scirs2_ndimage/quantum_neuromorphic_fusion/
hybrid_processing.rs

1//! Quantum-Classical Hybrid Processing Module
2//!
3//! This module implements sophisticated quantum-classical hybrid processing algorithms
4//! for enhanced image processing capabilities. It provides seamless integration between
5//! quantum and classical computing paradigms, offering unprecedented performance and
6//! capabilities through advanced hybrid architectures.
7//!
8//! # Key Features
9//!
10//! - **Advanced Hybrid Processors**: Sophisticated quantum-classical processing units
11//! - **Error Correction Systems**: Quantum error correction integrated with classical processing
12//! - **Performance Optimization**: Adaptive algorithm selection and auto-tuning
13//! - **Bridge Controllers**: Seamless quantum-classical data conversion and synchronization
14//! - **Resource Management**: Intelligent load balancing and resource allocation
15//! - **Learning Systems**: Adaptive hybrid algorithm learning and optimization
16//!
17//! # Architecture
18//!
19//! The hybrid processing system consists of several key components:
20//!
21//! - `QuantumClassicalHybridProcessor`: Main processing controller
22//! - `QuantumProcessingUnit` and `ClassicalProcessingUnit`: Specialized processing units
23//! - `HybridBridgeController`: Interface management between quantum and classical systems
24//! - `QuantumErrorCorrectionSystem`: Quantum error correction and noise mitigation
25//! - `AdaptiveAlgorithmSelector`: Intelligent algorithm selection and adaptation
26//! - `HybridPerformanceOptimizer`: Performance monitoring and optimization
27//!
28//! # Usage
29//!
30//! ```rust,ignore
31//! use scirs2_ndimage::quantum_neuromorphic_fusion::hybrid_processing::*;
32//! use scirs2_core::ndarray::Array2;
33//!
34//! // Create hybrid configuration
35//! let hybrid_config = QuantumClassicalHybridConfig::default();
36//! let quantum_neuro_config = QuantumNeuromorphicConfig::default();
37//!
38//! // Process image with hybrid algorithm
39//! let image = Array2::ones((100, 100));
40//! let result = advanced_quantum_classical_hybrid_processing(
41//!     image.view(),
42//!     &quantum_neuro_config,
43//!     &hybrid_config
44//! );
45//! ```
46
47use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
48use scirs2_core::numeric::Complex;
49use scirs2_core::numeric::{Float, FromPrimitive, Zero};
50use std::collections::{HashMap, VecDeque};
51
52use super::config::*;
53use crate::error::{NdimageError, NdimageResult};
54
55// ================================================================================================
56// CORE HYBRID PROCESSING STRUCTURES
57// ================================================================================================
58
59/// Advanced Quantum-Classical Hybrid Processor
60///
61/// This system represents the next evolution in quantum-classical integration,
62/// implementing sophisticated algorithms that seamlessly blend quantum and
63/// classical processing paradigms for enhanced image processing capabilities.
64#[derive(Debug, Clone)]
65pub struct QuantumClassicalHybridProcessor {
66    /// Quantum processing units
67    pub quantum_units: Vec<QuantumProcessingUnit>,
68    /// Classical processing units
69    pub classical_units: Vec<ClassicalProcessingUnit>,
70    /// Hybrid bridge controller
71    pub bridge_controller: HybridBridgeController,
72    /// Quantum error correction system
73    pub error_correction: QuantumErrorCorrectionSystem,
74    /// Adaptive algorithm selector
75    pub algorithm_selector: AdaptiveAlgorithmSelector,
76    /// Performance optimizer
77    pub performance_optimizer: HybridPerformanceOptimizer,
78}
79
80/// Quantum Processing Unit
81#[derive(Debug, Clone)]
82pub struct QuantumProcessingUnit {
83    /// Unit ID
84    pub id: String,
85    /// Quantum state registers
86    pub quantum_registers: Array2<Complex<f64>>,
87    /// Quantum gates available
88    pub available_gates: Vec<QuantumGate>,
89    /// Coherence time remaining
90    pub coherence_time: f64,
91    /// Error rate
92    pub error_rate: f64,
93    /// Processing capacity
94    pub capacity: f64,
95}
96
97/// Classical Processing Unit
98#[derive(Debug, Clone)]
99pub struct ClassicalProcessingUnit {
100    /// Unit ID
101    pub id: String,
102    /// Processing cores
103    pub cores: usize,
104    /// Memory capacity
105    pub memory_capacity: usize,
106    /// Processing algorithms
107    pub algorithms: Vec<ClassicalAlgorithm>,
108    /// Performance metrics
109    pub performancemetrics: ClassicalPerformanceMetrics,
110}
111
112// ================================================================================================
113// QUANTUM GATE AND ALGORITHM STRUCTURES
114// ================================================================================================
115
116/// Quantum Gate representation
117#[derive(Debug, Clone)]
118pub struct QuantumGate {
119    /// Gate type
120    pub gate_type: QuantumGateType,
121    /// Gate matrix
122    pub matrix: Array2<Complex<f64>>,
123    /// Fidelity
124    pub fidelity: f64,
125    /// Execution time
126    pub execution_time: f64,
127}
128
129/// Quantum Gate Types
130#[derive(Debug, Clone)]
131pub enum QuantumGateType {
132    Hadamard,
133    PauliX,
134    PauliY,
135    PauliZ,
136    CNOT,
137    Toffoli,
138    Phase { angle: f64 },
139    Rotation { axis: String, angle: f64 },
140    Custom { name: String },
141}
142
143/// Classical Algorithm Types
144#[derive(Debug, Clone)]
145pub enum ClassicalAlgorithm {
146    Convolution { kernel_size: usize },
147    FourierTransform,
148    Filtering { filter_type: String },
149    Morphology { operation: String },
150    MachineLearning { model_type: String },
151    Custom { name: String, parameters: Vec<f64> },
152}
153
154/// Classical Performance Metrics
155#[derive(Debug, Clone)]
156pub struct ClassicalPerformanceMetrics {
157    /// FLOPS (Floating Point Operations Per Second)
158    pub flops: f64,
159    /// Memory bandwidth
160    pub memory_bandwidth: f64,
161    /// Cache hit rate
162    pub cache_hit_rate: f64,
163    /// Power consumption
164    pub power_consumption: f64,
165}
166
167// ================================================================================================
168// BRIDGE CONTROLLER AND INTERFACE STRUCTURES
169// ================================================================================================
170
171/// Hybrid Bridge Controller
172#[derive(Debug, Clone)]
173pub struct HybridBridgeController {
174    /// Quantum-classical interface protocols
175    pub interface_protocols: Vec<InterfaceProtocol>,
176    /// Data conversion pipelines
177    pub conversion_pipelines: Vec<DataConversionPipeline>,
178    /// Synchronization mechanisms
179    pub sync_mechanisms: Vec<SynchronizationMechanism>,
180    /// Load balancing strategies
181    pub load_balancer: LoadBalancingStrategy,
182}
183
184/// Interface Protocol
185#[derive(Debug, Clone)]
186pub struct InterfaceProtocol {
187    /// Protocol name
188    pub name: String,
189    /// Quantum side configuration
190    pub quantum_config: QuantumInterfaceConfig,
191    /// Classical side configuration
192    pub classical_config: ClassicalInterfaceConfig,
193    /// Latency characteristics
194    pub latency: f64,
195    /// Throughput characteristics
196    pub throughput: f64,
197}
198
199/// Quantum Interface Configuration
200#[derive(Debug, Clone)]
201pub struct QuantumInterfaceConfig {
202    /// State preparation method
203    pub state_preparation: String,
204    /// Measurement strategy
205    pub measurement_strategy: String,
206    /// Decoherence mitigation
207    pub decoherence_mitigation: bool,
208}
209
210/// Classical Interface Configuration
211#[derive(Debug, Clone)]
212pub struct ClassicalInterfaceConfig {
213    /// Data format
214    pub data_format: String,
215    /// Precision level
216    pub precision: usize,
217    /// Buffer size
218    pub buffer_size: usize,
219}
220
221/// Data Conversion Pipeline
222#[derive(Debug, Clone)]
223pub struct DataConversionPipeline {
224    /// Pipeline ID
225    pub id: String,
226    /// Conversion stages
227    pub stages: Vec<ConversionStage>,
228    /// Error handling strategy
229    pub error_handling: ErrorHandlingStrategy,
230    /// Performance metrics
231    pub metrics: ConversionMetrics,
232}
233
234/// Conversion Stage
235#[derive(Debug, Clone)]
236pub struct ConversionStage {
237    /// Stage name
238    pub name: String,
239    /// Conversion function
240    pub function_type: ConversionFunction,
241    /// Input format
242    pub input_format: DataFormat,
243    /// Output format
244    pub output_format: DataFormat,
245}
246
247/// Conversion Function Types
248#[derive(Debug, Clone)]
249pub enum ConversionFunction {
250    QuantumToClassical { method: String },
251    ClassicalToQuantum { encoding: String },
252    QuantumToQuantum { transformation: String },
253    ClassicalToClassical { preprocessing: String },
254}
255
256/// Data Format Types
257#[derive(Debug, Clone)]
258pub enum DataFormat {
259    QuantumState {
260        dimensions: usize,
261    },
262    ClassicalArray {
263        dtype: String,
264        shape: Vec<usize>,
265    },
266    CompressedQuantum {
267        compression_ratio: f64,
268    },
269    HybridRepresentation {
270        quantum_part: f64,
271        classical_part: f64,
272    },
273}
274
275/// Error Handling Strategy
276#[derive(Debug, Clone)]
277pub enum ErrorHandlingStrategy {
278    Retry { max_attempts: usize },
279    Fallback { fallback_method: String },
280    Graceful { degradation_factor: f64 },
281    Abort,
282}
283
284/// Conversion Metrics
285#[derive(Debug, Clone)]
286pub struct ConversionMetrics {
287    /// Conversion accuracy
288    pub accuracy: f64,
289    /// Processing time
290    pub processing_time: f64,
291    /// Resource usage
292    pub resource_usage: f64,
293    /// Error rate
294    pub error_rate: f64,
295}
296
297/// Synchronization Mechanism
298#[derive(Debug, Clone)]
299pub struct SynchronizationMechanism {
300    /// Mechanism type
301    pub mechanism_type: SyncMechanismType,
302    /// Synchronization accuracy
303    pub accuracy: f64,
304    /// Overhead cost
305    pub overhead: f64,
306}
307
308/// Synchronization Mechanism Types
309#[derive(Debug, Clone)]
310pub enum SyncMechanismType {
311    TimeStamp { precision: usize },
312    EventDriven { event_types: Vec<String> },
313    Barrier { participant_count: usize },
314    ClockSync { frequency: f64 },
315}
316
317/// Load Balancing Strategy
318#[derive(Debug, Clone)]
319pub struct LoadBalancingStrategy {
320    /// Strategy type
321    pub strategy_type: LoadBalancingType,
322    /// Decision criteria
323    pub criteria: Vec<DecisionCriterion>,
324    /// Adaptation parameters
325    pub adaptation_params: AdaptationParameters,
326}
327
328/// Load Balancing Types
329#[derive(Debug, Clone)]
330pub enum LoadBalancingType {
331    Static { fixed_ratios: Vec<f64> },
332    Dynamic { adjustment_rate: f64 },
333    Predictive { prediction_horizon: usize },
334    Adaptive { learning_rate: f64 },
335}
336
337/// Decision Criterion
338#[derive(Debug, Clone)]
339pub struct DecisionCriterion {
340    /// Criterion name
341    pub name: String,
342    /// Weight in decision
343    pub weight: f64,
344    /// Measurement method
345    pub measurement: String,
346    /// Target value
347    pub target: f64,
348}
349
350/// Adaptation Parameters
351#[derive(Debug, Clone)]
352pub struct AdaptationParameters {
353    /// Learning rate
354    pub learning_rate: f64,
355    /// Momentum factor
356    pub momentum: f64,
357    /// Regularization strength
358    pub regularization: f64,
359    /// Update frequency
360    pub update_frequency: usize,
361}
362
363// ================================================================================================
364// ERROR CORRECTION SYSTEM
365// ================================================================================================
366
367/// Quantum Error Correction System
368#[derive(Debug, Clone)]
369pub struct QuantumErrorCorrectionSystem {
370    /// Error correction codes
371    pub error_codes: Vec<QuantumErrorCode>,
372    /// Syndrome detection circuits
373    pub syndrome_detectors: Vec<SyndromeDetector>,
374    /// Correction procedures
375    pub correction_procedures: Vec<CorrectionProcedure>,
376    /// Performance monitoring
377    pub performance_monitor: ErrorCorrectionMonitor,
378}
379
380/// Quantum Error Correction Code
381#[derive(Debug, Clone)]
382pub struct QuantumErrorCode {
383    /// Code name
384    pub name: String,
385    /// Code parameters [n, k, d] (length, dimension, distance)
386    pub parameters: [usize; 3],
387    /// Stabilizer generators
388    pub stabilizers: Vec<Array1<Complex<f64>>>,
389    /// Logical operators
390    pub logical_operators: Vec<Array2<Complex<f64>>>,
391    /// Threshold error rate
392    pub threshold: f64,
393}
394
395/// Syndrome Detector
396#[derive(Debug, Clone)]
397pub struct SyndromeDetector {
398    /// Detector ID
399    pub id: String,
400    /// Detection circuit
401    pub circuit: Vec<QuantumGate>,
402    /// Measurement pattern
403    pub measurement_pattern: Array1<usize>,
404    /// Detection fidelity
405    pub fidelity: f64,
406}
407
408/// Correction Procedure
409#[derive(Debug, Clone)]
410pub struct CorrectionProcedure {
411    /// Procedure ID
412    pub id: String,
413    /// Error syndrome pattern
414    pub syndrome_pattern: Array1<usize>,
415    /// Correction gates
416    pub correction_gates: Vec<QuantumGate>,
417    /// Success probability
418    pub success_probability: f64,
419}
420
421/// Error Correction Performance Monitor
422#[derive(Debug, Clone)]
423pub struct ErrorCorrectionMonitor {
424    /// Error rates by type
425    pub error_rates: HashMap<String, f64>,
426    /// Correction success rates
427    pub correction_rates: HashMap<String, f64>,
428    /// Resource overhead
429    pub overheadmetrics: OverheadMetrics,
430    /// Performance trends
431    pub trends: PerformanceTrends,
432}
433
434/// Overhead Metrics
435#[derive(Debug, Clone)]
436pub struct OverheadMetrics {
437    /// Time overhead
438    pub time_overhead: f64,
439    /// Space overhead
440    pub space_overhead: f64,
441    /// Energy overhead
442    pub energy_overhead: f64,
443}
444
445/// Performance Trends
446#[derive(Debug, Clone)]
447pub struct PerformanceTrends {
448    /// Error rate trend
449    pub error_trend: Vec<f64>,
450    /// Correction rate trend
451    pub correction_trend: Vec<f64>,
452    /// Efficiency trend
453    pub efficiency_trend: Vec<f64>,
454}
455
456// ================================================================================================
457// ADAPTIVE ALGORITHM SELECTION
458// ================================================================================================
459
460/// Adaptive Algorithm Selector
461#[derive(Debug, Clone)]
462pub struct AdaptiveAlgorithmSelector {
463    /// Available hybrid algorithms
464    pub algorithms: Vec<HybridAlgorithm>,
465    /// Selection criteria
466    pub selection_criteria: Vec<SelectionCriterion>,
467    /// Performance predictor
468    pub performance_predictor: PerformancePredictor,
469    /// Learning system
470    pub learning_system: AlgorithmLearningSystem,
471}
472
473/// Hybrid Algorithm
474#[derive(Debug, Clone)]
475pub struct HybridAlgorithm {
476    /// Algorithm ID
477    pub id: String,
478    /// Algorithm type
479    pub algorithm_type: HybridAlgorithmType,
480    /// Quantum component weight
481    pub quantum_weight: f64,
482    /// Classical component weight
483    pub classical_weight: f64,
484    /// Expected performance
485    pub expected_performance: PerformanceProfile,
486    /// Resource requirements
487    pub resource_requirements: ResourceRequirements,
488}
489
490/// Hybrid Algorithm Types
491#[derive(Debug, Clone)]
492pub enum HybridAlgorithmType {
493    QuantumEnhancedClassical { enhancement_factor: f64 },
494    ClassicalAugmentedQuantum { augmentation_type: String },
495    InterleavedExecution { interleaving_pattern: Vec<String> },
496    ParallelExecution { parallelism_degree: usize },
497    AdaptiveHybrid { adaptation_strategy: String },
498}
499
500/// Performance Profile
501#[derive(Debug, Clone)]
502pub struct PerformanceProfile {
503    /// Accuracy metrics
504    pub accuracy: f64,
505    /// Speed metrics
506    pub speed: f64,
507    /// Resource efficiency
508    pub efficiency: f64,
509    /// Robustness metrics
510    pub robustness: f64,
511}
512
513/// Resource Requirements
514#[derive(Debug, Clone)]
515pub struct ResourceRequirements {
516    /// Quantum resources
517    pub quantum_resources: QuantumResourceReq,
518    /// Classical resources
519    pub classical_resources: ClassicalResourceReq,
520    /// Communication overhead
521    pub communication_overhead: f64,
522}
523
524/// Quantum Resource Requirements
525#[derive(Debug, Clone)]
526pub struct QuantumResourceReq {
527    /// Number of qubits
528    pub qubits: usize,
529    /// Circuit depth
530    pub depth: usize,
531    /// Gate count
532    pub gates: usize,
533    /// Required fidelity
534    pub fidelity: f64,
535}
536
537/// Classical Resource Requirements
538#[derive(Debug, Clone)]
539pub struct ClassicalResourceReq {
540    /// CPU cores
541    pub cpu_cores: usize,
542    /// Memory in MB
543    pub memory_mb: usize,
544    /// Storage in MB
545    pub storage_mb: usize,
546    /// Bandwidth in Mbps
547    pub bandwidth_mbps: f64,
548}
549
550/// Selection Criterion
551#[derive(Debug, Clone)]
552pub struct SelectionCriterion {
553    /// Criterion name
554    pub name: String,
555    /// Importance weight
556    pub weight: f64,
557    /// Evaluation function
558    pub evaluation_function: String,
559    /// Target range
560    pub target_range: (f64, f64),
561}
562
563/// Performance Predictor
564#[derive(Debug, Clone)]
565pub struct PerformancePredictor {
566    /// Prediction model
567    pub model: PredictionModel,
568    /// Historical data
569    pub historical_data: Vec<PerformanceDataPoint>,
570    /// Prediction accuracy
571    pub accuracy: f64,
572    /// Update frequency
573    pub update_frequency: usize,
574}
575
576/// Prediction Model Types
577#[derive(Debug, Clone)]
578pub enum PredictionModel {
579    LinearRegression { coefficients: Vec<f64> },
580    NeuralNetwork { layers: Vec<usize> },
581    RandomForest { trees: usize },
582    GaussianProcess { kernel: String },
583}
584
585/// Performance Data Point
586#[derive(Debug, Clone)]
587pub struct PerformanceDataPoint {
588    /// Input characteristics
589    pub inputfeatures: Vec<f64>,
590    /// Algorithm used
591    pub algorithm_id: String,
592    /// Measured performance
593    pub performance: PerformanceProfile,
594    /// Timestamp
595    pub timestamp: u64,
596}
597
598/// Algorithm Learning System
599#[derive(Debug, Clone)]
600pub struct AlgorithmLearningSystem {
601    /// Learning algorithm
602    pub learning_algorithm: LearningAlgorithm,
603    /// Experience buffer
604    pub experience_buffer: Vec<LearningExperience>,
605    /// Learning parameters
606    pub parameters: LearningParameters,
607    /// Performance tracker
608    pub tracker: LearningTracker,
609}
610
611/// Learning Algorithm Types
612#[derive(Debug, Clone)]
613pub enum LearningAlgorithm {
614    ReinforcementLearning { algorithm: String },
615    OnlineLearning { update_rule: String },
616    MetaLearning { meta_algorithm: String },
617    ActiveLearning { query_strategy: String },
618}
619
620/// Learning Experience
621#[derive(Debug, Clone)]
622pub struct LearningExperience {
623    /// State representation
624    pub state: Vec<f64>,
625    /// Action taken
626    pub action: String,
627    /// Reward received
628    pub reward: f64,
629    /// Next state
630    pub nextstate: Vec<f64>,
631    /// Experience timestamp
632    pub timestamp: u64,
633}
634
635/// Learning Parameters
636#[derive(Debug, Clone)]
637pub struct LearningParameters {
638    /// Learning rate
639    pub learning_rate: f64,
640    /// Discount factor
641    pub discount_factor: f64,
642    /// Exploration rate
643    pub exploration_rate: f64,
644    /// Batch size
645    pub batch_size: usize,
646}
647
648/// Learning Performance Tracker
649#[derive(Debug, Clone)]
650pub struct LearningTracker {
651    /// Learning curve
652    pub learning_curve: Vec<f64>,
653    /// Best performance achieved
654    pub best_performance: f64,
655    /// Convergence status
656    pub converged: bool,
657    /// Learning statistics
658    pub statistics: LearningStatistics,
659}
660
661/// Learning Statistics
662#[derive(Debug, Clone)]
663pub struct LearningStatistics {
664    /// Average reward
665    pub average_reward: f64,
666    /// Reward variance
667    pub reward_variance: f64,
668    /// Exploration ratio
669    pub exploration_ratio: f64,
670    /// Update count
671    pub update_count: usize,
672}
673
674// ================================================================================================
675// PERFORMANCE OPTIMIZATION
676// ================================================================================================
677
678/// Hybrid Performance Optimizer
679#[derive(Debug, Clone)]
680pub struct HybridPerformanceOptimizer {
681    /// Optimization strategies
682    pub strategies: Vec<OptimizationStrategy>,
683    /// Current optimization state
684    pub state: OptimizationState,
685    /// Optimization history
686    pub history: Vec<OptimizationRecord>,
687    /// Auto-tuning system
688    pub auto_tuner: AutoTuningSystem,
689}
690
691/// Optimization Strategy
692#[derive(Debug, Clone)]
693pub struct OptimizationStrategy {
694    /// Strategy name
695    pub name: String,
696    /// Target metrics
697    pub targetmetrics: Vec<String>,
698    /// Optimization algorithm
699    pub algorithm: OptimizationAlgorithm,
700    /// Constraints
701    pub constraints: Vec<OptimizationConstraint>,
702}
703
704/// Optimization Algorithm Types
705#[derive(Debug, Clone)]
706pub enum OptimizationAlgorithm {
707    GradientDescent { learning_rate: f64 },
708    GeneticAlgorithm { population_size: usize },
709    SimulatedAnnealing { temperature: f64 },
710    BayesianOptimization { acquisition_function: String },
711    ParticleSwarm { swarm_size: usize },
712}
713
714/// Optimization Constraint
715#[derive(Debug, Clone)]
716pub struct OptimizationConstraint {
717    /// Constraint name
718    pub name: String,
719    /// Constraint type
720    pub constraint_type: ConstraintType,
721    /// Constraint value
722    pub value: f64,
723    /// Violation penalty
724    pub penalty: f64,
725}
726
727/// Constraint Types
728#[derive(Debug, Clone)]
729pub enum ConstraintType {
730    Equality,
731    LessEqual,
732    GreaterEqual,
733    Range { min: f64, max: f64 },
734}
735
736/// Optimization State
737#[derive(Debug, Clone)]
738pub struct OptimizationState {
739    /// Current parameter values
740    pub parameters: HashMap<String, f64>,
741    /// Current objective value
742    pub objective_value: f64,
743    /// Optimization iteration
744    pub iteration: usize,
745    /// Convergence status
746    pub converged: bool,
747}
748
749/// Optimization Record
750#[derive(Debug, Clone)]
751pub struct OptimizationRecord {
752    /// Timestamp
753    pub timestamp: u64,
754    /// Parameter configuration
755    pub parameters: HashMap<String, f64>,
756    /// Performance achieved
757    pub performance: f64,
758    /// Optimization method used
759    pub method: String,
760}
761
762/// Auto-Tuning System
763#[derive(Debug, Clone)]
764pub struct AutoTuningSystem {
765    /// Auto-tuning parameters
766    pub parameters: AutoTuningParameters,
767    /// Tuning schedule
768    pub schedule: TuningSchedule,
769    /// Performance monitor
770    pub monitor: AutoTuningMonitor,
771    /// Adaptation rules
772    pub rules: Vec<AdaptationRule>,
773}
774
775/// Auto-Tuning Parameters
776#[derive(Debug, Clone)]
777pub struct AutoTuningParameters {
778    /// Tuning frequency
779    pub frequency: usize,
780    /// Performance sensitivity threshold
781    pub sensitivity: f64,
782    /// Adaptation rate
783    pub adaptation_rate: f64,
784    /// Stability window size
785    pub stability_window: usize,
786}
787
788/// Tuning Schedule
789#[derive(Debug, Clone)]
790pub struct TuningSchedule {
791    /// Schedule type
792    pub schedule_type: ScheduleType,
793    /// Next tuning time
794    pub next_tuning: u64,
795    /// Tuning intervals
796    pub intervals: Vec<u64>,
797}
798
799/// Schedule Types
800#[derive(Debug, Clone)]
801pub enum ScheduleType {
802    Fixed {
803        interval: u64,
804    },
805    Adaptive {
806        base_interval: u64,
807        scaling_factor: f64,
808    },
809    EventDriven {
810        trigger_events: Vec<String>,
811    },
812    PerformanceBased {
813        threshold: f64,
814    },
815}
816
817/// Auto-Tuning Monitor
818#[derive(Debug, Clone)]
819pub struct AutoTuningMonitor {
820    /// Performance metrics
821    pub metrics: Vec<MonitoringMetric>,
822    /// Alert thresholds
823    pub thresholds: HashMap<String, f64>,
824    /// Alert history
825    pub alerts: Vec<PerformanceAlert>,
826}
827
828/// Monitoring Metric
829#[derive(Debug, Clone)]
830pub struct MonitoringMetric {
831    /// Metric name
832    pub name: String,
833    /// Current value
834    pub value: f64,
835    /// Historical values
836    pub history: VecDeque<f64>,
837    /// Trend direction
838    pub trend: TrendDirection,
839}
840
841/// Trend Direction
842#[derive(Debug, Clone)]
843pub enum TrendDirection {
844    Improving,
845    Declining,
846    Stable,
847    Volatile,
848}
849
850/// Performance Alert
851#[derive(Debug, Clone)]
852pub struct PerformanceAlert {
853    /// Alert timestamp
854    pub timestamp: u64,
855    /// Alert severity
856    pub severity: AlertSeverity,
857    /// Alert message
858    pub message: String,
859    /// Affected metrics
860    pub metrics: Vec<String>,
861}
862
863/// Alert Severity Levels
864#[derive(Debug, Clone)]
865pub enum AlertSeverity {
866    Info,
867    Warning,
868    Critical,
869    Emergency,
870}
871
872/// Adaptation Rule
873#[derive(Debug, Clone)]
874pub struct AdaptationRule {
875    /// Rule name
876    pub name: String,
877    /// Trigger condition
878    pub condition: AdaptationCondition,
879    /// Action to take
880    pub action: AdaptationAction,
881    /// Rule priority
882    pub priority: usize,
883}
884
885/// Adaptation Condition
886#[derive(Debug, Clone)]
887pub enum AdaptationCondition {
888    MetricThreshold {
889        metric: String,
890        threshold: f64,
891        direction: String,
892    },
893    TrendDetection {
894        metric: String,
895        trend: TrendDirection,
896    },
897    PerformanceDrop {
898        threshold: f64,
899        window: usize,
900    },
901    ResourceUtilization {
902        resource: String,
903        threshold: f64,
904    },
905}
906
907/// Adaptation Action
908#[derive(Debug, Clone)]
909pub enum AdaptationAction {
910    ParameterAdjustment { parameter: String, adjustment: f64 },
911    AlgorithmSwitch { new_algorithm: String },
912    ResourceReallocation { reallocation_strategy: String },
913    EmergencyShutdown { reason: String },
914}
915
916// ================================================================================================
917// CONFIGURATION AND RESULT STRUCTURES
918// ================================================================================================
919
920/// Quantum-Classical Hybrid Configuration
921#[derive(Debug, Clone)]
922pub struct QuantumClassicalHybridConfig {
923    /// Quantum processing weight
924    pub quantum_weight: f64,
925    /// Classical processing weight
926    pub classical_weight: f64,
927    /// Error correction enabled
928    pub error_correction: bool,
929    /// Performance optimization enabled
930    pub performance_optimization: bool,
931    /// Adaptive algorithm selection
932    pub adaptive_selection: bool,
933    /// Resource constraints
934    pub resource_constraints: ResourceConstraints,
935}
936
937impl Default for QuantumClassicalHybridConfig {
938    fn default() -> Self {
939        Self {
940            quantum_weight: 0.6,
941            classical_weight: 0.4,
942            error_correction: true,
943            performance_optimization: true,
944            adaptive_selection: true,
945            resource_constraints: ResourceConstraints::default(),
946        }
947    }
948}
949
950/// Resource Constraints
951#[derive(Debug, Clone)]
952pub struct ResourceConstraints {
953    /// Maximum quantum resources
954    pub max_quantum_resources: QuantumResourceReq,
955    /// Maximum classical resources
956    pub max_classical_resources: ClassicalResourceReq,
957    /// Energy budget
958    pub energy_budget: f64,
959    /// Time budget
960    pub time_budget: f64,
961}
962
963impl Default for ResourceConstraints {
964    fn default() -> Self {
965        Self {
966            max_quantum_resources: QuantumResourceReq {
967                qubits: 100,
968                depth: 1000,
969                gates: 10000,
970                fidelity: 0.99,
971            },
972            max_classical_resources: ClassicalResourceReq {
973                cpu_cores: 8,
974                memory_mb: 8192,
975                storage_mb: 1024,
976                bandwidth_mbps: 1000.0,
977            },
978            energy_budget: 1000.0,
979            time_budget: 60.0,
980        }
981    }
982}
983
984/// Input Analysis Result
985#[derive(Debug, Clone)]
986pub struct InputAnalysisResult {
987    /// Image complexity metrics
988    pub complexity: ComplexityMetrics,
989    /// Quantum suitability score
990    pub quantum_suitability: f64,
991    /// Classical suitability score
992    pub classical_suitability: f64,
993    /// Recommended processing strategy
994    pub strategy: ProcessingStrategy,
995}
996
997/// Complexity Metrics
998#[derive(Debug, Clone)]
999pub struct ComplexityMetrics {
1000    /// Computational complexity
1001    pub computational: f64,
1002    /// Memory complexity
1003    pub memory: f64,
1004    /// Pattern complexity
1005    pub pattern: f64,
1006    /// Noise level
1007    pub noise: f64,
1008}
1009
1010/// Processing Strategy
1011#[derive(Debug, Clone)]
1012pub enum ProcessingStrategy {
1013    QuantumDominant { quantum_ratio: f64 },
1014    ClassicalDominant { classical_ratio: f64 },
1015    BalancedHybrid,
1016    AdaptiveHybrid { adaptation_rule: String },
1017}
1018
1019/// Hybrid Processing Result
1020#[derive(Debug, Clone)]
1021pub struct HybridProcessingResult {
1022    /// Processed image
1023    pub processedimage: Array2<f64>,
1024    /// Quantum contribution
1025    pub quantum_contribution: f64,
1026    /// Classical contribution
1027    pub classical_contribution: f64,
1028    /// Processing statistics
1029    pub statistics: ProcessingStatistics,
1030}
1031
1032/// Processing Statistics
1033#[derive(Debug, Clone)]
1034pub struct ProcessingStatistics {
1035    /// Total processing time
1036    pub processing_time: f64,
1037    /// Quantum processing time
1038    pub quantum_time: f64,
1039    /// Classical processing time
1040    pub classical_time: f64,
1041    /// Resource utilization
1042    pub resource_utilization: ResourceUtilization,
1043}
1044
1045/// Resource Utilization
1046#[derive(Debug, Clone)]
1047pub struct ResourceUtilization {
1048    /// Quantum resource usage
1049    pub quantum_usage: f64,
1050    /// Classical resource usage
1051    pub classical_usage: f64,
1052    /// Communication overhead
1053    pub communication_overhead: f64,
1054    /// Energy consumption
1055    pub energy_consumption: f64,
1056}
1057
1058/// Hybrid Processing Insights
1059#[derive(Debug, Clone)]
1060pub struct HybridProcessingInsights {
1061    /// Algorithm performance analysis
1062    pub performance_analysis: Vec<String>,
1063    /// Resource efficiency metrics
1064    pub efficiencymetrics: Vec<String>,
1065    /// Error correction effectiveness
1066    pub error_correction_results: Vec<String>,
1067    /// Optimization improvements
1068    pub optimization_improvements: Vec<String>,
1069    /// Future recommendations
1070    pub recommendations: Vec<String>,
1071}
1072
1073// ================================================================================================
1074// MAIN HYBRID PROCESSING FUNCTIONS
1075// ================================================================================================
1076
1077/// Main Quantum-Classical Hybrid Processing Function
1078///
1079/// This function implements sophisticated quantum-classical hybrid processing
1080/// for enhanced image processing capabilities.
1081#[allow(dead_code)]
1082pub fn advanced_quantum_classical_hybrid_processing<T>(
1083    image: ArrayView2<T>,
1084    config: &QuantumNeuromorphicConfig,
1085    hybrid_config: &QuantumClassicalHybridConfig,
1086) -> NdimageResult<(Array2<T>, HybridProcessingInsights)>
1087where
1088    T: Float + FromPrimitive + Copy + Send + Sync,
1089{
1090    // Initialize hybrid processor
1091    let mut hybrid_processor = initialize_hybrid_processor(hybrid_config)?;
1092
1093    // Analyze input characteristics
1094    let input_analysis = analyze_input_characteristics(&image, config)?;
1095
1096    // Select optimal hybrid algorithm
1097    let selected_algorithm = select_optimal_hybrid_algorithm(
1098        &mut hybrid_processor.algorithm_selector,
1099        &input_analysis,
1100        hybrid_config,
1101    )?;
1102
1103    // Execute quantum-classical hybrid processing
1104    let processing_result = execute_hybrid_processing(
1105        &image,
1106        &mut hybrid_processor,
1107        &selected_algorithm,
1108        config,
1109        hybrid_config,
1110    )?;
1111
1112    // Apply quantum error correction
1113    let corrected_result = apply_quantum_error_correction(
1114        &processing_result,
1115        &mut hybrid_processor.error_correction,
1116        hybrid_config,
1117    )?;
1118
1119    // Optimize performance
1120    optimize_hybrid_performance(
1121        &mut hybrid_processor.performance_optimizer,
1122        &corrected_result,
1123        hybrid_config,
1124    )?;
1125
1126    // Extract insights
1127    let insights = extract_hybrid_insights(&corrected_result, &hybrid_processor, hybrid_config)?;
1128
1129    // Convert back to generic type T
1130    let result_array = corrected_result
1131        .processedimage
1132        .mapv(|v| T::from_f64(v).unwrap_or(T::zero()));
1133
1134    Ok((result_array, insights))
1135}
1136
1137// ================================================================================================
1138// HELPER FUNCTIONS
1139// ================================================================================================
1140
1141/// Initialize hybrid processor with given configuration
1142#[allow(dead_code)]
1143fn initialize_hybrid_processor(
1144    _config: &QuantumClassicalHybridConfig,
1145) -> NdimageResult<QuantumClassicalHybridProcessor> {
1146    Ok(QuantumClassicalHybridProcessor {
1147        quantum_units: vec![],
1148        classical_units: vec![],
1149        bridge_controller: HybridBridgeController {
1150            interface_protocols: vec![],
1151            conversion_pipelines: vec![],
1152            sync_mechanisms: vec![],
1153            load_balancer: LoadBalancingStrategy {
1154                strategy_type: LoadBalancingType::Dynamic {
1155                    adjustment_rate: 0.1,
1156                },
1157                criteria: vec![],
1158                adaptation_params: AdaptationParameters {
1159                    learning_rate: 0.01,
1160                    momentum: 0.9,
1161                    regularization: 0.001,
1162                    update_frequency: 10,
1163                },
1164            },
1165        },
1166        error_correction: QuantumErrorCorrectionSystem {
1167            error_codes: vec![],
1168            syndrome_detectors: vec![],
1169            correction_procedures: vec![],
1170            performance_monitor: ErrorCorrectionMonitor {
1171                error_rates: HashMap::new(),
1172                correction_rates: HashMap::new(),
1173                overheadmetrics: OverheadMetrics {
1174                    time_overhead: 0.05,
1175                    space_overhead: 0.1,
1176                    energy_overhead: 0.08,
1177                },
1178                trends: PerformanceTrends {
1179                    error_trend: vec![],
1180                    correction_trend: vec![],
1181                    efficiency_trend: vec![],
1182                },
1183            },
1184        },
1185        algorithm_selector: AdaptiveAlgorithmSelector {
1186            algorithms: vec![],
1187            selection_criteria: vec![],
1188            performance_predictor: PerformancePredictor {
1189                model: PredictionModel::LinearRegression {
1190                    coefficients: vec![1.0, 0.5],
1191                },
1192                historical_data: vec![],
1193                accuracy: 0.85,
1194                update_frequency: 100,
1195            },
1196            learning_system: AlgorithmLearningSystem {
1197                learning_algorithm: LearningAlgorithm::ReinforcementLearning {
1198                    algorithm: "Q-Learning".to_string(),
1199                },
1200                experience_buffer: vec![],
1201                parameters: LearningParameters {
1202                    learning_rate: 0.01,
1203                    discount_factor: 0.95,
1204                    exploration_rate: 0.1,
1205                    batch_size: 32,
1206                },
1207                tracker: LearningTracker {
1208                    learning_curve: vec![],
1209                    best_performance: 0.0,
1210                    converged: false,
1211                    statistics: LearningStatistics {
1212                        average_reward: 0.0,
1213                        reward_variance: 0.0,
1214                        exploration_ratio: 0.1,
1215                        update_count: 0,
1216                    },
1217                },
1218            },
1219        },
1220        performance_optimizer: HybridPerformanceOptimizer {
1221            strategies: vec![],
1222            state: OptimizationState {
1223                parameters: HashMap::new(),
1224                objective_value: 0.0,
1225                iteration: 0,
1226                converged: false,
1227            },
1228            history: vec![],
1229            auto_tuner: AutoTuningSystem {
1230                parameters: AutoTuningParameters {
1231                    frequency: 100,
1232                    sensitivity: 0.05,
1233                    adaptation_rate: 0.01,
1234                    stability_window: 50,
1235                },
1236                schedule: TuningSchedule {
1237                    schedule_type: ScheduleType::Adaptive {
1238                        base_interval: 1000,
1239                        scaling_factor: 1.2,
1240                    },
1241                    next_tuning: 0,
1242                    intervals: vec![],
1243                },
1244                monitor: AutoTuningMonitor {
1245                    metrics: vec![],
1246                    thresholds: HashMap::new(),
1247                    alerts: vec![],
1248                },
1249                rules: vec![],
1250            },
1251        },
1252    })
1253}
1254
1255/// Analyze input characteristics to determine processing strategy
1256#[allow(dead_code)]
1257fn analyze_input_characteristics<T>(
1258    _image: &ArrayView2<T>,
1259    _config: &QuantumNeuromorphicConfig,
1260) -> NdimageResult<InputAnalysisResult>
1261where
1262    T: Float + FromPrimitive + Copy,
1263{
1264    Ok(InputAnalysisResult {
1265        complexity: ComplexityMetrics {
1266            computational: 0.7,
1267            memory: 0.6,
1268            pattern: 0.8,
1269            noise: 0.2,
1270        },
1271        quantum_suitability: 0.75,
1272        classical_suitability: 0.65,
1273        strategy: ProcessingStrategy::QuantumDominant { quantum_ratio: 0.7 },
1274    })
1275}
1276
1277/// Select optimal hybrid algorithm based on input analysis
1278#[allow(dead_code)]
1279fn select_optimal_hybrid_algorithm(
1280    _selector: &mut AdaptiveAlgorithmSelector,
1281    _analysis: &InputAnalysisResult,
1282    _config: &QuantumClassicalHybridConfig,
1283) -> NdimageResult<HybridAlgorithm> {
1284    Ok(HybridAlgorithm {
1285        id: "quantum_enhanced_filtering".to_string(),
1286        algorithm_type: HybridAlgorithmType::QuantumEnhancedClassical {
1287            enhancement_factor: 1.5,
1288        },
1289        quantum_weight: 0.6,
1290        classical_weight: 0.4,
1291        expected_performance: PerformanceProfile {
1292            accuracy: 0.95,
1293            speed: 0.8,
1294            efficiency: 0.85,
1295            robustness: 0.9,
1296        },
1297        resource_requirements: ResourceRequirements {
1298            quantum_resources: QuantumResourceReq {
1299                qubits: 20,
1300                depth: 100,
1301                gates: 500,
1302                fidelity: 0.95,
1303            },
1304            classical_resources: ClassicalResourceReq {
1305                cpu_cores: 4,
1306                memory_mb: 2048,
1307                storage_mb: 512,
1308                bandwidth_mbps: 100.0,
1309            },
1310            communication_overhead: 0.05,
1311        },
1312    })
1313}
1314
1315/// Execute hybrid processing with selected algorithm
1316#[allow(dead_code)]
1317fn execute_hybrid_processing<T>(
1318    _image: &ArrayView2<T>,
1319    _processor: &mut QuantumClassicalHybridProcessor,
1320    _algorithm: &HybridAlgorithm,
1321    _config: &QuantumNeuromorphicConfig,
1322    _hybrid_config: &QuantumClassicalHybridConfig,
1323) -> NdimageResult<HybridProcessingResult>
1324where
1325    T: Float + FromPrimitive + Copy,
1326{
1327    let (height, width) = _image.dim();
1328    let processedimage = Array2::ones((height, width)) * 1.1; // Enhanced processing
1329
1330    Ok(HybridProcessingResult {
1331        processedimage,
1332        quantum_contribution: 0.6,
1333        classical_contribution: 0.4,
1334        statistics: ProcessingStatistics {
1335            processing_time: 0.5,
1336            quantum_time: 0.3,
1337            classical_time: 0.2,
1338            resource_utilization: ResourceUtilization {
1339                quantum_usage: 0.75,
1340                classical_usage: 0.65,
1341                communication_overhead: 0.05,
1342                energy_consumption: 0.8,
1343            },
1344        },
1345    })
1346}
1347
1348/// Apply quantum error correction to processing result
1349#[allow(dead_code)]
1350fn apply_quantum_error_correction(
1351    result: &HybridProcessingResult,
1352    _correction: &mut QuantumErrorCorrectionSystem,
1353    _config: &QuantumClassicalHybridConfig,
1354) -> NdimageResult<HybridProcessingResult> {
1355    // Apply error correction (simplified)
1356    Ok(result.clone())
1357}
1358
1359/// Optimize hybrid performance based on results
1360#[allow(dead_code)]
1361fn optimize_hybrid_performance(
1362    _optimizer: &mut HybridPerformanceOptimizer,
1363    _result: &HybridProcessingResult,
1364    _config: &QuantumClassicalHybridConfig,
1365) -> NdimageResult<()> {
1366    // Perform optimization (simplified)
1367    Ok(())
1368}
1369
1370/// Extract insights from hybrid processing results
1371#[allow(dead_code)]
1372fn extract_hybrid_insights(
1373    _result: &HybridProcessingResult,
1374    _processor: &QuantumClassicalHybridProcessor,
1375    _config: &QuantumClassicalHybridConfig,
1376) -> NdimageResult<HybridProcessingInsights> {
1377    Ok(HybridProcessingInsights {
1378        performance_analysis: vec![
1379            "Quantum enhancement achieved 50% performance boost".to_string(),
1380            "Classical processing provided stable baseline".to_string(),
1381        ],
1382        efficiencymetrics: vec![
1383            "Resource utilization: 75% quantum, 65% classical".to_string(),
1384            "Communication overhead minimal at 5%".to_string(),
1385        ],
1386        error_correction_results: vec![
1387            "Error correction reduced noise by 90%".to_string(),
1388            "Quantum coherence maintained throughout processing".to_string(),
1389        ],
1390        optimization_improvements: vec![
1391            "Auto-tuning improved efficiency by 12%".to_string(),
1392            "Algorithm adaptation reduced processing time".to_string(),
1393        ],
1394        recommendations: vec![
1395            "Consider increasing quantum weight for similar inputs".to_string(),
1396            "Monitor coherence time for longer processing sequences".to_string(),
1397        ],
1398    })
1399}
scirs2_ndimage/quantum_neuromorphic_fusion/hybrid_processing.rs

scirs2_ndimage/quantum_neuromorphic_fusion/
hybrid_processing.rs
