quantrs2_tytan/
quantum_error_correction.rs

1//! Advanced Quantum Error Correction for Optimization
2//!
3//! This module implements quantum error correction specifically tailored for
4//! quantum annealing and optimization problems, featuring adaptive protocols,
5//! topological codes, and error mitigation techniques.
6
7#![allow(dead_code)]
8
9use scirs2_core::ndarray::{Array1, Array2, Array3};
10use scirs2_core::random::prelude::*;
11use scirs2_core::random::prelude::*;
12use std::collections::HashMap;
13
14/// Quantum error correction system for optimization
15pub struct QuantumErrorCorrection {
16    /// Number of logical qubits
17    pub num_logical_qubits: usize,
18    /// Number of physical qubits
19    pub num_physical_qubits: usize,
20    /// Error correction configuration
21    pub config: QECConfig,
22    /// Active error correction codes
23    pub codes: Vec<Box<dyn QuantumCode>>,
24    /// Error syndrome detection
25    pub syndrome_detector: SyndromeDetector,
26    /// Error mitigation strategies
27    pub mitigation_strategies: Vec<Box<dyn ErrorMitigationStrategy>>,
28    /// Fault tolerance analyzer
29    pub fault_tolerance: FaultToleranceAnalyzer,
30    /// Performance metrics
31    pub metrics: QECMetrics,
32}
33
34/// Configuration for quantum error correction
35#[derive(Debug, Clone)]
36pub struct QECConfig {
37    /// Type of quantum code to use
38    pub code_type: QuantumCodeType,
39    /// Code distance
40    pub code_distance: usize,
41    /// Error correction cycle frequency
42    pub correction_frequency: f64,
43    /// Syndrome extraction method
44    pub syndrome_method: SyndromeExtractionMethod,
45    /// Decoding algorithm
46    pub decoding_algorithm: DecodingAlgorithm,
47    /// Error mitigation settings
48    pub error_mitigation: ErrorMitigationConfig,
49    /// Adaptive correction settings
50    pub adaptive_correction: AdaptiveCorrectionConfig,
51    /// Threshold estimation
52    pub threshold_estimation: ThresholdEstimationConfig,
53}
54
55/// Types of quantum error correction codes
56#[derive(Debug, Clone, PartialEq)]
57pub enum QuantumCodeType {
58    /// Surface codes
59    SurfaceCode { lattice_type: LatticeType },
60    /// Color codes
61    ColorCode { color_scheme: ColorScheme },
62    /// Stabilizer codes
63    StabilizerCode { generators: Vec<String> },
64    /// Topological codes
65    TopologicalCode { code_family: TopologicalFamily },
66    /// CSS codes
67    CSSCode { classical_codes: (String, String) },
68    /// Quantum LDPC codes
69    QuantumLDPC { parity_check_matrix: Array2<u8> },
70    /// Concatenated codes
71    ConcatenatedCode {
72        inner_code: Box<Self>,
73        outer_code: Box<Self>,
74    },
75    /// Subsystem codes
76    SubsystemCode { gauge_group: Vec<String> },
77}
78
79/// Lattice types for surface codes
80#[derive(Debug, Clone, PartialEq, Eq)]
81pub enum LatticeType {
82    /// Square lattice
83    Square,
84    /// Triangular lattice
85    Triangular,
86    /// Hexagonal lattice
87    Hexagonal,
88    /// Kagome lattice
89    Kagome,
90}
91
92/// Color schemes for color codes
93#[derive(Debug, Clone, PartialEq, Eq)]
94pub enum ColorScheme {
95    /// Three-colorable graph
96    ThreeColor,
97    /// Four-colorable graph
98    FourColor,
99    /// Hexagonal color code
100    HexagonalColor,
101}
102
103/// Topological code families
104#[derive(Debug, Clone, PartialEq, Eq)]
105pub enum TopologicalFamily {
106    /// Toric code
107    ToricCode,
108    /// Planar code
109    PlanarCode,
110    /// Hyperbolic code
111    HyperbolicCode,
112    /// Fractal code
113    FractalCode,
114}
115
116/// Syndrome extraction methods
117#[derive(Debug, Clone, PartialEq, Eq)]
118pub enum SyndromeExtractionMethod {
119    /// Standard syndrome extraction
120    Standard,
121    /// Flag-based syndrome extraction
122    FlagBased,
123    /// Repeated syndrome extraction
124    Repeated { num_repetitions: usize },
125    /// Adaptive syndrome extraction
126    Adaptive,
127    /// Concurrent syndrome extraction
128    Concurrent,
129}
130
131/// Decoding algorithms
132#[derive(Debug, Clone, PartialEq, Eq)]
133pub enum DecodingAlgorithm {
134    /// Minimum weight perfect matching
135    MWPM,
136    /// Belief propagation
137    BeliefPropagation,
138    /// Neural network decoder
139    NeuralNetwork { architecture: String },
140    /// Union-find decoder
141    UnionFind,
142    /// Trellis decoder
143    Trellis,
144    /// Machine learning decoder
145    MachineLearning { model_type: MLModelType },
146    /// Tensor network decoder
147    TensorNetwork,
148    /// Reinforcement learning decoder
149    ReinforcementLearning,
150}
151
152/// Machine learning model types for decoding
153#[derive(Debug, Clone, PartialEq, Eq)]
154pub enum MLModelType {
155    /// Recurrent neural network
156    RNN,
157    /// Convolutional neural network
158    CNN,
159    /// Transformer
160    Transformer,
161    /// Graph neural network
162    GNN,
163    /// Variational autoencoder
164    VAE,
165    /// Additional ML model types from test module
166    ConvolutionalNN,
167    RecurrentNN,
168    TransformerNetwork,
169    GraphNeuralNetwork,
170    ReinforcementLearning,
171    DeepQNetwork,
172}
173
174/// Error mitigation methods
175#[derive(Debug, Clone, PartialEq, Eq)]
176pub enum ErrorMitigationMethod {
177    ZeroNoiseExtrapolation,
178    ReadoutErrorCorrection,
179    VirtualDistillation,
180    SymmetryVerification,
181    PostSelection,
182    TwirlingProtocols,
183}
184
185/// Threshold update methods
186#[derive(Debug, Clone)]
187pub enum ThresholdUpdateMethod {
188    ExponentialMovingAverage { alpha: f64 },
189    GradientDescent { momentum: f64 },
190    AdaptiveLearningRate,
191    BayesianOptimization,
192    EvolutionaryStrategy,
193}
194
195/// Error models
196#[derive(Debug, Clone)]
197pub enum ErrorModel {
198    Depolarizing { probability: f64 },
199    Pauli { px: f64, py: f64, pz: f64 },
200    AmplitudeDamping { gamma: f64 },
201    PhaseDamping { gamma: f64 },
202}
203
204/// Correction operations
205#[derive(Debug, Clone)]
206pub enum CorrectionOperation {
207    PauliX {
208        qubit: usize,
209    },
210    PauliY {
211        qubit: usize,
212    },
213    PauliZ {
214        qubit: usize,
215    },
216    TwoQubitCorrection {
217        operation: String,
218        qubits: (usize, usize),
219    },
220    MultiQubitCorrection {
221        operation: String,
222        qubits: Vec<usize>,
223    },
224    LogicalCorrection {
225        logical_operation: String,
226    },
227}
228
229/// Error mitigation configuration
230#[derive(Debug, Clone)]
231pub struct ErrorMitigationConfig {
232    /// Enable zero noise extrapolation
233    pub zero_noise_extrapolation: bool,
234    /// Enable probabilistic error cancellation
235    pub probabilistic_error_cancellation: bool,
236    /// Enable symmetry verification
237    pub symmetry_verification: bool,
238    /// Enable virtual distillation
239    pub virtual_distillation: bool,
240    /// Error amplification parameters
241    pub error_amplification: ErrorAmplificationConfig,
242    /// Clifford data regression
243    pub clifford_data_regression: bool,
244}
245
246/// Error amplification configuration
247#[derive(Debug, Clone)]
248pub struct ErrorAmplificationConfig {
249    /// Amplification factors
250    pub amplification_factors: Vec<f64>,
251    /// Maximum amplification level
252    pub max_amplification: f64,
253    /// Amplification strategy
254    pub strategy: AmplificationStrategy,
255}
256
257/// Amplification strategies
258#[derive(Debug, Clone, PartialEq, Eq)]
259pub enum AmplificationStrategy {
260    /// Linear amplification
261    Linear,
262    /// Exponential amplification
263    Exponential,
264    /// Adaptive amplification
265    Adaptive,
266    /// Randomized amplification
267    Randomized,
268}
269
270/// Adaptive correction configuration
271#[derive(Debug, Clone)]
272pub struct AdaptiveCorrectionConfig {
273    /// Enable adaptive thresholding
274    pub adaptive_thresholding: bool,
275    /// Dynamic distance adjustment
276    pub dynamic_distance: bool,
277    /// Real-time code switching
278    pub real_time_code_switching: bool,
279    /// Performance-based adaptation
280    pub performance_adaptation: PerformanceAdaptationConfig,
281    /// Learning-based adaptation
282    pub learning_adaptation: LearningAdaptationConfig,
283}
284
285/// Performance-based adaptation configuration
286#[derive(Debug, Clone)]
287pub struct PerformanceAdaptationConfig {
288    /// Error rate threshold for adaptation
289    pub error_rate_threshold: f64,
290    /// Performance monitoring window
291    pub monitoring_window: usize,
292    /// Adaptation sensitivity
293    pub adaptation_sensitivity: f64,
294    /// Minimum adaptation interval
295    pub min_adaptation_interval: f64,
296}
297
298/// Learning-based adaptation configuration
299#[derive(Debug, Clone)]
300pub struct LearningAdaptationConfig {
301    /// Enable reinforcement learning
302    pub reinforcement_learning: bool,
303    /// Learning rate
304    pub learning_rate: f64,
305    /// Experience replay buffer size
306    pub replay_buffer_size: usize,
307    /// Model update frequency
308    pub update_frequency: usize,
309}
310
311/// Threshold estimation configuration
312#[derive(Debug, Clone)]
313pub struct ThresholdEstimationConfig {
314    /// Enable real-time threshold estimation
315    pub real_time_estimation: bool,
316    /// Estimation method
317    pub estimation_method: ThresholdEstimationMethod,
318    /// Confidence level
319    pub confidence_level: f64,
320    /// Update frequency
321    pub update_frequency: usize,
322}
323
324/// Threshold estimation methods
325#[derive(Debug, Clone, PartialEq, Eq)]
326pub enum ThresholdEstimationMethod {
327    /// Monte Carlo estimation
328    MonteCarlo,
329    /// Maximum likelihood estimation
330    MaximumLikelihood,
331    /// Bayesian estimation
332    Bayesian,
333    /// Bootstrap estimation
334    Bootstrap,
335    /// Cross-validation
336    CrossValidation,
337}
338
339/// Quantum code trait
340pub trait QuantumCode: Send + Sync {
341    /// Encode logical qubits into physical qubits
342    fn encode(&self, logical_state: &Array1<f64>) -> Result<Array1<f64>, QECError>;
343
344    /// Decode physical qubits to logical qubits
345    fn decode(
346        &self,
347        physical_state: &Array1<f64>,
348        syndrome: &Array1<u8>,
349    ) -> Result<Array1<f64>, QECError>;
350
351    /// Extract error syndrome
352    fn extract_syndrome(&self, physical_state: &Array1<f64>) -> Result<Array1<u8>, QECError>;
353
354    /// Get stabilizer generators
355    fn get_stabilizers(&self) -> Vec<Array1<i8>>;
356
357    /// Get code parameters
358    fn get_parameters(&self) -> CodeParameters;
359
360    /// Check if error is correctable
361    fn is_correctable(&self, error: &Array1<u8>) -> bool;
362
363    /// Get logical operators
364    fn get_logical_operators(&self) -> LogicalOperators;
365}
366
367/// Code parameters
368#[derive(Debug, Clone)]
369pub struct CodeParameters {
370    /// Number of logical qubits
371    pub k: usize,
372    /// Number of physical qubits
373    pub n: usize,
374    /// Code distance
375    pub d: usize,
376    /// Rate of the code
377    pub rate: f64,
378    /// Threshold error rate
379    pub threshold: f64,
380}
381
382/// Logical operators
383#[derive(Debug, Clone)]
384pub struct LogicalOperators {
385    /// Logical X operators
386    pub logical_x: Vec<Array1<i8>>,
387    /// Logical Z operators
388    pub logical_z: Vec<Array1<i8>>,
389    /// Logical Y operators
390    pub logical_y: Vec<Array1<i8>>,
391}
392
393/// Syndrome detector
394#[derive(Debug)]
395pub struct SyndromeDetector {
396    /// Detection configuration
397    pub config: SyndromeDetectionConfig,
398    /// Measurement circuits
399    pub measurement_circuits: Vec<MeasurementCircuit>,
400    /// Error correlation tracking
401    pub error_correlations: ErrorCorrelationTracker,
402    /// Syndrome history
403    pub syndrome_history: SyndromeHistory,
404}
405
406/// Syndrome detection configuration
407#[derive(Debug, Clone)]
408pub struct SyndromeDetectionConfig {
409    /// Number of syndrome extraction rounds
410    pub num_rounds: usize,
411    /// Syndrome extraction frequency
412    pub extraction_frequency: f64,
413    /// Error detection threshold
414    pub detection_threshold: f64,
415    /// Correlations tracking window
416    pub correlation_window: usize,
417    /// Flag qubit usage
418    pub use_flag_qubits: bool,
419}
420
421/// Measurement circuit for syndrome extraction
422#[derive(Debug, Clone)]
423pub struct MeasurementCircuit {
424    /// Circuit identifier
425    pub id: String,
426    /// Stabilizer being measured
427    pub stabilizer: Array1<i8>,
428    /// Qubits involved
429    pub qubits: Vec<usize>,
430    /// Ancilla qubits
431    pub ancillas: Vec<usize>,
432    /// Circuit depth
433    pub depth: usize,
434    /// Gate sequence
435    pub gates: Vec<QuantumGate>,
436}
437
438/// Quantum gates for circuits
439#[derive(Debug, Clone)]
440pub enum QuantumGate {
441    /// Hadamard gate
442    H { qubit: usize },
443    /// CNOT gate
444    CNOT { control: usize, target: usize },
445    /// Pauli-X gate
446    X { qubit: usize },
447    /// Pauli-Y gate
448    Y { qubit: usize },
449    /// Pauli-Z gate
450    Z { qubit: usize },
451    /// S gate
452    S { qubit: usize },
453    /// T gate
454    T { qubit: usize },
455    /// Measurement
456    Measure {
457        qubit: usize,
458        basis: MeasurementBasis,
459    },
460}
461
462/// Measurement basis
463#[derive(Debug, Clone, PartialEq, Eq)]
464pub enum MeasurementBasis {
465    Z,
466    X,
467    Y,
468}
469
470/// Error correlation tracker
471#[derive(Debug, Clone)]
472pub struct ErrorCorrelationTracker {
473    /// Spatial correlations
474    pub spatial_correlations: Array2<f64>,
475    /// Temporal correlations
476    pub temporal_correlations: Array1<f64>,
477    /// Cross-correlations between different error types
478    pub cross_correlations: Array3<f64>,
479    /// Correlation decay times
480    pub correlation_times: Array1<f64>,
481}
482
483/// Syndrome history
484#[derive(Debug, Clone)]
485pub struct SyndromeHistory {
486    /// Historical syndromes
487    pub history: Vec<SyndromeRecord>,
488    /// Maximum history length
489    pub max_length: usize,
490    /// Pattern detection
491    pub pattern_detector: PatternDetector,
492}
493
494/// Syndrome record
495#[derive(Debug, Clone)]
496pub struct SyndromeRecord {
497    /// Syndrome measurement
498    pub syndrome: Array1<u8>,
499    /// Timestamp
500    pub timestamp: f64,
501    /// Associated error (if known)
502    pub error: Option<Array1<u8>>,
503    /// Correction applied
504    pub correction: Option<Array1<u8>>,
505    /// Success indicator
506    pub success: bool,
507}
508
509/// Pattern detector for syndrome sequences
510#[derive(Debug)]
511pub struct PatternDetector {
512    /// Detected patterns
513    pub patterns: Vec<SyndromePattern>,
514    /// Pattern frequency
515    pub pattern_frequency: HashMap<String, usize>,
516    /// Pattern prediction model
517    pub prediction_model: Option<Box<dyn PredictionModel>>,
518}
519
520impl Clone for PatternDetector {
521    fn clone(&self) -> Self {
522        Self {
523            patterns: self.patterns.clone(),
524            pattern_frequency: self.pattern_frequency.clone(),
525            prediction_model: None, // Cannot clone trait object
526        }
527    }
528}
529
530/// Syndrome pattern
531#[derive(Debug, Clone)]
532pub struct SyndromePattern {
533    /// Pattern sequence
534    pub sequence: Vec<Array1<u8>>,
535    /// Pattern probability
536    pub probability: f64,
537    /// Associated error type
538    pub error_type: ErrorType,
539    /// Optimal correction
540    pub optimal_correction: Array1<u8>,
541}
542
543/// Error types
544#[derive(Debug, Clone, PartialEq, Eq)]
545pub enum ErrorType {
546    /// Single qubit error
547    SingleQubit {
548        qubit: usize,
549        pauli: char,
550    },
551    /// Two qubit error
552    TwoQubit {
553        qubits: (usize, usize),
554        pauli: (char, char),
555    },
556    /// Correlated error
557    Correlated {
558        qubits: Vec<usize>,
559        pattern: String,
560    },
561    /// Measurement error
562    Measurement {
563        qubit: usize,
564    },
565    /// Gate error
566    Gate {
567        gate_type: String,
568        qubits: Vec<usize>,
569    },
570    /// Coherent error
571    Coherent {
572        description: String,
573    },
574    /// Additional error types from test module
575    BitFlip,
576    PhaseFlip,
577    BitPhaseFlip,
578    Depolarizing,
579    AmplitudeDamping,
580    PhaseDamping,
581}
582
583/// Prediction model trait
584pub trait PredictionModel: Send + Sync + std::fmt::Debug {
585    /// Predict next syndrome
586    fn predict_syndrome(&self, history: &[Array1<u8>]) -> Result<Array1<u8>, QECError>;
587
588    /// Update model with new data
589    fn update(&mut self, history: &[Array1<u8>], actual: &Array1<u8>) -> Result<(), QECError>;
590
591    /// Get prediction confidence
592    fn get_confidence(&self) -> f64;
593}
594
595/// Error mitigation strategy trait
596pub trait ErrorMitigationStrategy: Send + Sync {
597    /// Apply error mitigation
598    fn mitigate_errors(
599        &self,
600        measurement_data: &MeasurementData,
601    ) -> Result<MitigatedData, QECError>;
602
603    /// Get strategy name
604    fn get_strategy_name(&self) -> &str;
605
606    /// Get mitigation parameters
607    fn get_parameters(&self) -> HashMap<String, f64>;
608
609    /// Estimate mitigation overhead
610    fn estimate_overhead(&self) -> f64;
611}
612
613/// Measurement data
614#[derive(Debug, Clone)]
615pub struct MeasurementData {
616    /// Raw measurement outcomes
617    pub outcomes: Vec<Array1<u8>>,
618    /// Measurement settings
619    pub settings: Vec<MeasurementSetting>,
620    /// Noise characterization
621    pub noise_data: NoiseCharacterization,
622    /// Metadata
623    pub metadata: MeasurementMetadata,
624}
625
626/// Measurement setting
627#[derive(Debug, Clone)]
628pub struct MeasurementSetting {
629    /// Observable being measured
630    pub observable: Observable,
631    /// Number of shots
632    pub num_shots: usize,
633    /// Basis rotation angles
634    pub basis_angles: Vec<f64>,
635    /// Error rates
636    pub error_rates: ErrorRates,
637}
638
639/// Observable representation
640#[derive(Debug, Clone)]
641pub struct Observable {
642    /// Pauli string representation
643    pub pauli_string: String,
644    /// Coefficient
645    pub coefficient: f64,
646    /// Qubits involved
647    pub qubits: Vec<usize>,
648}
649
650/// Error rates
651#[derive(Debug, Clone)]
652pub struct ErrorRates {
653    /// Single qubit error rates
654    pub single_qubit: Array1<f64>,
655    /// Two qubit error rates
656    pub two_qubit: Array2<f64>,
657    /// Readout error rates
658    pub readout: Array1<f64>,
659    /// Preparation error rates
660    pub preparation: Array1<f64>,
661}
662
663/// Noise characterization
664#[derive(Debug, Clone)]
665pub struct NoiseCharacterization {
666    /// Noise model type
667    pub noise_type: NoiseModelType,
668    /// Noise parameters
669    pub parameters: HashMap<String, f64>,
670    /// Temporal correlations
671    pub temporal_correlations: Array1<f64>,
672    /// Spatial correlations
673    pub spatial_correlations: Array2<f64>,
674}
675
676/// Noise model types
677#[derive(Debug, Clone, PartialEq, Eq)]
678pub enum NoiseModelType {
679    /// Depolarizing noise
680    Depolarizing,
681    /// Amplitude damping
682    AmplitudeDamping,
683    /// Phase damping
684    PhaseDamping,
685    /// Pauli noise
686    Pauli,
687    /// Coherent noise
688    Coherent,
689    /// Correlated noise
690    Correlated,
691    /// Non-Markovian noise
692    NonMarkovian,
693}
694
695/// Measurement metadata
696#[derive(Debug, Clone)]
697pub struct MeasurementMetadata {
698    /// Device information
699    pub device_info: String,
700    /// Calibration data
701    pub calibration_timestamp: f64,
702    /// Environmental conditions
703    pub environment: HashMap<String, f64>,
704    /// Software versions
705    pub software_versions: HashMap<String, String>,
706}
707
708/// Mitigated data
709#[derive(Debug, Clone)]
710pub struct MitigatedData {
711    /// Mitigated expectation values
712    pub expectation_values: Array1<f64>,
713    /// Uncertainty estimates
714    pub uncertainties: Array1<f64>,
715    /// Mitigation overhead
716    pub overhead: f64,
717    /// Confidence intervals
718    pub confidence_intervals: Array2<f64>,
719    /// Mitigation metadata
720    pub metadata: MitigationMetadata,
721}
722
723/// Mitigation metadata
724#[derive(Debug, Clone)]
725pub struct MitigationMetadata {
726    /// Strategies applied
727    pub strategies_applied: Vec<String>,
728    /// Mitigation parameters
729    pub parameters: HashMap<String, f64>,
730    /// Success indicators
731    pub success_indicators: HashMap<String, f64>,
732    /// Performance metrics
733    pub performance_metrics: HashMap<String, f64>,
734}
735
736/// Fault tolerance analyzer
737#[derive(Debug)]
738pub struct FaultToleranceAnalyzer {
739    /// Analysis configuration
740    pub config: FaultToleranceConfig,
741    /// Threshold calculators
742    pub threshold_calculators: Vec<Box<dyn ThresholdCalculator>>,
743    /// Fault propagation models
744    pub propagation_models: Vec<Box<dyn FaultPropagationModel>>,
745    /// Resource estimators
746    pub resource_estimators: Vec<Box<dyn ResourceEstimator>>,
747}
748
749/// Fault tolerance configuration
750#[derive(Debug, Clone)]
751pub struct FaultToleranceConfig {
752    /// Target logical error rate
753    pub target_logical_error_rate: f64,
754    /// Physical error rate assumption
755    pub physical_error_rate: f64,
756    /// Computation depth
757    pub computation_depth: usize,
758    /// Resource optimization objective
759    pub optimization_objective: OptimizationObjective,
760}
761
762/// Optimization objectives for fault tolerance
763#[derive(Debug, Clone, PartialEq)]
764pub enum OptimizationObjective {
765    /// Minimize total qubits
766    MinimizeQubits,
767    /// Minimize computation time
768    MinimizeTime,
769    /// Minimize total resources
770    MinimizeResources,
771    /// Maximize success probability
772    MaximizeSuccess,
773    /// Multi-objective optimization
774    MultiObjective { weights: Vec<f64> },
775}
776
777/// Threshold calculator trait
778pub trait ThresholdCalculator: Send + Sync + std::fmt::Debug {
779    /// Calculate error threshold for a given code
780    fn calculate_threshold(&self, code: &dyn QuantumCode) -> Result<f64, QECError>;
781
782    /// Get calculator name
783    fn get_calculator_name(&self) -> &str;
784
785    /// Get calculation parameters
786    fn get_parameters(&self) -> HashMap<String, f64>;
787}
788
789/// Fault propagation model trait
790pub trait FaultPropagationModel: Send + Sync + std::fmt::Debug {
791    /// Model fault propagation
792    fn propagate_faults(
793        &self,
794        initial_faults: &Array1<u8>,
795        circuit: &QuantumCircuit,
796    ) -> Result<Array1<u8>, QECError>;
797
798    /// Get model name
799    fn get_model_name(&self) -> &str;
800
801    /// Get model parameters
802    fn get_parameters(&self) -> HashMap<String, f64>;
803}
804
805/// Quantum circuit representation
806#[derive(Debug, Clone)]
807pub struct QuantumCircuit {
808    /// Number of qubits
809    pub num_qubits: usize,
810    /// Circuit gates
811    pub gates: Vec<QuantumGate>,
812    /// Circuit depth
813    pub depth: usize,
814    /// Parallelization information
815    pub parallel_layers: Vec<Vec<usize>>,
816}
817
818/// Resource estimator trait
819pub trait ResourceEstimator: Send + Sync + std::fmt::Debug {
820    /// Estimate resources required
821    fn estimate_resources(
822        &self,
823        code: &dyn QuantumCode,
824        computation: &QuantumCircuit,
825    ) -> Result<ResourceEstimate, QECError>;
826
827    /// Get estimator name
828    fn get_estimator_name(&self) -> &str;
829}
830
831/// Resource estimate
832#[derive(Debug, Clone)]
833pub struct ResourceEstimate {
834    /// Total physical qubits
835    pub total_qubits: usize,
836    /// Total time steps
837    pub total_time: usize,
838    /// Gate count by type
839    pub gate_counts: HashMap<String, usize>,
840    /// Memory requirements
841    pub memory_requirements: usize,
842    /// Success probability
843    pub success_probability: f64,
844    /// Resource overhead
845    pub overhead: ResourceOverhead,
846}
847
848/// Resource overhead
849#[derive(Debug, Clone)]
850pub struct ResourceOverhead {
851    /// Space overhead
852    pub space_overhead: f64,
853    /// Time overhead
854    pub time_overhead: f64,
855    /// Computational overhead
856    pub computational_overhead: f64,
857    /// Energy overhead
858    pub energy_overhead: f64,
859}
860
861/// QEC performance metrics
862#[derive(Debug, Clone)]
863pub struct QECMetrics {
864    /// Logical error rate
865    pub logical_error_rate: f64,
866    /// Syndrome detection fidelity
867    pub syndrome_fidelity: f64,
868    /// Decoding success rate
869    pub decoding_success_rate: f64,
870    /// Correction latency
871    pub correction_latency: f64,
872    /// Resource efficiency
873    pub resource_efficiency: f64,
874    /// Fault tolerance margin
875    pub fault_tolerance_margin: f64,
876    /// Overall QEC performance
877    pub overall_performance: f64,
878}
879
880/// Syndrome data
881#[derive(Debug, Clone)]
882pub struct SyndromeData {
883    pub syndrome_bits: Vec<bool>,
884    pub measurement_round: usize,
885    pub timestamp: std::time::SystemTime,
886    pub confidence_scores: Vec<f64>,
887    pub measurement_errors: Vec<bool>,
888}
889
890/// Error syndrome
891#[derive(Debug, Clone)]
892pub struct ErrorSyndrome {
893    pub detected_errors: Vec<DetectedError>,
894    pub syndrome_weight: usize,
895    pub decoding_confidence: f64,
896    pub correction_success_probability: f64,
897}
898
899/// Detected error
900#[derive(Debug, Clone)]
901pub struct DetectedError {
902    pub error_type: ErrorType,
903    pub qubit_index: usize,
904    pub error_probability: f64,
905    pub correction_operation: CorrectionOperation,
906}
907
908/// Performance benchmark
909#[derive(Debug, Clone)]
910pub struct PerformanceBenchmark {
911    pub test_name: String,
912    pub error_rates: Vec<f64>,
913    pub logical_error_rates: Vec<f64>,
914    pub decoding_times: Vec<f64>,
915    pub memory_usage: Vec<f64>,
916    pub success_rates: Vec<f64>,
917    pub benchmark_timestamp: std::time::SystemTime,
918}
919
920/// QEC errors
921#[derive(Debug, Clone)]
922pub enum QECError {
923    /// Invalid code parameters
924    InvalidCodeParameters(String),
925    /// Syndrome extraction failed
926    SyndromeExtractionFailed(String),
927    /// Decoding failed
928    DecodingFailed(String),
929    /// Insufficient error correction capability
930    InsufficientCorrection(String),
931    /// Threshold exceeded
932    ThresholdExceeded(String),
933    /// Resource estimation failed
934    ResourceEstimationFailed(String),
935    /// Numerical error
936    NumericalError(String),
937}
938
939impl std::fmt::Display for QECError {
940    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
941        match self {
942            Self::InvalidCodeParameters(msg) => write!(f, "Invalid code parameters: {msg}"),
943            Self::SyndromeExtractionFailed(msg) => {
944                write!(f, "Syndrome extraction failed: {msg}")
945            }
946            Self::DecodingFailed(msg) => write!(f, "Decoding failed: {msg}"),
947            Self::InsufficientCorrection(msg) => write!(f, "Insufficient correction: {msg}"),
948            Self::ThresholdExceeded(msg) => write!(f, "Threshold exceeded: {msg}"),
949            Self::ResourceEstimationFailed(msg) => {
950                write!(f, "Resource estimation failed: {msg}")
951            }
952            Self::NumericalError(msg) => write!(f, "Numerical error: {msg}"),
953        }
954    }
955}
956
957impl std::error::Error for QECError {}
958
959impl QuantumErrorCorrection {
960    /// Create new quantum error correction system
961    pub fn new(num_logical_qubits: usize, config: QECConfig) -> Self {
962        let num_physical_qubits = Self::estimate_physical_qubits(num_logical_qubits, &config);
963
964        Self {
965            num_logical_qubits,
966            num_physical_qubits,
967            config,
968            codes: Vec::new(),
969            syndrome_detector: SyndromeDetector::new(num_physical_qubits),
970            mitigation_strategies: Vec::new(),
971            fault_tolerance: FaultToleranceAnalyzer::new(),
972            metrics: QECMetrics::default(),
973        }
974    }
975
976    /// Perform quantum error correction
977    pub fn correct_errors(&mut self, quantum_state: &Array1<f64>) -> Result<Array1<f64>, QECError> {
978        println!(
979            "Starting quantum error correction for {} logical qubits",
980            self.num_logical_qubits
981        );
982
983        // Step 1: Extract error syndrome
984        let syndrome = self.extract_syndrome(quantum_state)?;
985
986        // Step 2: Decode error from syndrome
987        let error_estimate = self.decode_syndrome(&syndrome)?;
988
989        // Step 3: Apply error correction
990        let corrected_state = self.apply_correction(quantum_state, &error_estimate)?;
991
992        // Step 4: Verify correction success
993        let correction_successful = self.verify_correction(&corrected_state, quantum_state)?;
994
995        // Step 5: Update metrics
996        self.update_metrics(&syndrome, &error_estimate, correction_successful);
997
998        // Step 6: Apply error mitigation if needed
999        let final_state = if correction_successful {
1000            corrected_state
1001        } else {
1002            self.apply_error_mitigation(&corrected_state)?
1003        };
1004
1005        println!(
1006            "Error correction completed. Success rate: {:.4}",
1007            self.metrics.decoding_success_rate
1008        );
1009        Ok(final_state)
1010    }
1011
1012    /// Extract error syndrome from quantum state
1013    fn extract_syndrome(&mut self, quantum_state: &Array1<f64>) -> Result<Array1<u8>, QECError> {
1014        if self.codes.is_empty() {
1015            return Err(QECError::InvalidCodeParameters(
1016                "No quantum codes loaded".to_string(),
1017            ));
1018        }
1019
1020        // Use the first loaded code for syndrome extraction
1021        let syndrome = self.codes[0].extract_syndrome(quantum_state)?;
1022
1023        // Store syndrome in history
1024        self.syndrome_detector
1025            .syndrome_history
1026            .history
1027            .push(SyndromeRecord {
1028                syndrome: syndrome.clone(),
1029                timestamp: std::time::SystemTime::now()
1030                    .duration_since(std::time::UNIX_EPOCH)
1031                    .unwrap_or_default()
1032                    .as_secs_f64(),
1033                error: None,
1034                correction: None,
1035                success: false,
1036            });
1037
1038        // Maintain history size
1039        let max_length = self.syndrome_detector.syndrome_history.max_length;
1040        if self.syndrome_detector.syndrome_history.history.len() > max_length {
1041            self.syndrome_detector.syndrome_history.history.remove(0);
1042        }
1043
1044        Ok(syndrome)
1045    }
1046
1047    /// Decode error from syndrome
1048    fn decode_syndrome(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1049        match &self.config.decoding_algorithm {
1050            DecodingAlgorithm::MWPM => self.decode_mwpm(syndrome),
1051            DecodingAlgorithm::BeliefPropagation => self.decode_belief_propagation(syndrome),
1052            DecodingAlgorithm::NeuralNetwork { architecture: _ } => {
1053                self.decode_neural_network(syndrome)
1054            }
1055            DecodingAlgorithm::UnionFind => self.decode_union_find(syndrome),
1056            DecodingAlgorithm::MachineLearning { model_type } => {
1057                self.decode_machine_learning(syndrome, model_type)
1058            }
1059            _ => self.decode_lookup_table(syndrome),
1060        }
1061    }
1062
1063    /// Minimum weight perfect matching decoder
1064    fn decode_mwpm(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1065        // Simplified MWPM decoder
1066        let mut error_estimate = Array1::zeros(self.num_physical_qubits);
1067
1068        // Find syndrome positions
1069        let syndrome_positions: Vec<usize> = syndrome
1070            .iter()
1071            .enumerate()
1072            .filter_map(|(i, &s)| if s != 0 { Some(i) } else { None })
1073            .collect();
1074
1075        // Pair up syndrome positions (simplified pairing)
1076        for chunk in syndrome_positions.chunks(2) {
1077            if chunk.len() == 2 {
1078                let start = chunk[0];
1079                let end = chunk[1];
1080
1081                // Apply correction along shortest path (simplified)
1082                for i in start..=end {
1083                    if i < error_estimate.len() {
1084                        error_estimate[i] = 1;
1085                    }
1086                }
1087            }
1088        }
1089
1090        Ok(error_estimate)
1091    }
1092
1093    /// Belief propagation decoder
1094    fn decode_belief_propagation(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1095        // Simplified belief propagation
1096        let mut beliefs = Array1::from_elem(self.num_physical_qubits, 0.5);
1097        let max_iterations = 100;
1098        let tolerance = 1e-6;
1099
1100        for _iteration in 0..max_iterations {
1101            let old_beliefs = beliefs.clone();
1102
1103            // Update beliefs based on syndrome constraints
1104            for (i, &syndrome_bit) in syndrome.iter().enumerate() {
1105                if syndrome_bit != 0 {
1106                    // Update beliefs for qubits connected to this syndrome bit
1107                    for j in 0..self.num_physical_qubits {
1108                        if self.are_connected(i, j) {
1109                            beliefs[j] = 1.0 - beliefs[j];
1110                        }
1111                    }
1112                }
1113            }
1114
1115            // Check convergence
1116            let diff = (&beliefs - &old_beliefs).mapv(|x: f64| x.abs()).sum();
1117            if diff < tolerance {
1118                break;
1119            }
1120        }
1121
1122        // Threshold beliefs to get binary error estimate
1123        let error_estimate = beliefs.mapv(|x| u8::from(x > 0.5));
1124        Ok(error_estimate)
1125    }
1126
1127    /// Neural network decoder (simplified)
1128    fn decode_neural_network(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1129        // Simplified neural network decoder
1130        let input_size = syndrome.len();
1131        let hidden_size = input_size * 2;
1132        let output_size = self.num_physical_qubits;
1133
1134        // Random weights for demonstration
1135        let mut rng = thread_rng();
1136        let w1 = Array2::from_shape_fn((hidden_size, input_size), |_| rng.gen_range(-1.0..1.0));
1137        let w2 = Array2::from_shape_fn((output_size, hidden_size), |_| rng.gen_range(-1.0..1.0));
1138
1139        // Convert syndrome to float
1140        let syndrome_float = syndrome.mapv(|x| x as f64);
1141
1142        // Forward pass
1143        let hidden = w1
1144            .dot(&syndrome_float)
1145            .mapv(|x| if x > 0.0 { x } else { 0.0 }); // ReLU
1146        let output = w2.dot(&hidden).mapv(|x| 1.0 / (1.0 + (-x).exp())); // Sigmoid
1147
1148        // Threshold to binary
1149        let error_estimate = output.mapv(|x| u8::from(x > 0.5));
1150        Ok(error_estimate)
1151    }
1152
1153    /// Union-find decoder
1154    fn decode_union_find(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1155        // Simplified union-find decoder
1156        let mut error_estimate = Array1::zeros(self.num_physical_qubits);
1157
1158        // Find connected components of syndrome
1159        let mut parent: Vec<usize> = (0..syndrome.len()).collect();
1160
1161        // Union-find operations (simplified)
1162        for i in 0..syndrome.len() {
1163            if syndrome[i] != 0 {
1164                for j in (i + 1)..syndrome.len() {
1165                    if syndrome[j] != 0 && self.are_syndrome_connected(i, j) {
1166                        parent[j] = i;
1167                    }
1168                }
1169            }
1170        }
1171
1172        // Apply corrections based on components
1173        for i in 0..syndrome.len() {
1174            if syndrome[i] != 0 {
1175                let root = self.find_root(&parent, i);
1176                if root < error_estimate.len() {
1177                    error_estimate[root] = 1;
1178                }
1179            }
1180        }
1181
1182        Ok(error_estimate)
1183    }
1184
1185    /// Machine learning decoder
1186    fn decode_machine_learning(
1187        &self,
1188        syndrome: &Array1<u8>,
1189        model_type: &MLModelType,
1190    ) -> Result<Array1<u8>, QECError> {
1191        match model_type {
1192            MLModelType::RNN => self.decode_rnn(syndrome),
1193            MLModelType::CNN => self.decode_cnn(syndrome),
1194            MLModelType::Transformer => self.decode_transformer(syndrome),
1195            MLModelType::GNN => self.decode_gnn(syndrome),
1196            MLModelType::VAE => self.decode_vae(syndrome),
1197            // Handle additional enum variants
1198            MLModelType::ConvolutionalNN => self.decode_cnn(syndrome),
1199            MLModelType::RecurrentNN => self.decode_rnn(syndrome),
1200            MLModelType::TransformerNetwork => self.decode_transformer(syndrome),
1201            MLModelType::GraphNeuralNetwork => self.decode_gnn(syndrome),
1202            MLModelType::ReinforcementLearning => self.decode_rnn(syndrome), // Use RNN as fallback
1203            MLModelType::DeepQNetwork => self.decode_rnn(syndrome),          // Use RNN as fallback
1204        }
1205    }
1206
1207    /// RNN decoder (simplified)
1208    fn decode_rnn(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1209        // Simplified RNN implementation
1210        let hidden_size = syndrome.len();
1211        let mut hidden_state = Array1::<f64>::zeros(hidden_size);
1212        let mut output = Array1::zeros(self.num_physical_qubits);
1213
1214        // Process syndrome sequentially
1215        for &syndrome_bit in syndrome {
1216            // Update hidden state (simplified LSTM-like operation)
1217            hidden_state =
1218                hidden_state.mapv(|h: f64| 0.5f64.mul_add(h, 0.5 * (syndrome_bit as f64)));
1219        }
1220
1221        // Generate output from final hidden state
1222        for i in 0..output.len() {
1223            output[i] = if hidden_state[i % hidden_state.len()] > 0.5 {
1224                1.0
1225            } else {
1226                0.0
1227            };
1228        }
1229
1230        Ok(output.mapv(|x| x as u8))
1231    }
1232
1233    /// CNN decoder (simplified)
1234    fn decode_cnn(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1235        // Simplified CNN implementation
1236        let kernel_size = 3;
1237        let mut convolved = Array1::zeros(syndrome.len());
1238
1239        // Apply convolution
1240        for i in 0..syndrome.len() {
1241            let mut sum = 0.0;
1242            for j in 0..kernel_size {
1243                let idx = (i + j).saturating_sub(kernel_size / 2);
1244                if idx < syndrome.len() {
1245                    sum += syndrome[idx] as f64;
1246                }
1247            }
1248            convolved[i] = sum / kernel_size as f64;
1249        }
1250
1251        // Map to error estimate
1252        let error_estimate = Array1::from_vec(
1253            (0..self.num_physical_qubits)
1254                .map(|i| u8::from(convolved[i % convolved.len()] > 0.5))
1255                .collect(),
1256        );
1257
1258        Ok(error_estimate)
1259    }
1260
1261    /// Transformer decoder (simplified)
1262    fn decode_transformer(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1263        // Simplified transformer implementation using attention mechanism
1264        let seq_len = syndrome.len();
1265        let _d_model = seq_len;
1266
1267        // Self-attention (simplified)
1268        let mut attention_output = Array1::zeros(seq_len);
1269        for i in 0..seq_len {
1270            let mut weighted_sum = 0.0;
1271            let mut weight_sum = 0.0;
1272
1273            for j in 0..seq_len {
1274                let attention_weight = (-((i as f64 - j as f64).powi(2)) / 2.0).exp();
1275                weighted_sum += attention_weight * syndrome[j] as f64;
1276                weight_sum += attention_weight;
1277            }
1278
1279            attention_output[i] = if weight_sum > 0.0 {
1280                weighted_sum / weight_sum
1281            } else {
1282                0.0
1283            };
1284        }
1285
1286        // Map to error estimate
1287        let error_estimate = Array1::from_vec(
1288            (0..self.num_physical_qubits)
1289                .map(|i| u8::from(attention_output[i % attention_output.len()] > 0.5))
1290                .collect(),
1291        );
1292
1293        Ok(error_estimate)
1294    }
1295
1296    /// Graph neural network decoder (simplified)
1297    fn decode_gnn(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1298        // Simplified GNN implementation
1299        let num_nodes = syndrome.len();
1300        let mut node_features = syndrome.mapv(|x| x as f64);
1301
1302        // Message passing iterations
1303        for _ in 0..3 {
1304            let mut new_features = node_features.clone();
1305
1306            for i in 0..num_nodes {
1307                let mut neighbor_sum = 0.0;
1308                let mut neighbor_count = 0;
1309
1310                // Aggregate from neighbors
1311                for j in 0..num_nodes {
1312                    if i != j && self.are_syndrome_connected(i, j) {
1313                        neighbor_sum += node_features[j];
1314                        neighbor_count += 1;
1315                    }
1316                }
1317
1318                if neighbor_count > 0 {
1319                    new_features[i] =
1320                        f64::midpoint(node_features[i], neighbor_sum / neighbor_count as f64);
1321                }
1322            }
1323
1324            node_features = new_features;
1325        }
1326
1327        // Map to error estimate
1328        let error_estimate = Array1::from_vec(
1329            (0..self.num_physical_qubits)
1330                .map(|i| u8::from(node_features[i % node_features.len()] > 0.5))
1331                .collect(),
1332        );
1333
1334        Ok(error_estimate)
1335    }
1336
1337    /// Variational autoencoder decoder (simplified)
1338    fn decode_vae(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1339        // Simplified VAE implementation
1340        let latent_dim = syndrome.len() / 2;
1341        let syndrome_float = syndrome.mapv(|x| x as f64);
1342
1343        // Encoder (syndrome -> latent space)
1344        let mut latent = Array1::zeros(latent_dim);
1345        for i in 0..latent_dim {
1346            let start_idx = i * 2;
1347            let end_idx = ((i + 1) * 2).min(syndrome.len());
1348
1349            let mut sum = 0.0;
1350            for j in start_idx..end_idx {
1351                sum += syndrome_float[j];
1352            }
1353            latent[i] = sum / (end_idx - start_idx) as f64;
1354        }
1355
1356        // Decoder (latent space -> error estimate)
1357        let mut error_estimate = Array1::zeros(self.num_physical_qubits);
1358        for i in 0..self.num_physical_qubits {
1359            let latent_idx = i % latent_dim;
1360            error_estimate[i] = u8::from(latent[latent_idx] > 0.5);
1361        }
1362
1363        Ok(error_estimate)
1364    }
1365
1366    /// Lookup table decoder (fallback)
1367    fn decode_lookup_table(&self, syndrome: &Array1<u8>) -> Result<Array1<u8>, QECError> {
1368        // Simple lookup table based on syndrome patterns
1369        let mut error_estimate = Array1::zeros(self.num_physical_qubits);
1370
1371        // Convert syndrome to pattern
1372        let pattern: String = syndrome
1373            .iter()
1374            .map(|&x| char::from_digit(x as u32, 10).unwrap_or('0'))
1375            .collect();
1376
1377        // Simple pattern matching
1378        match pattern.as_str() {
1379            s if s.contains('1') => {
1380                // Find first syndrome bit and apply correction
1381                if let Some(pos) = syndrome.iter().position(|&x| x != 0) {
1382                    if pos < error_estimate.len() {
1383                        error_estimate[pos] = 1;
1384                    }
1385                }
1386            }
1387            _ => {
1388                // No syndrome, no correction needed
1389            }
1390        }
1391
1392        Ok(error_estimate)
1393    }
1394
1395    /// Apply error correction to quantum state
1396    fn apply_correction(
1397        &self,
1398        quantum_state: &Array1<f64>,
1399        error_estimate: &Array1<u8>,
1400    ) -> Result<Array1<f64>, QECError> {
1401        let mut corrected_state = quantum_state.clone();
1402
1403        // Apply Pauli corrections based on error estimate
1404        for (i, &error_bit) in error_estimate.iter().enumerate() {
1405            if error_bit != 0 && i < corrected_state.len() {
1406                // Apply Pauli-X correction (bit flip)
1407                self.apply_pauli_x(&mut corrected_state, i);
1408            }
1409        }
1410
1411        Ok(corrected_state)
1412    }
1413
1414    /// Apply Pauli-X gate to quantum state
1415    fn apply_pauli_x(&self, state: &mut Array1<f64>, qubit: usize) {
1416        let num_qubits = (state.len() as f64).log2() as usize;
1417        if qubit >= num_qubits {
1418            return;
1419        }
1420
1421        let qubit_mask = 1 << qubit;
1422
1423        for i in 0..state.len() {
1424            let j = i ^ qubit_mask;
1425            if i < j {
1426                let temp = state[i];
1427                state[i] = state[j];
1428                state[j] = temp;
1429            }
1430        }
1431    }
1432
1433    /// Verify correction success
1434    fn verify_correction(
1435        &self,
1436        corrected_state: &Array1<f64>,
1437        original_state: &Array1<f64>,
1438    ) -> Result<bool, QECError> {
1439        // Simple verification: check if syndrome is reduced
1440        let original_syndrome = if self.codes.is_empty() {
1441            Array1::zeros(1)
1442        } else {
1443            self.codes[0].extract_syndrome(original_state)?
1444        };
1445
1446        let corrected_syndrome = if self.codes.is_empty() {
1447            Array1::zeros(1)
1448        } else {
1449            self.codes[0].extract_syndrome(corrected_state)?
1450        };
1451
1452        let original_syndrome_weight = original_syndrome.iter().map(|&x| x as usize).sum::<usize>();
1453        let corrected_syndrome_weight = corrected_syndrome
1454            .iter()
1455            .map(|&x| x as usize)
1456            .sum::<usize>();
1457
1458        Ok(corrected_syndrome_weight <= original_syndrome_weight)
1459    }
1460
1461    /// Apply error mitigation as fallback
1462    fn apply_error_mitigation(&self, state: &Array1<f64>) -> Result<Array1<f64>, QECError> {
1463        // Simple error mitigation: noise reduction
1464        let mut mitigated_state = state.clone();
1465
1466        // Apply simple denoising
1467        let noise_threshold = 0.01;
1468        for value in &mut mitigated_state {
1469            if value.abs() < noise_threshold {
1470                *value = 0.0;
1471            }
1472        }
1473
1474        // Renormalize
1475        let norm = mitigated_state.dot(&mitigated_state).sqrt();
1476        if norm > 1e-15 {
1477            mitigated_state /= norm;
1478        }
1479
1480        Ok(mitigated_state)
1481    }
1482
1483    /// Update performance metrics
1484    fn update_metrics(
1485        &mut self,
1486        syndrome: &Array1<u8>,
1487        error_estimate: &Array1<u8>,
1488        success: bool,
1489    ) {
1490        // Update syndrome detection fidelity
1491        let syndrome_weight = syndrome.iter().map(|&x| x as f64).sum::<f64>();
1492        let total_syndromes = syndrome.len() as f64;
1493        self.metrics.syndrome_fidelity = 1.0 - syndrome_weight / total_syndromes;
1494
1495        // Update decoding success rate (exponential moving average)
1496        let alpha = 0.1;
1497        let success_value = if success { 1.0 } else { 0.0 };
1498        self.metrics.decoding_success_rate =
1499            alpha * success_value + (1.0 - alpha) * self.metrics.decoding_success_rate;
1500
1501        // Update logical error rate estimate
1502        let error_weight = error_estimate.iter().map(|&x| x as f64).sum::<f64>();
1503        let total_qubits = error_estimate.len() as f64;
1504        self.metrics.logical_error_rate = error_weight / total_qubits;
1505
1506        // Update overall performance
1507        self.metrics.overall_performance = (1.0 - self.metrics.logical_error_rate).mul_add(
1508            0.3,
1509            self.metrics
1510                .syndrome_fidelity
1511                .mul_add(0.3, self.metrics.decoding_success_rate * 0.4),
1512        );
1513    }
1514
1515    /// Helper function to check if qubits are connected
1516    const fn are_connected(&self, qubit1: usize, qubit2: usize) -> bool {
1517        // Simplified connectivity: nearest neighbors in 1D
1518        (qubit1 as i32 - qubit2 as i32).abs() == 1
1519    }
1520
1521    /// Helper function to check if syndrome positions are connected
1522    const fn are_syndrome_connected(&self, pos1: usize, pos2: usize) -> bool {
1523        // Simplified: positions are connected if they're within distance 2
1524        (pos1 as i32 - pos2 as i32).abs() <= 2
1525    }
1526
1527    /// Helper function for union-find
1528    fn find_root(&self, parent: &[usize], mut x: usize) -> usize {
1529        while parent[x] != x {
1530            x = parent[x];
1531        }
1532        x
1533    }
1534
1535    /// Estimate number of physical qubits needed
1536    const fn estimate_physical_qubits(num_logical: usize, config: &QECConfig) -> usize {
1537        match &config.code_type {
1538            QuantumCodeType::SurfaceCode { .. } => {
1539                // Surface code: roughly d^2 physical qubits per logical qubit
1540                let d = config.code_distance;
1541                num_logical * d * d
1542            }
1543            QuantumCodeType::ColorCode { .. } => {
1544                // Color code: similar to surface code
1545                let d = config.code_distance;
1546                num_logical * d * d
1547            }
1548            _ => {
1549                // Default estimate
1550                num_logical * config.code_distance * config.code_distance
1551            }
1552        }
1553    }
1554}
1555
1556impl SyndromeDetector {
1557    /// Create new syndrome detector
1558    pub fn new(num_qubits: usize) -> Self {
1559        Self {
1560            config: SyndromeDetectionConfig {
1561                num_rounds: 3,
1562                extraction_frequency: 1000.0, // Hz
1563                detection_threshold: 0.5,
1564                correlation_window: 100,
1565                use_flag_qubits: false,
1566            },
1567            measurement_circuits: Vec::new(),
1568            error_correlations: ErrorCorrelationTracker {
1569                spatial_correlations: Array2::zeros((num_qubits, num_qubits)),
1570                temporal_correlations: Array1::zeros(100),
1571                cross_correlations: Array3::zeros((3, num_qubits, num_qubits)), // X, Y, Z errors
1572                correlation_times: Array1::ones(num_qubits),
1573            },
1574            syndrome_history: SyndromeHistory {
1575                history: Vec::new(),
1576                max_length: 1000,
1577                pattern_detector: PatternDetector {
1578                    patterns: Vec::new(),
1579                    pattern_frequency: HashMap::new(),
1580                    prediction_model: None,
1581                },
1582            },
1583        }
1584    }
1585}
1586
1587impl Default for FaultToleranceAnalyzer {
1588    fn default() -> Self {
1589        Self::new()
1590    }
1591}
1592
1593impl FaultToleranceAnalyzer {
1594    /// Create new fault tolerance analyzer
1595    pub fn new() -> Self {
1596        Self {
1597            config: FaultToleranceConfig {
1598                target_logical_error_rate: 1e-6,
1599                physical_error_rate: 1e-3,
1600                computation_depth: 1000,
1601                optimization_objective: OptimizationObjective::MinimizeQubits,
1602            },
1603            threshold_calculators: Vec::new(),
1604            propagation_models: Vec::new(),
1605            resource_estimators: Vec::new(),
1606        }
1607    }
1608}
1609
1610impl Default for QECMetrics {
1611    fn default() -> Self {
1612        Self {
1613            logical_error_rate: 0.0,
1614            syndrome_fidelity: 1.0,
1615            decoding_success_rate: 1.0,
1616            correction_latency: 0.0,
1617            resource_efficiency: 1.0,
1618            fault_tolerance_margin: 1.0,
1619            overall_performance: 1.0,
1620        }
1621    }
1622}
1623
1624/// Create default QEC configuration
1625pub fn create_default_qec_config() -> QECConfig {
1626    QECConfig {
1627        code_type: QuantumCodeType::SurfaceCode {
1628            lattice_type: LatticeType::Square,
1629        },
1630        code_distance: 3,
1631        correction_frequency: 1000.0,
1632        syndrome_method: SyndromeExtractionMethod::Standard,
1633        decoding_algorithm: DecodingAlgorithm::MWPM,
1634        error_mitigation: ErrorMitigationConfig {
1635            zero_noise_extrapolation: true,
1636            probabilistic_error_cancellation: false,
1637            symmetry_verification: true,
1638            virtual_distillation: false,
1639            error_amplification: ErrorAmplificationConfig {
1640                amplification_factors: vec![1.0, 1.5, 2.0],
1641                max_amplification: 3.0,
1642                strategy: AmplificationStrategy::Linear,
1643            },
1644            clifford_data_regression: false,
1645        },
1646        adaptive_correction: AdaptiveCorrectionConfig {
1647            adaptive_thresholding: false,
1648            dynamic_distance: false,
1649            real_time_code_switching: false,
1650            performance_adaptation: PerformanceAdaptationConfig {
1651                error_rate_threshold: 0.01,
1652                monitoring_window: 100,
1653                adaptation_sensitivity: 0.1,
1654                min_adaptation_interval: 10.0,
1655            },
1656            learning_adaptation: LearningAdaptationConfig {
1657                reinforcement_learning: false,
1658                learning_rate: 0.01,
1659                replay_buffer_size: 10000,
1660                update_frequency: 100,
1661            },
1662        },
1663        threshold_estimation: ThresholdEstimationConfig {
1664            real_time_estimation: false,
1665            estimation_method: ThresholdEstimationMethod::MonteCarlo,
1666            confidence_level: 0.95,
1667            update_frequency: 1000,
1668        },
1669    }
1670}
1671
1672/// Create QEC system for optimization problems
1673pub fn create_optimization_qec(num_logical_qubits: usize) -> QuantumErrorCorrection {
1674    let config = create_default_qec_config();
1675    QuantumErrorCorrection::new(num_logical_qubits, config)
1676}
1677
1678/// Create adaptive QEC configuration
1679pub fn create_adaptive_qec_config() -> QECConfig {
1680    let mut config = create_default_qec_config();
1681    config.adaptive_correction.adaptive_thresholding = true;
1682    config.adaptive_correction.dynamic_distance = true;
1683    config.adaptive_correction.real_time_code_switching = true;
1684    config
1685        .adaptive_correction
1686        .learning_adaptation
1687        .reinforcement_learning = true;
1688    config.threshold_estimation.real_time_estimation = true;
1689    config
1690}
1691
1692#[cfg(test)]
1693mod tests {
1694    use super::*;
1695
1696    #[test]
1697    fn test_qec_creation() {
1698        let mut qec = create_optimization_qec(2);
1699        assert_eq!(qec.num_logical_qubits, 2);
1700        assert_eq!(qec.config.code_distance, 3);
1701    }
1702
1703    #[test]
1704    fn test_syndrome_extraction() {
1705        let mut qec = create_optimization_qec(1);
1706        let mut quantum_state = Array1::from_vec(vec![1.0, 0.0, 0.0, 0.0]); // |00⟩
1707
1708        // This should fail since no codes are loaded yet
1709        let mut result = qec.extract_syndrome(&quantum_state);
1710        assert!(result.is_err());
1711    }
1712
1713    #[test]
1714    fn test_mwpm_decoding() {
1715        let qec = create_optimization_qec(2);
1716        let syndrome = Array1::from_vec(vec![1, 0, 1, 0]);
1717
1718        let error_estimate = qec
1719            .decode_mwpm(&syndrome)
1720            .expect("MWPM decoding should succeed");
1721        assert_eq!(error_estimate.len(), qec.num_physical_qubits);
1722    }
1723
1724    #[test]
1725    fn test_belief_propagation_decoding() {
1726        let qec = create_optimization_qec(2);
1727        let syndrome = Array1::from_vec(vec![1, 1, 0, 0]);
1728
1729        let error_estimate = qec
1730            .decode_belief_propagation(&syndrome)
1731            .expect("Belief propagation decoding should succeed");
1732        assert_eq!(error_estimate.len(), qec.num_physical_qubits);
1733    }
1734
1735    #[test]
1736    fn test_pauli_x_application() {
1737        let mut qec = create_optimization_qec(1);
1738        let mut state = Array1::from_vec(vec![1.0, 0.0]);
1739
1740        qec.apply_pauli_x(&mut state, 0);
1741
1742        // Should flip the state |0⟩ -> |1⟩
1743        assert!((state[0] - 0.0).abs() < 1e-10);
1744        assert!((state[1] - 1.0).abs() < 1e-10);
1745    }
1746
1747    #[test]
1748    fn test_physical_qubit_estimation() {
1749        let mut config = create_default_qec_config();
1750        let num_physical = QuantumErrorCorrection::estimate_physical_qubits(2, &config);
1751
1752        // For distance 3 surface code: 2 * 3^2 = 18 physical qubits
1753        assert_eq!(num_physical, 18);
1754    }
1755}
quantrs2_tytan/quantum_error_correction.rs

quantrs2_tytan/
quantum_error_correction.rs
