrics
1468            perf.last_updated = now;
1469
1470            // Add performance snapshot
1471            let snapshot = PerformanceSnapshot {
1472                timestamp: now,
1473                queue_length: perf.queue_length,
1474                utilization: perf.utilization,
1475                avg_queue_time_secs: perf.avg_queue_time.as_secs_f64(),
1476                success_rate: perf.success_rate,
1477            };
1478
1479            perf.history.push_back(snapshot);
1480
1481            // Keep only recent history (last 24 hours)
1482            let cutoff = now - Duration::from_secs(86400);
1483            while let Some(front) = perf.history.front() {
1484                if front.timestamp < cutoff {
1485                    perf.history.pop_front();
1486                } else {
1487                    break;
1488                }
1489            }
1490        }
1491    }
1492
1493    // SciRS2 optimization loop
1494    async fn scirs2_optimization_loop(&self) {
1495        let frequency = {
1496            let params = self
1497                .params
1498                .read()
1499                .expect("Failed to acquire read lock on params in scirs2_optimization_loop");
1500            params.scirs2_params.optimization_frequency
1501        };
1502
1503        loop {
1504            let should_continue = *self
1505                .scheduler_running
1506                .lock()
1507                .expect("Failed to acquire lock on scheduler_running in scirs2_optimization_loop");
1508            if !should_continue {
1509                break;
1510            }
1511
1512            if let Err(e) = self.run_scirs2_optimization().await {
1513                eprintln!("SciRS2 optimization error: {e}");
1514            }
1515
1516            tokio::time::sleep(frequency).await;
1517        }
1518    }
1519
1520    async fn run_scirs2_optimization(&self) -> DeviceResult<()> {
1521        #[cfg(feature = "scirs2")]
1522        {
1523            // Implement SciRS2-based scheduling optimization
1524            // This would use machine learning and statistical analysis
1525            // to optimize job scheduling decisions
1526
1527            // Collect performance data
1528            let backend_performance = self.backend_performance.read().expect(
1529                "Failed to acquire read lock on backend_performance in run_scirs2_optimization",
1530            );
1531            let performance_data: Vec<f64> = backend_performance
1532                .values()
1533                .map(|p| p.utilization)
1534                .collect();
1535
1536            if performance_data.len() > 1 {
1537                // Calculate optimization metrics
1538                use scirs2_core::ndarray::Array1;
1539                let data_array = Array1::from_vec(performance_data);
1540                let avg_utilization = mean(&data_array.view());
1541                let utilization_std = std(&data_array.view(), 1, None);
1542
1543                // TODO: Implement more sophisticated SciRS2 optimization
1544                // This could include:
1545                // - Predicting optimal job placement
1546                // - Learning from historical performance
1547                // - Optimizing for multiple objectives (throughput, fairness, cost)
1548            }
1549        }
1550
1551        Ok(())
1552    }
1553}
1554
1555// Clone implementation for scheduler (simplified)
1556impl Clone for QuantumJobScheduler {
1557    fn clone(&self) -> Self {
1558        Self {
1559            params: Arc::clone(&self.params),
1560            job_queues: Arc::clone(&self.job_queues),
1561            jobs: Arc::clone(&self.jobs),
1562            backend_performance: Arc::clone(&self.backend_performance),
1563            backends: Arc::clone(&self.backends),
1564            running_jobs: Arc::clone(&self.running_jobs),
1565            execution_history: Arc::clone(&self.execution_history),
1566            user_shares: Arc::clone(&self.user_shares),
1567            scheduler_running: Arc::clone(&self.scheduler_running),
1568            event_sender: self.event_sender.clone(),
1569            performance_predictor: Arc::clone(&self.performance_predictor),
1570            resource_manager: Arc::clone(&self.resource_manager),
1571        }
1572    }
1573}
1574
1575impl PerformancePredictor {
1576    fn new() -> Self {
1577        Self {
1578            history: VecDeque::new(),
1579            model_params: HashMap::new(),
1580            accuracy_metrics: HashMap::new(),
1581        }
1582    }
1583}
1584
1585impl ResourceManager {
1586    fn new() -> Self {
1587        Self {
1588            available_resources: HashMap::new(),
1589            reservations: HashMap::new(),
1590            utilization_history: VecDeque::new(),
1591        }
1592    }
1593}
1594
1595/// Advanced machine learning algorithms for scheduling
1596#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1597pub enum MLAlgorithm {
1598    /// Linear regression for simple predictions
1599    LinearRegression,
1600    /// Support Vector Machine for classification
1601    SVM,
1602    /// Random Forest for ensemble learning
1603    RandomForest,
1604    /// Gradient Boosting for performance optimization
1605    GradientBoosting,
1606    /// Neural Network for complex patterns
1607    NeuralNetwork,
1608    /// Ensemble method combining multiple algorithms
1609    EnsembleMethod,
1610    /// Deep Reinforcement Learning
1611    DeepRL,
1612    /// Graph Neural Network for topology-aware scheduling
1613    GraphNN,
1614}
1615
1616/// Multi-objective optimization weights
1617#[derive(Debug, Clone, Serialize, Deserialize)]
1618pub struct MultiObjectiveWeights {
1619    /// Throughput optimization weight
1620    pub throughput: f64,
1621    /// Cost minimization weight
1622    pub cost: f64,
1623    /// Energy efficiency weight
1624    pub energy: f64,
1625    /// Fairness weight
1626    pub fairness: f64,
1627    /// SLA compliance weight
1628    pub sla_compliance: f64,
1629    /// Quality of service weight
1630    pub qos: f64,
1631}
1632
1633/// Reinforcement Learning parameters
1634#[derive(Debug, Clone, Serialize, Deserialize)]
1635pub struct RLParameters {
1636    /// Learning rate
1637    pub learning_rate: f64,
1638    /// Discount factor
1639    pub discount_factor: f64,
1640    /// Exploration rate
1641    pub exploration_rate: f64,
1642    /// Episode length
1643    pub episode_length: usize,
1644    /// Reward function weights
1645    pub reward_weights: HashMap<String, f64>,
1646    /// State representation dimension
1647    pub state_dimension: usize,
1648    /// Action space size
1649    pub action_space_size: usize,
1650}
1651
1652/// Genetic Algorithm parameters
1653#[derive(Debug, Clone, Serialize, Deserialize)]
1654pub struct GAParameters {
1655    /// Population size
1656    pub population_size: usize,
1657    /// Number of generations
1658    pub generations: usize,
1659    /// Crossover probability
1660    pub crossover_prob: f64,
1661    /// Mutation probability
1662    pub mutation_prob: f64,
1663    /// Selection strategy
1664    pub selection_strategy: String,
1665    /// Elite size
1666    pub elite_size: usize,
1667}
1668
1669/// Feature engineering parameters
1670#[derive(Debug, Clone, Serialize, Deserialize)]
1671pub struct FeatureParams {
1672    /// Enable time-based features
1673    pub enable_temporal_features: bool,
1674    /// Enable circuit complexity features
1675    pub enable_complexity_features: bool,
1676    /// Enable user behavior features
1677    pub enable_user_features: bool,
1678    /// Enable platform performance features
1679    pub enable_platform_features: bool,
1680    /// Enable historical pattern features
1681    pub enable_historical_features: bool,
1682    /// Feature normalization method
1683    pub normalization_method: String,
1684    /// Feature selection threshold
1685    pub selection_threshold: f64,
1686}
1687
1688/// Service Level Agreement configuration
1689#[derive(Debug, Clone, Serialize, Deserialize)]
1690pub struct SLAConfig {
1691    /// Maximum allowed queue time
1692    pub max_queue_time: Duration,
1693    /// Maximum allowed execution time
1694    pub max_execution_time: Duration,
1695    /// Minimum required availability
1696    pub min_availability: f64,
1697    /// Penalty for SLA violations
1698    pub violation_penalty: f64,
1699    /// SLA tier (Gold, Silver, Bronze)
1700    pub tier: SLATier,
1701}
1702
1703/// SLA tier levels
1704#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1705pub enum SLATier {
1706    Gold,
1707    Silver,
1708    Bronze,
1709    Basic,
1710}
1711
1712/// Default implementations for new types
1713impl Default for MultiObjectiveWeights {
1714    fn default() -> Self {
1715        Self {
1716            throughput: 0.3,
1717            cost: 0.2,
1718            energy: 0.15,
1719            fairness: 0.15,
1720            sla_compliance: 0.1,
1721            qos: 0.1,
1722        }
1723    }
1724}
1725
1726impl Default for RLParameters {
1727    fn default() -> Self {
1728        Self {
1729            learning_rate: 0.001,
1730            discount_factor: 0.95,
1731            exploration_rate: 0.1,
1732            episode_length: 1000,
1733            reward_weights: [
1734                ("throughput".to_string(), 1.0),
1735                ("fairness".to_string(), 0.5),
1736                ("cost".to_string(), -0.3),
1737            ]
1738            .into_iter()
1739            .collect(),
1740            state_dimension: 64,
1741            action_space_size: 16,
1742        }
1743    }
1744}
1745
1746impl Default for GAParameters {
1747    fn default() -> Self {
1748        Self {
1749            population_size: 50,
1750            generations: 100,
1751            crossover_prob: 0.8,
1752            mutation_prob: 0.1,
1753            selection_strategy: "tournament".to_string(),
1754            elite_size: 5,
1755        }
1756    }
1757}
1758
1759impl Default for FeatureParams {
1760    fn default() -> Self {
1761        Self {
1762            enable_temporal_features: true,
1763            enable_complexity_features: true,
1764            enable_user_features: true,
1765            enable_platform_features: true,
1766            enable_historical_features: true,
1767            normalization_method: "z_score".to_string(),
1768            selection_threshold: 0.1,
1769        }
1770    }
1771}
1772
1773// Convenience functions for creating common job configurations
1774
1775/// Create a high-priority quantum job configuration
1776pub fn create_high_priority_config(max_execution_time: Duration) -> JobConfig {
1777    JobConfig {
1778        priority: JobPriority::High,
1779        max_execution_time,
1780        retry_attempts: 5,
1781        ..Default::default()
1782    }
1783}
1784
1785/// Create a best-effort quantum job configuration for batch processing
1786pub fn create_batch_job_config() -> JobConfig {
1787    JobConfig {
1788        priority: JobPriority::BestEffort,
1789        max_execution_time: Duration::from_secs(3600 * 24), // 24 hours
1790        max_queue_time: None,                               // No queue time limit
1791        retry_attempts: 1,
1792        ..Default::default()
1793    }
1794}
1795
1796/// Create job configuration for real-time quantum applications
1797pub fn create_realtime_config() -> JobConfig {
1798    JobConfig {
1799        priority: JobPriority::Critical,
1800        max_execution_time: Duration::from_secs(60), // 1 minute
1801        max_queue_time: Some(Duration::from_secs(30)), // 30 seconds max wait
1802        retry_attempts: 0,                           // No retries for real-time
1803        ..Default::default()
1804    }
1805}
1806
1807/// Create SLA-aware job configuration for enterprise workloads
1808pub fn create_sla_aware_config(tier: SLATier) -> JobConfig {
1809    let (priority, max_execution_time, max_queue_time, retry_attempts) = match tier {
1810        SLATier::Gold => (
1811            JobPriority::Critical,
1812            Duration::from_secs(300),
1813            Some(Duration::from_secs(60)),
1814            5,
1815        ),
1816        SLATier::Silver => (
1817            JobPriority::High,
1818            Duration::from_secs(600),
1819            Some(Duration::from_secs(300)),
1820            3,
1821        ),
1822        SLATier::Bronze => (
1823            JobPriority::Normal,
1824            Duration::from_secs(1800),
1825            Some(Duration::from_secs(900)),
1826            2,
1827        ),
1828        SLATier::Basic => (
1829            JobPriority::Low,
1830            Duration::from_secs(3600),
1831            Some(Duration::from_secs(1800)),
1832            1,
1833        ),
1834    };
1835
1836    JobConfig {
1837        priority,
1838        max_execution_time,
1839        max_queue_time,
1840        retry_attempts,
1841        ..Default::default()
1842    }
1843}
1844
1845/// Create cost-optimized job configuration for budget-conscious workloads
1846pub fn create_cost_optimized_config(budget_limit: f64) -> JobConfig {
1847    JobConfig {
1848        priority: JobPriority::BestEffort,
1849        max_execution_time: Duration::from_secs(7200), // 2 hours
1850        max_queue_time: None,                          // No queue limit for cost optimization
1851        retry_attempts: 1,
1852        cost_limit: Some(budget_limit),
1853        preferred_backends: vec![], // Let cost optimizer choose
1854        ..Default::default()
1855    }
1856}
1857
1858/// Create energy-efficient job configuration for green computing
1859pub fn create_energy_efficient_config() -> JobConfig {
1860    JobConfig {
1861        priority: JobPriority::Low,
1862        max_execution_time: Duration::from_secs(3600), // 1 hour
1863        max_queue_time: None,                          // Wait for renewable energy availability
1864        retry_attempts: 2,
1865        tags: std::iter::once(("energy_profile".to_string(), "green".to_string())).collect(),
1866        ..Default::default()
1867    }
1868}
1869
1870/// Create research-focused job configuration with fault tolerance
1871pub fn create_research_config() -> JobConfig {
1872    JobConfig {
1873        priority: JobPriority::Normal,
1874        max_execution_time: Duration::from_secs(14400), // 4 hours
1875        max_queue_time: Some(Duration::from_secs(7200)), // 2 hours max wait
1876        retry_attempts: 3,
1877        tags: [
1878            ("workload_type".to_string(), "research".to_string()),
1879            ("fault_tolerance".to_string(), "high".to_string()),
1880        ]
1881        .into_iter()
1882        .collect(),
1883        ..Default::default()
1884    }
1885}
1886
1887/// Create deadline-sensitive job configuration
1888pub fn create_deadline_config(deadline: SystemTime) -> JobConfig {
1889    JobConfig {
1890        priority: JobPriority::High,
1891        max_execution_time: Duration::from_secs(1800), // 30 minutes
1892        max_queue_time: Some(Duration::from_secs(300)), // 5 minutes max wait
1893        retry_attempts: 2,
1894        deadline: Some(deadline),
1895        tags: std::iter::once((
1896            "scheduling_type".to_string(),
1897            "deadline_sensitive".to_string(),
1898        ))
1899        .collect(),
1900        ..Default::default()
1901    }
1902}
1903
1904/// Create machine learning training job configuration
1905pub fn create_ml_training_config() -> JobConfig {
1906    JobConfig {
1907        priority: JobPriority::Normal,
1908        max_execution_time: Duration::from_secs(21600), // 6 hours
1909        max_queue_time: Some(Duration::from_secs(3600)), // 1 hour max wait
1910        retry_attempts: 2,
1911        resource_requirements: ResourceRequirements {
1912            min_qubits: 20, // Typically need more qubits for ML
1913            max_depth: Some(1000),
1914            min_fidelity: Some(0.95),
1915            memory_mb: Some(16384), // 16GB for ML workloads
1916            cpu_cores: Some(8),
1917            required_features: vec![
1918                "variational_circuits".to_string(),
1919                "parametric_gates".to_string(),
1920            ],
1921            ..Default::default()
1922        },
1923        tags: [
1924            ("workload_type".to_string(), "machine_learning".to_string()),
1925            ("resource_intensive".to_string(), "true".to_string()),
1926        ]
1927        .into_iter()
1928        .collect(),
1929        ..Default::default()
1930    }
1931}
1932
1933/// Create optimization problem job configuration
1934pub fn create_optimization_config() -> JobConfig {
1935    JobConfig {
1936        priority: JobPriority::Normal,
1937        max_execution_time: Duration::from_secs(10800), // 3 hours
1938        max_queue_time: Some(Duration::from_secs(1800)), // 30 minutes max wait
1939        retry_attempts: 3,
1940        resource_requirements: ResourceRequirements {
1941            min_qubits: 10,
1942            max_depth: Some(500),
1943            min_fidelity: Some(0.90),
1944            required_features: vec!["qaoa".to_string(), "variational_algorithms".to_string()],
1945            ..Default::default()
1946        },
1947        tags: [
1948            ("workload_type".to_string(), "optimization".to_string()),
1949            ("algorithm_type".to_string(), "variational".to_string()),
1950        ]
1951        .into_iter()
1952        .collect(),
1953        ..Default::default()
1954    }
1955}
1956
1957/// Create simulation job configuration for large-scale quantum simulation
1958pub fn create_simulation_config(qubit_count: usize) -> JobConfig {
1959    let (max_execution_time, memory_requirement) = match qubit_count {
1960        1..=20 => (Duration::from_secs(3600), 4096), // 1 hour, 4GB
1961        21..=30 => (Duration::from_secs(7200), 8192), // 2 hours, 8GB
1962        31..=40 => (Duration::from_secs(14400), 16384), // 4 hours, 16GB
1963        _ => (Duration::from_secs(28800), 32768),    // 8 hours, 32GB
1964    };
1965
1966    JobConfig {
1967        priority: JobPriority::Low, // Simulations can be background tasks
1968        max_execution_time,
1969        max_queue_time: None, // Flexible queue time for simulations
1970        retry_attempts: 1,
1971        resource_requirements: ResourceRequirements {
1972            min_qubits: qubit_count,
1973            memory_mb: Some(memory_requirement),
1974            cpu_cores: Some(16), // High CPU for simulation
1975            required_features: vec!["high_precision".to_string(), "large_circuits".to_string()],
1976            ..Default::default()
1977        },
1978        tags: [
1979            ("workload_type".to_string(), "simulation".to_string()),
1980            ("qubit_count".to_string(), qubit_count.to_string()),
1981        ]
1982        .into_iter()
1983        .collect(),
1984        ..Default::default()
1985    }
1986}
1987
1988#[cfg(test)]
1989mod tests {
1990    use super::*;
1991    use quantrs2_circuit::prelude::CircuitBuilder;
1992
1993    #[tokio::test]
1994    async fn test_job_scheduler_creation() {
1995        let params = SchedulingParams::default();
1996        let scheduler = QuantumJobScheduler::new(params);
1997        assert!(!*scheduler
1998            .scheduler_running
1999            .lock()
2000            .expect("Failed to acquire lock on scheduler_running in test"));
2001    }
2002
2003    #[tokio::test]
2004    async fn test_job_config_validation() {
2005        let config = JobConfig::default();
2006        assert_eq!(config.priority, JobPriority::Normal);
2007        assert_eq!(config.retry_attempts, 3);
2008        assert!(config.dependencies.is_empty());
2009    }
2010
2011    #[tokio::test]
2012    async fn test_priority_ordering() {
2013        assert!(JobPriority::Critical < JobPriority::High);
2014        assert!(JobPriority::High < JobPriority::Normal);
2015        assert!(JobPriority::Normal < JobPriority::Low);
2016        assert!(JobPriority::Low < JobPriority::BestEffort);
2017    }
2018
2019    #[test]
2020    fn test_job_id_creation() {
2021        let id1 = JobId::new();
2022        let id2 = JobId::new();
2023        assert_ne!(id1, id2);
2024        assert!(!id1.0.is_empty());
2025    }
2026
2027    #[test]
2028    fn test_convenience_configs() {
2029        let high_priority = create_high_priority_config(Duration::from_secs(300));
2030        assert_eq!(high_priority.priority, JobPriority::High);
2031        assert_eq!(high_priority.retry_attempts, 5);
2032
2033        let batch = create_batch_job_config();
2034        assert_eq!(batch.priority, JobPriority::BestEffort);
2035        assert!(batch.max_queue_time.is_none());
2036
2037        let realtime = create_realtime_config();
2038        assert_eq!(realtime.priority, JobPriority::Critical);
2039        assert_eq!(realtime.retry_attempts, 0);
2040    }
2041}
quantrs2_device/job_scheduling.rs

quantrs2_device/
job_scheduling.rs
