quantrs2_device/dynamical_decoupling/
noise.rs

1//! Noise characterization and analysis for dynamical decoupling
2
3use scirs2_core::ndarray::{Array1, Array2, Array3};
4use std::collections::HashMap;
5use std::time::Duration;
6
7use super::{
8    config::{DDNoiseConfig, NoiseType},
9    performance::DDPerformanceAnalysis,
10    sequences::DDSequence,
11};
12use crate::DeviceResult;
13
14/// Noise analysis results for DD sequences
15#[derive(Debug, Clone)]
16pub struct DDNoiseAnalysis {
17    /// Noise characterization results
18    pub noise_characterization: NoiseCharacterization,
19    /// Spectral analysis results
20    pub spectral_analysis: Option<SpectralAnalysis>,
21    /// Temporal correlation analysis
22    pub temporal_analysis: Option<TemporalAnalysis>,
23    /// Spatial correlation analysis
24    pub spatial_analysis: Option<SpatialAnalysis>,
25    /// Non-Markovian analysis
26    pub non_markovian_analysis: Option<NonMarkovianAnalysis>,
27    /// Noise suppression effectiveness
28    pub suppression_effectiveness: SuppressionEffectiveness,
29}
30
31/// Comprehensive noise characterization
32#[derive(Debug, Clone)]
33pub struct NoiseCharacterization {
34    /// Noise types present
35    pub noise_types: HashMap<NoiseType, NoiseCharacteristics>,
36    /// Dominant noise sources
37    pub dominant_sources: Vec<NoiseSource>,
38    /// Noise environment classification
39    pub environment_classification: NoiseEnvironmentClassification,
40    /// Time-dependent noise parameters
41    pub time_dependent_parameters: TimeDependentNoiseParameters,
42}
43
44/// Characteristics of a specific noise type
45#[derive(Debug, Clone)]
46pub struct NoiseCharacteristics {
47    /// Noise strength
48    pub strength: f64,
49    /// Frequency characteristics
50    pub frequency_profile: FrequencyProfile,
51    /// Temporal characteristics
52    pub temporal_profile: TemporalProfile,
53    /// Spatial characteristics
54    pub spatial_profile: Option<SpatialProfile>,
55    /// Statistical properties
56    pub statistical_properties: NoiseStatistics,
57}
58
59/// Frequency profile of noise
60#[derive(Debug, Clone)]
61pub struct FrequencyProfile {
62    /// Power spectral density
63    pub power_spectral_density: Array1<f64>,
64    /// Frequency bins
65    pub frequency_bins: Array1<f64>,
66    /// Dominant frequencies
67    pub dominant_frequencies: Vec<f64>,
68    /// Spectral shape classification
69    pub spectral_shape: SpectralShape,
70}
71
72/// Spectral shape classification
73#[derive(Debug, Clone, PartialEq)]
74pub enum SpectralShape {
75    /// White noise (flat spectrum)
76    White,
77    /// Pink noise (1/f)
78    Pink,
79    /// Red noise (1/f²)
80    Red,
81    /// Blue noise (f)
82    Blue,
83    /// Violet noise (f²)
84    Violet,
85    /// Custom spectrum
86    Custom,
87}
88
89/// Temporal profile of noise
90#[derive(Debug, Clone)]
91pub struct TemporalProfile {
92    /// Autocorrelation function
93    pub autocorrelation: Array1<f64>,
94    /// Correlation time
95    pub correlation_time: Duration,
96    /// Non-Gaussianity measures
97    pub non_gaussianity: NonGaussianityMeasures,
98    /// Intermittency characteristics
99    pub intermittency: IntermittencyCharacteristics,
100}
101
102/// Non-Gaussianity measures
103#[derive(Debug, Clone)]
104pub struct NonGaussianityMeasures {
105    /// Skewness
106    pub skewness: f64,
107    /// Kurtosis
108    pub kurtosis: f64,
109    /// Higher order cumulants
110    pub higher_cumulants: Array1<f64>,
111    /// Non-Gaussianity index
112    pub non_gaussianity_index: f64,
113}
114
115/// Intermittency characteristics
116#[derive(Debug, Clone)]
117pub struct IntermittencyCharacteristics {
118    /// Burst duration statistics
119    pub burst_durations: Array1<f64>,
120    /// Quiet period statistics
121    pub quiet_periods: Array1<f64>,
122    /// Intermittency factor
123    pub intermittency_factor: f64,
124    /// Burst intensity distribution
125    pub burst_intensity: Array1<f64>,
126}
127
128/// Spatial profile of noise
129#[derive(Debug, Clone)]
130pub struct SpatialProfile {
131    /// Spatial correlation matrix
132    pub spatial_correlation: Array2<f64>,
133    /// Correlation length
134    pub correlation_length: f64,
135    /// Spatial coherence
136    pub spatial_coherence: f64,
137    /// Spatial gradient
138    pub spatial_gradient: Array1<f64>,
139}
140
141/// Statistical properties of noise
142#[derive(Debug, Clone)]
143pub struct NoiseStatistics {
144    /// Mean value
145    pub mean: f64,
146    /// Variance
147    pub variance: f64,
148    /// Distribution type
149    pub distribution_type: NoiseDistribution,
150    /// Distribution parameters
151    pub distribution_parameters: Array1<f64>,
152    /// Entropy measures
153    pub entropy_measures: EntropyMeasures,
154}
155
156/// Noise distribution types
157#[derive(Debug, Clone, PartialEq)]
158pub enum NoiseDistribution {
159    Gaussian,
160    Uniform,
161    Exponential,
162    Gamma,
163    Beta,
164    LogNormal,
165    Levy,
166    StudentT,
167    Custom,
168}
169
170/// Entropy measures
171#[derive(Debug, Clone)]
172pub struct EntropyMeasures {
173    /// Shannon entropy
174    pub shannon_entropy: f64,
175    /// Renyi entropy
176    pub renyi_entropy: Array1<f64>,
177    /// Tsallis entropy
178    pub tsallis_entropy: f64,
179    /// Differential entropy
180    pub differential_entropy: f64,
181}
182
183/// Noise source identification
184#[derive(Debug, Clone)]
185pub struct NoiseSource {
186    /// Source type
187    pub source_type: NoiseSourceType,
188    /// Source strength
189    pub strength: f64,
190    /// Source location
191    pub location: Option<NoiseSourceLocation>,
192    /// Frequency range
193    pub frequency_range: (f64, f64),
194    /// Mitigation strategies
195    pub mitigation_strategies: Vec<String>,
196}
197
198/// Types of noise sources
199#[derive(Debug, Clone, PartialEq)]
200pub enum NoiseSourceType {
201    /// Environmental electromagnetic interference
202    ElectromagneticInterference,
203    /// Thermal fluctuations
204    ThermalFluctuations,
205    /// Voltage fluctuations
206    VoltageFluctuations,
207    /// Mechanical vibrations
208    MechanicalVibrations,
209    /// Control system noise
210    ControlSystemNoise,
211    /// Quantum shot noise
212    QuantumShotNoise,
213    /// Cross-talk between qubits
214    CrossTalk,
215    /// Measurement back-action
216    MeasurementBackAction,
217    /// Unknown source
218    Unknown,
219}
220
221/// Noise source location
222#[derive(Debug, Clone)]
223pub struct NoiseSourceLocation {
224    /// Physical coordinates
225    pub coordinates: Array1<f64>,
226    /// Affected qubits
227    pub affected_qubits: Vec<quantrs2_core::qubit::QubitId>,
228    /// Propagation pattern
229    pub propagation_pattern: PropagationPattern,
230}
231
232/// Noise propagation pattern
233#[derive(Debug, Clone, PartialEq)]
234pub enum PropagationPattern {
235    Local,
236    Nearest,
237    Global,
238    Exponential,
239    PowerLaw,
240    Custom,
241}
242
243/// Noise environment classification
244#[derive(Debug, Clone)]
245pub struct NoiseEnvironmentClassification {
246    /// Environment type
247    pub environment_type: NoiseEnvironmentType,
248    /// Complexity score
249    pub complexity_score: f64,
250    /// Predictability score
251    pub predictability_score: f64,
252    /// Stationarity assessment
253    pub stationarity: StationarityAssessment,
254}
255
256/// Types of noise environments
257#[derive(Debug, Clone, PartialEq)]
258pub enum NoiseEnvironmentType {
259    /// Simple Markovian noise
260    SimpleMarkovian,
261    /// Complex Markovian noise
262    ComplexMarkovian,
263    /// Non-Markovian noise
264    NonMarkovian,
265    /// Hybrid environment
266    Hybrid,
267    /// Exotic environment
268    Exotic,
269}
270
271/// Stationarity assessment
272#[derive(Debug, Clone)]
273pub struct StationarityAssessment {
274    /// Is stationary
275    pub is_stationary: bool,
276    /// Stationarity confidence
277    pub confidence: f64,
278    /// Non-stationary components
279    pub non_stationary_components: Vec<NonStationaryComponent>,
280    /// Trend analysis
281    pub trend_analysis: TrendAnalysis,
282}
283
284/// Non-stationary component
285#[derive(Debug, Clone)]
286pub struct NonStationaryComponent {
287    /// Component type
288    pub component_type: NonStationaryType,
289    /// Time scale
290    pub time_scale: Duration,
291    /// Strength variation
292    pub strength_variation: f64,
293    /// Frequency drift
294    pub frequency_drift: f64,
295}
296
297/// Types of non-stationary behavior
298#[derive(Debug, Clone, PartialEq)]
299pub enum NonStationaryType {
300    LinearTrend,
301    PeriodicModulation,
302    RandomWalk,
303    Switching,
304    Burst,
305    Drift,
306}
307
308/// Trend analysis
309#[derive(Debug, Clone)]
310pub struct TrendAnalysis {
311    /// Linear trend slope
312    pub linear_slope: f64,
313    /// Trend significance
314    pub trend_significance: f64,
315    /// Cyclical components
316    pub cyclical_components: Vec<CyclicalComponent>,
317    /// Change points
318    pub change_points: Vec<ChangePoint>,
319}
320
321/// Cyclical component
322#[derive(Debug, Clone)]
323pub struct CyclicalComponent {
324    /// Period
325    pub period: Duration,
326    /// Amplitude
327    pub amplitude: f64,
328    /// Phase
329    pub phase: f64,
330    /// Confidence
331    pub confidence: f64,
332}
333
334/// Change point in noise characteristics
335#[derive(Debug, Clone)]
336pub struct ChangePoint {
337    /// Time of change
338    pub time: Duration,
339    /// Change magnitude
340    pub magnitude: f64,
341    /// Change type
342    pub change_type: ChangeType,
343    /// Detection confidence
344    pub confidence: f64,
345}
346
347/// Types of changes
348#[derive(Debug, Clone, PartialEq)]
349pub enum ChangeType {
350    MeanShift,
351    VarianceChange,
352    DistributionChange,
353    CorrelationChange,
354    FrequencyShift,
355}
356
357/// Time-dependent noise parameters
358#[derive(Debug, Clone)]
359pub struct TimeDependentNoiseParameters {
360    /// Parameter evolution
361    pub parameter_evolution: HashMap<String, Array1<f64>>,
362    /// Evolution time grid
363    pub time_grid: Array1<f64>,
364    /// Parameter correlations
365    pub parameter_correlations: Array2<f64>,
366    /// Prediction model
367    pub prediction_model: Option<ParameterPredictionModel>,
368}
369
370/// Parameter prediction model
371#[derive(Debug, Clone)]
372pub struct ParameterPredictionModel {
373    /// Model type
374    pub model_type: PredictionModelType,
375    /// Model parameters
376    pub model_parameters: Array1<f64>,
377    /// Prediction accuracy
378    pub accuracy: f64,
379    /// Prediction horizon
380    pub horizon: Duration,
381}
382
383/// Types of prediction models
384#[derive(Debug, Clone, PartialEq)]
385pub enum PredictionModelType {
386    AutoRegressive,
387    MovingAverage,
388    ARIMA,
389    StateSpace,
390    NeuralNetwork,
391    GaussianProcess,
392}
393
394/// Spectral analysis results
395#[derive(Debug, Clone)]
396pub struct SpectralAnalysis {
397    /// Power spectral density analysis
398    pub psd_analysis: PSDAnalysis,
399    /// Cross-spectral analysis
400    pub cross_spectral_analysis: CrossSpectralAnalysis,
401    /// Coherence analysis
402    pub coherence_analysis: CoherenceAnalysis,
403    /// Spectral features
404    pub spectral_features: SpectralFeatures,
405}
406
407/// Power spectral density analysis
408#[derive(Debug, Clone)]
409pub struct PSDAnalysis {
410    /// PSD estimate
411    pub psd_estimate: Array1<f64>,
412    /// Frequency bins
413    pub frequency_bins: Array1<f64>,
414    /// Confidence intervals
415    pub confidence_intervals: Array2<f64>,
416    /// Peak detection
417    pub peaks: Vec<SpectralPeak>,
418}
419
420/// Spectral peak
421#[derive(Debug, Clone)]
422pub struct SpectralPeak {
423    /// Peak frequency
424    pub frequency: f64,
425    /// Peak power
426    pub power: f64,
427    /// Peak width
428    pub width: f64,
429    /// Peak significance
430    pub significance: f64,
431}
432
433/// Cross-spectral analysis
434#[derive(Debug, Clone)]
435pub struct CrossSpectralAnalysis {
436    /// Cross-power spectral density
437    pub cross_psd: Array2<f64>,
438    /// Phase relationships
439    pub phase_relationships: Array2<f64>,
440    /// Frequency coupling
441    pub frequency_coupling: Array2<f64>,
442    /// Coherence function
443    pub coherence_function: Array1<f64>,
444}
445
446/// Coherence analysis
447#[derive(Debug, Clone)]
448pub struct CoherenceAnalysis {
449    /// Coherence matrix
450    pub coherence_matrix: Array2<f64>,
451    /// Significant coherences
452    pub significant_coherences: Vec<(usize, usize, f64)>,
453    /// Coherence network
454    pub coherence_network: CoherenceNetwork,
455}
456
457/// Coherence network
458#[derive(Debug, Clone)]
459pub struct CoherenceNetwork {
460    /// Network adjacency matrix
461    pub adjacency_matrix: Array2<f64>,
462    /// Network metrics
463    pub network_metrics: NetworkMetrics,
464    /// Community structure
465    pub communities: Vec<Vec<usize>>,
466}
467
468/// Network metrics
469#[derive(Debug, Clone)]
470pub struct NetworkMetrics {
471    /// Clustering coefficient
472    pub clustering_coefficient: f64,
473    /// Average path length
474    pub average_path_length: f64,
475    /// Network density
476    pub density: f64,
477    /// Small-world index
478    pub small_world_index: f64,
479}
480
481/// Spectral features
482#[derive(Debug, Clone)]
483pub struct SpectralFeatures {
484    /// Spectral centroid
485    pub spectral_centroid: f64,
486    /// Spectral bandwidth
487    pub spectral_bandwidth: f64,
488    /// Spectral rolloff
489    pub spectral_rolloff: f64,
490    /// Spectral flatness
491    pub spectral_flatness: f64,
492    /// Spectral entropy
493    pub spectral_entropy: f64,
494}
495
496/// Temporal correlation analysis
497#[derive(Debug, Clone)]
498pub struct TemporalAnalysis {
499    /// Autocorrelation analysis
500    pub autocorrelation_analysis: AutocorrelationAnalysis,
501    /// Memory effects
502    pub memory_effects: MemoryEffects,
503    /// Temporal scaling
504    pub temporal_scaling: TemporalScaling,
505    /// Burst statistics
506    pub burst_statistics: BurstStatistics,
507}
508
509/// Autocorrelation analysis
510#[derive(Debug, Clone)]
511pub struct AutocorrelationAnalysis {
512    /// Autocorrelation function
513    pub autocorrelation_function: Array1<f64>,
514    /// Time lags
515    pub time_lags: Array1<f64>,
516    /// Decay time constants
517    pub decay_constants: Array1<f64>,
518    /// Oscillatory components
519    pub oscillatory_components: Vec<OscillatoryComponent>,
520}
521
522/// Oscillatory component in autocorrelation
523#[derive(Debug, Clone)]
524pub struct OscillatoryComponent {
525    /// Frequency
526    pub frequency: f64,
527    /// Amplitude
528    pub amplitude: f64,
529    /// Decay rate
530    pub decay_rate: f64,
531    /// Phase
532    pub phase: f64,
533}
534
535/// Memory effects in noise
536#[derive(Debug, Clone)]
537pub struct MemoryEffects {
538    /// Memory kernel
539    pub memory_kernel: Array1<f64>,
540    /// Memory time
541    pub memory_time: Duration,
542    /// Non-Markovianity measure
543    pub non_markovianity: f64,
544    /// Information backflow
545    pub information_backflow: Array1<f64>,
546}
547
548/// Temporal scaling properties
549#[derive(Debug, Clone)]
550pub struct TemporalScaling {
551    /// Hurst exponent
552    pub hurst_exponent: f64,
553    /// Scaling exponents
554    pub scaling_exponents: Array1<f64>,
555    /// Multifractal spectrum
556    pub multifractal_spectrum: MultifractalSpectrum,
557    /// Long-range correlations
558    pub long_range_correlations: LongRangeCorrelations,
559}
560
561/// Multifractal spectrum
562#[derive(Debug, Clone)]
563pub struct MultifractalSpectrum {
564    /// Singularity strengths
565    pub singularity_strengths: Array1<f64>,
566    /// Fractal dimensions
567    pub fractal_dimensions: Array1<f64>,
568    /// Multifractality parameter
569    pub multifractality_parameter: f64,
570}
571
572/// Long-range correlations
573#[derive(Debug, Clone)]
574pub struct LongRangeCorrelations {
575    /// Correlation exponent
576    pub correlation_exponent: f64,
577    /// Correlation length
578    pub correlation_length: f64,
579    /// Power-law range
580    pub power_law_range: (f64, f64),
581    /// Crossover scales
582    pub crossover_scales: Array1<f64>,
583}
584
585/// Burst statistics
586#[derive(Debug, Clone)]
587pub struct BurstStatistics {
588    /// Burst size distribution
589    pub burst_size_distribution: Array1<f64>,
590    /// Burst duration distribution
591    pub burst_duration_distribution: Array1<f64>,
592    /// Inter-burst intervals
593    pub inter_burst_intervals: Array1<f64>,
594    /// Burstiness parameter
595    pub burstiness_parameter: f64,
596}
597
598/// Spatial correlation analysis
599#[derive(Debug, Clone)]
600pub struct SpatialAnalysis {
601    /// Spatial correlation functions
602    pub spatial_correlations: SpatialCorrelations,
603    /// Spatial coherence
604    pub spatial_coherence: SpatialCoherence,
605    /// Spatial patterns
606    pub spatial_patterns: SpatialPatterns,
607    /// Propagation analysis
608    pub propagation_analysis: PropagationAnalysis,
609}
610
611/// Spatial correlations
612#[derive(Debug, Clone)]
613pub struct SpatialCorrelations {
614    /// Correlation matrix
615    pub correlationmatrix: Array2<f64>,
616    /// Correlation functions
617    pub correlation_functions: Array2<f64>,
618    /// Distance matrix
619    pub distance_matrix: Array2<f64>,
620    /// Correlation length scales
621    pub length_scales: Array1<f64>,
622}
623
624/// Spatial coherence
625#[derive(Debug, Clone)]
626pub struct SpatialCoherence {
627    /// Coherence matrix
628    pub coherence_matrix: Array2<f64>,
629    /// Coherence length
630    pub coherence_length: f64,
631    /// Coherence anisotropy
632    pub coherence_anisotropy: f64,
633    /// Principal coherence directions
634    pub principal_directions: Array2<f64>,
635}
636
637/// Spatial patterns
638#[derive(Debug, Clone)]
639pub struct SpatialPatterns {
640    /// Pattern types detected
641    pub pattern_types: Vec<SpatialPatternType>,
642    /// Pattern strength
643    pub pattern_strength: Array1<f64>,
644    /// Pattern wavelengths
645    pub pattern_wavelengths: Array1<f64>,
646    /// Pattern orientations
647    pub pattern_orientations: Array1<f64>,
648}
649
650/// Types of spatial patterns
651#[derive(Debug, Clone, PartialEq)]
652pub enum SpatialPatternType {
653    Uniform,
654    Gradient,
655    Periodic,
656    Random,
657    Clustered,
658    Striped,
659    Spiral,
660    Fractal,
661}
662
663/// Propagation analysis
664#[derive(Debug, Clone)]
665pub struct PropagationAnalysis {
666    /// Propagation speed
667    pub propagation_speed: f64,
668    /// Propagation direction
669    pub propagation_direction: Array1<f64>,
670    /// Dispersion relation
671    pub dispersion_relation: DispersionRelation,
672    /// Attenuation characteristics
673    pub attenuation: AttenuationCharacteristics,
674}
675
676/// Dispersion relation
677#[derive(Debug, Clone)]
678pub struct DispersionRelation {
679    /// Frequency-wavevector relationship
680    pub frequency_wavevector: Array2<f64>,
681    /// Group velocity
682    pub group_velocity: f64,
683    /// Phase velocity
684    pub phase_velocity: f64,
685    /// Dispersion parameter
686    pub dispersion_parameter: f64,
687}
688
689/// Attenuation characteristics
690#[derive(Debug, Clone)]
691pub struct AttenuationCharacteristics {
692    /// Attenuation coefficient
693    pub attenuation_coefficient: f64,
694    /// Penetration depth
695    pub penetration_depth: f64,
696    /// Attenuation anisotropy
697    pub attenuation_anisotropy: f64,
698    /// Frequency dependence
699    pub frequency_dependence: Array1<f64>,
700}
701
702/// Non-Markovian analysis
703#[derive(Debug, Clone)]
704pub struct NonMarkovianAnalysis {
705    /// Non-Markovianity measures
706    pub non_markovianity_measures: NonMarkovianityMeasures,
707    /// Memory effects characterization
708    pub memory_characterization: MemoryCharacterization,
709    /// Information flow analysis
710    pub information_flow: InformationFlowAnalysis,
711    /// Non-Markovian models
712    pub non_markovian_models: Vec<NonMarkovianModel>,
713}
714
715/// Non-Markovianity measures
716#[derive(Debug, Clone)]
717pub struct NonMarkovianityMeasures {
718    /// BLP measure
719    pub blp_measure: f64,
720    /// RHP measure
721    pub rhp_measure: f64,
722    /// LLI measure
723    pub lli_measure: f64,
724    /// Trace distance measure
725    pub trace_distance_measure: f64,
726    /// Volume measure
727    pub volume_measure: f64,
728}
729
730/// Memory characterization
731#[derive(Debug, Clone)]
732pub struct MemoryCharacterization {
733    /// Memory kernel reconstruction
734    pub memory_kernel: Array1<f64>,
735    /// Memory depth
736    pub memory_depth: Duration,
737    /// Memory strength
738    pub memory_strength: f64,
739    /// Memory type classification
740    pub memory_type: MemoryType,
741}
742
743/// Types of memory
744#[derive(Debug, Clone, PartialEq)]
745pub enum MemoryType {
746    ShortTerm,
747    LongTerm,
748    Infinite,
749    Oscillatory,
750    Algebraic,
751    Exponential,
752}
753
754/// Information flow analysis
755#[derive(Debug, Clone)]
756pub struct InformationFlowAnalysis {
757    /// Information backflow
758    pub information_backflow: Array1<f64>,
759    /// Transfer entropy
760    pub transfer_entropy: f64,
761    /// Mutual information
762    pub mutual_information: f64,
763    /// Causal relationships
764    pub causal_relationships: CausalRelationships,
765}
766
767/// Causal relationships
768#[derive(Debug, Clone)]
769pub struct CausalRelationships {
770    /// Causal network
771    pub causal_network: Array2<f64>,
772    /// Causal strength
773    pub causal_strength: Array2<f64>,
774    /// Causal delays
775    pub causal_delays: Array2<f64>,
776    /// Feedback loops
777    pub feedback_loops: Vec<FeedbackLoop>,
778}
779
780/// Feedback loop
781#[derive(Debug, Clone)]
782pub struct FeedbackLoop {
783    /// Loop nodes
784    pub nodes: Vec<usize>,
785    /// Loop strength
786    pub strength: f64,
787    /// Loop delay
788    pub delay: Duration,
789    /// Loop stability
790    pub stability: f64,
791}
792
793/// Non-Markovian model
794#[derive(Debug, Clone)]
795pub struct NonMarkovianModel {
796    /// Model type
797    pub model_type: NonMarkovianModelType,
798    /// Model parameters
799    pub parameters: Array1<f64>,
800    /// Model accuracy
801    pub accuracy: f64,
802    /// Predictive power
803    pub predictive_power: f64,
804}
805
806/// Types of non-Markovian models
807#[derive(Debug, Clone, PartialEq)]
808pub enum NonMarkovianModelType {
809    GeneralizedLangevin,
810    FractionalBrownian,
811    MemoryKernel,
812    StochasticDelay,
813    HierarchicalEquations,
814}
815
816/// Noise suppression effectiveness
817#[derive(Debug, Clone)]
818pub struct SuppressionEffectiveness {
819    /// Suppression by noise type
820    pub suppression_by_type: HashMap<NoiseType, f64>,
821    /// Overall suppression factor
822    pub overall_suppression: f64,
823    /// Frequency-dependent suppression
824    pub frequency_suppression: Array1<f64>,
825    /// Temporal suppression profile
826    pub temporal_suppression: Array1<f64>,
827    /// Suppression mechanisms
828    pub suppression_mechanisms: Vec<SuppressionMechanism>,
829}
830
831/// Suppression mechanism
832#[derive(Debug, Clone)]
833pub struct SuppressionMechanism {
834    /// Mechanism type
835    pub mechanism_type: SuppressionMechanismType,
836    /// Effectiveness
837    pub effectiveness: f64,
838    /// Target noise types
839    pub target_noise_types: Vec<NoiseType>,
840    /// Operating frequency range
841    pub frequency_range: (f64, f64),
842}
843
844/// Types of suppression mechanisms
845#[derive(Debug, Clone, PartialEq)]
846pub enum SuppressionMechanismType {
847    Averaging,
848    Decoupling,
849    Refocusing,
850    Cancellation,
851    Filtering,
852    Coherence,
853}
854
855/// DD noise analyzer
856pub struct DDNoiseAnalyzer {
857    pub config: DDNoiseConfig,
858}
859
860impl DDNoiseAnalyzer {
861    /// Create new noise analyzer
862    pub fn new(config: DDNoiseConfig) -> Self {
863        Self { config }
864    }
865
866    /// Analyze noise characteristics
867    pub fn analyze_noise_characteristics(
868        &self,
869        sequence: &DDSequence,
870        performance_analysis: &DDPerformanceAnalysis,
871    ) -> DeviceResult<DDNoiseAnalysis> {
872        println!("Starting DD noise analysis");
873
874        let noise_characterization = self.characterize_noise(sequence, performance_analysis)?;
875
876        let spectral_analysis = if self.config.spectral_analysis {
877            Some(self.perform_spectral_analysis(sequence)?)
878        } else {
879            None
880        };
881
882        let temporal_analysis = if self.config.temporal_correlation {
883            Some(self.perform_temporal_analysis(sequence)?)
884        } else {
885            None
886        };
887
888        let spatial_analysis = if self.config.spatial_correlation {
889            Some(self.perform_spatial_analysis(sequence)?)
890        } else {
891            None
892        };
893
894        let non_markovian_analysis = if self.config.non_markovian_modeling {
895            Some(self.perform_non_markovian_analysis(sequence)?)
896        } else {
897            None
898        };
899
900        let suppression_effectiveness = self.analyze_suppression_effectiveness(sequence)?;
901
902        Ok(DDNoiseAnalysis {
903            noise_characterization,
904            spectral_analysis,
905            temporal_analysis,
906            spatial_analysis,
907            non_markovian_analysis,
908            suppression_effectiveness,
909        })
910    }
911
912    /// Characterize noise
913    fn characterize_noise(
914        &self,
915        sequence: &DDSequence,
916        _performance_analysis: &DDPerformanceAnalysis,
917    ) -> DeviceResult<NoiseCharacterization> {
918        // Simplified noise characterization
919        let mut noise_types = HashMap::new();
920
921        for noise_type in &self.config.noise_types {
922            let characteristics = NoiseCharacteristics {
923                strength: 0.1, // Simplified
924                frequency_profile: FrequencyProfile {
925                    power_spectral_density: Array1::zeros(100),
926                    frequency_bins: Array1::zeros(100),
927                    dominant_frequencies: vec![1e6, 10e6],
928                    spectral_shape: SpectralShape::Pink,
929                },
930                temporal_profile: TemporalProfile {
931                    autocorrelation: Array1::zeros(100),
932                    correlation_time: Duration::from_micros(1),
933                    non_gaussianity: NonGaussianityMeasures {
934                        skewness: 0.1,
935                        kurtosis: 3.2,
936                        higher_cumulants: Array1::zeros(5),
937                        non_gaussianity_index: 0.05,
938                    },
939                    intermittency: IntermittencyCharacteristics {
940                        burst_durations: Array1::zeros(50),
941                        quiet_periods: Array1::zeros(50),
942                        intermittency_factor: 0.3,
943                        burst_intensity: Array1::zeros(50),
944                    },
945                },
946                spatial_profile: None,
947                statistical_properties: NoiseStatistics {
948                    mean: 0.0,
949                    variance: 0.01,
950                    distribution_type: NoiseDistribution::Gaussian,
951                    distribution_parameters: Array1::from_vec(vec![0.0, 0.1]),
952                    entropy_measures: EntropyMeasures {
953                        shannon_entropy: 2.5,
954                        renyi_entropy: Array1::from_vec(vec![2.3, 2.5, 2.7]),
955                        tsallis_entropy: 2.4,
956                        differential_entropy: 1.8,
957                    },
958                },
959            };
960            noise_types.insert(noise_type.clone(), characteristics);
961        }
962
963        Ok(NoiseCharacterization {
964            noise_types,
965            dominant_sources: vec![NoiseSource {
966                source_type: NoiseSourceType::ThermalFluctuations,
967                strength: 0.05,
968                location: None,
969                frequency_range: (1e3, 1e9),
970                mitigation_strategies: vec!["Temperature control".to_string()],
971            }],
972            environment_classification: NoiseEnvironmentClassification {
973                environment_type: NoiseEnvironmentType::ComplexMarkovian,
974                complexity_score: 0.6,
975                predictability_score: 0.7,
976                stationarity: StationarityAssessment {
977                    is_stationary: true,
978                    confidence: 0.85,
979                    non_stationary_components: Vec::new(),
980                    trend_analysis: TrendAnalysis {
981                        linear_slope: 0.001,
982                        trend_significance: 0.1,
983                        cyclical_components: Vec::new(),
984                        change_points: Vec::new(),
985                    },
986                },
987            },
988            time_dependent_parameters: TimeDependentNoiseParameters {
989                parameter_evolution: HashMap::new(),
990                time_grid: Array1::zeros(100),
991                parameter_correlations: Array2::eye(3),
992                prediction_model: None,
993            },
994        })
995    }
996
997    /// Perform spectral analysis (simplified)
998    fn perform_spectral_analysis(&self, _sequence: &DDSequence) -> DeviceResult<SpectralAnalysis> {
999        Ok(SpectralAnalysis {
1000            psd_analysis: PSDAnalysis {
1001                psd_estimate: Array1::zeros(100),
1002                frequency_bins: Array1::zeros(100),
1003                confidence_intervals: Array2::zeros((100, 2)),
1004                peaks: Vec::new(),
1005            },
1006            cross_spectral_analysis: CrossSpectralAnalysis {
1007                cross_psd: Array2::zeros((100, 100)),
1008                phase_relationships: Array2::zeros((100, 100)),
1009                frequency_coupling: Array2::zeros((100, 100)),
1010                coherence_function: Array1::zeros(100),
1011            },
1012            coherence_analysis: CoherenceAnalysis {
1013                coherence_matrix: Array2::eye(10),
1014                significant_coherences: Vec::new(),
1015                coherence_network: CoherenceNetwork {
1016                    adjacency_matrix: Array2::eye(10),
1017                    network_metrics: NetworkMetrics {
1018                        clustering_coefficient: 0.3,
1019                        average_path_length: 2.5,
1020                        density: 0.2,
1021                        small_world_index: 1.2,
1022                    },
1023                    communities: Vec::new(),
1024                },
1025            },
1026            spectral_features: SpectralFeatures {
1027                spectral_centroid: 1e6,
1028                spectral_bandwidth: 0.5e6,
1029                spectral_rolloff: 0.85,
1030                spectral_flatness: 0.3,
1031                spectral_entropy: 3.2,
1032            },
1033        })
1034    }
1035
1036    /// Perform temporal analysis (simplified)
1037    fn perform_temporal_analysis(&self, _sequence: &DDSequence) -> DeviceResult<TemporalAnalysis> {
1038        Ok(TemporalAnalysis {
1039            autocorrelation_analysis: AutocorrelationAnalysis {
1040                autocorrelation_function: Array1::zeros(100),
1041                time_lags: Array1::zeros(100),
1042                decay_constants: Array1::from_vec(vec![1e-6, 10e-6]),
1043                oscillatory_components: Vec::new(),
1044            },
1045            memory_effects: MemoryEffects {
1046                memory_kernel: Array1::zeros(100),
1047                memory_time: Duration::from_micros(1),
1048                non_markovianity: 0.2,
1049                information_backflow: Array1::zeros(100),
1050            },
1051            temporal_scaling: TemporalScaling {
1052                hurst_exponent: 0.55,
1053                scaling_exponents: Array1::from_vec(vec![0.5, 0.6, 0.7]),
1054                multifractal_spectrum: MultifractalSpectrum {
1055                    singularity_strengths: Array1::zeros(20),
1056                    fractal_dimensions: Array1::zeros(20),
1057                    multifractality_parameter: 0.1,
1058                },
1059                long_range_correlations: LongRangeCorrelations {
1060                    correlation_exponent: 0.8,
1061                    correlation_length: 1e-5,
1062                    power_law_range: (1e-9, 1e-6),
1063                    crossover_scales: Array1::from_vec(vec![1e-8, 1e-7]),
1064                },
1065            },
1066            burst_statistics: BurstStatistics {
1067                burst_size_distribution: Array1::zeros(50),
1068                burst_duration_distribution: Array1::zeros(50),
1069                inter_burst_intervals: Array1::zeros(50),
1070                burstiness_parameter: 0.4,
1071            },
1072        })
1073    }
1074
1075    /// Perform spatial analysis (simplified)
1076    fn perform_spatial_analysis(&self, _sequence: &DDSequence) -> DeviceResult<SpatialAnalysis> {
1077        let n_qubits = 10; // Simplified
1078
1079        Ok(SpatialAnalysis {
1080            spatial_correlations: SpatialCorrelations {
1081                correlationmatrix: Array2::eye(n_qubits),
1082                correlation_functions: Array2::zeros((n_qubits, 100)),
1083                distance_matrix: Array2::zeros((n_qubits, n_qubits)),
1084                length_scales: Array1::from_vec(vec![1e-3, 5e-3, 1e-2]),
1085            },
1086            spatial_coherence: SpatialCoherence {
1087                coherence_matrix: Array2::eye(n_qubits),
1088                coherence_length: 2e-3,
1089                coherence_anisotropy: 0.1,
1090                principal_directions: Array2::eye(3),
1091            },
1092            spatial_patterns: SpatialPatterns {
1093                pattern_types: vec![SpatialPatternType::Random, SpatialPatternType::Clustered],
1094                pattern_strength: Array1::from_vec(vec![0.3, 0.5]),
1095                pattern_wavelengths: Array1::from_vec(vec![1e-3, 2e-3]),
1096                pattern_orientations: Array1::from_vec(vec![0.0, 1.57]),
1097            },
1098            propagation_analysis: PropagationAnalysis {
1099                propagation_speed: 1e8,
1100                propagation_direction: Array1::from_vec(vec![1.0, 0.0, 0.0]),
1101                dispersion_relation: DispersionRelation {
1102                    frequency_wavevector: Array2::zeros((100, 2)),
1103                    group_velocity: 0.8e8,
1104                    phase_velocity: 1e8,
1105                    dispersion_parameter: 0.1,
1106                },
1107                attenuation: AttenuationCharacteristics {
1108                    attenuation_coefficient: 0.01,
1109                    penetration_depth: 1e-2,
1110                    attenuation_anisotropy: 0.05,
1111                    frequency_dependence: Array1::zeros(100),
1112                },
1113            },
1114        })
1115    }
1116
1117    /// Perform non-Markovian analysis (simplified)
1118    fn perform_non_markovian_analysis(
1119        &self,
1120        _sequence: &DDSequence,
1121    ) -> DeviceResult<NonMarkovianAnalysis> {
1122        Ok(NonMarkovianAnalysis {
1123            non_markovianity_measures: NonMarkovianityMeasures {
1124                blp_measure: 0.15,
1125                rhp_measure: 0.12,
1126                lli_measure: 0.18,
1127                trace_distance_measure: 0.14,
1128                volume_measure: 0.16,
1129            },
1130            memory_characterization: MemoryCharacterization {
1131                memory_kernel: Array1::zeros(100),
1132                memory_depth: Duration::from_micros(5),
1133                memory_strength: 0.3,
1134                memory_type: MemoryType::Exponential,
1135            },
1136            information_flow: InformationFlowAnalysis {
1137                information_backflow: Array1::zeros(100),
1138                transfer_entropy: 0.25,
1139                mutual_information: 0.8,
1140                causal_relationships: CausalRelationships {
1141                    causal_network: Array2::zeros((10, 10)),
1142                    causal_strength: Array2::zeros((10, 10)),
1143                    causal_delays: Array2::zeros((10, 10)),
1144                    feedback_loops: Vec::new(),
1145                },
1146            },
1147            non_markovian_models: vec![NonMarkovianModel {
1148                model_type: NonMarkovianModelType::GeneralizedLangevin,
1149                parameters: Array1::from_vec(vec![0.1, 0.5, 1.0]),
1150                accuracy: 0.85,
1151                predictive_power: 0.75,
1152            }],
1153        })
1154    }
1155
1156    /// Analyze suppression effectiveness
1157    fn analyze_suppression_effectiveness(
1158        &self,
1159        sequence: &DDSequence,
1160    ) -> DeviceResult<SuppressionEffectiveness> {
1161        let suppression_by_type = sequence.properties.noise_suppression.clone();
1162
1163        let overall_suppression =
1164            suppression_by_type.values().sum::<f64>() / suppression_by_type.len() as f64;
1165
1166        Ok(SuppressionEffectiveness {
1167            suppression_by_type,
1168            overall_suppression,
1169            frequency_suppression: Array1::zeros(100),
1170            temporal_suppression: Array1::zeros(100),
1171            suppression_mechanisms: vec![
1172                SuppressionMechanism {
1173                    mechanism_type: SuppressionMechanismType::Decoupling,
1174                    effectiveness: 0.8,
1175                    target_noise_types: vec![NoiseType::PhaseDamping],
1176                    frequency_range: (1e3, 1e9),
1177                },
1178                SuppressionMechanism {
1179                    mechanism_type: SuppressionMechanismType::Refocusing,
1180                    effectiveness: 0.6,
1181                    target_noise_types: vec![NoiseType::AmplitudeDamping],
1182                    frequency_range: (1e6, 1e8),
1183                },
1184            ],
1185        })
1186    }
1187}
quantrs2_device/dynamical_decoupling/noise.rs

quantrs2_device/dynamical_decoupling/
noise.rs
