quantrs2_tytan/
tensor_network_sampler.rs

1//! Tensor Network-Based Quantum Annealing Sampler
2//!
3//! This module implements advanced tensor network algorithms for quantum annealing,
4//! including Matrix Product States (MPS), Projected Entangled Pair States (PEPS),
5//! and Multi-scale Entanglement Renormalization Ansatz (MERA) for optimization.
6
7#![allow(dead_code)]
8
9use crate::sampler::{SampleResult, Sampler, SamplerError, SamplerResult};
10use scirs2_core::ndarray::{Array1, Array2, ArrayD};
11use scirs2_core::random::prelude::*;
12use std::collections::HashMap;
13
14/// Tensor network sampler for quantum annealing
15pub struct TensorNetworkSampler {
16    /// Sampler configuration
17    pub config: TensorNetworkConfig,
18    /// Tensor network representation
19    pub tensor_network: TensorNetwork,
20    /// Optimization algorithms
21    pub optimization: TensorOptimization,
22    /// Compression methods
23    pub compression: TensorCompression,
24    /// Performance metrics
25    pub metrics: TensorNetworkMetrics,
26}
27
28/// Configuration for tensor network sampler
29#[derive(Debug, Clone)]
30pub struct TensorNetworkConfig {
31    /// Tensor network type
32    pub network_type: TensorNetworkType,
33    /// Maximum bond dimension
34    pub max_bond_dimension: usize,
35    /// Compression tolerance
36    pub compression_tolerance: f64,
37    /// Number of sweeps for optimization
38    pub num_sweeps: usize,
39    /// Convergence tolerance
40    pub convergence_tolerance: f64,
41    /// Enable GPU acceleration
42    pub use_gpu: bool,
43    /// Parallel processing settings
44    pub parallel_config: ParallelConfig,
45    /// Memory management
46    pub memory_config: MemoryConfig,
47}
48
49/// Types of tensor networks
50#[derive(Debug, Clone, PartialEq, Eq)]
51pub enum TensorNetworkType {
52    /// Matrix Product State
53    MPS { bond_dimension: usize },
54    /// Projected Entangled Pair State
55    PEPS {
56        bond_dimension: usize,
57        lattice_shape: (usize, usize),
58    },
59    /// Multi-scale Entanglement Renormalization Ansatz
60    MERA {
61        layers: usize,
62        branching_factor: usize,
63    },
64    /// Tree Tensor Network
65    TTN { tree_structure: TreeStructure },
66    /// Infinite Matrix Product State
67    IMps { unit_cell_size: usize },
68    /// Infinite Projected Entangled Pair State
69    IPeps { unit_cell_shape: (usize, usize) },
70    /// Branching MERA
71    BranchingMERA {
72        layers: usize,
73        branching_tree: BranchingTree,
74    },
75}
76
77/// Tree structure for TTN
78#[derive(Debug, Clone, PartialEq, Eq)]
79pub struct TreeStructure {
80    /// Tree nodes
81    pub nodes: Vec<TreeNode>,
82    /// Tree edges
83    pub edges: Vec<(usize, usize)>,
84    /// Root node
85    pub root: usize,
86    /// Tree depth
87    pub depth: usize,
88}
89
90/// Tree node
91#[derive(Debug, Clone, PartialEq, Eq)]
92pub struct TreeNode {
93    /// Node identifier
94    pub id: usize,
95    /// Physical indices
96    pub physical_indices: Vec<usize>,
97    /// Virtual indices
98    pub virtual_indices: Vec<usize>,
99    /// Tensor dimension
100    pub tensor_shape: Vec<usize>,
101}
102
103/// Branching tree for branching MERA
104#[derive(Debug, Clone, PartialEq, Eq)]
105pub struct BranchingTree {
106    /// Branching factors at each layer
107    pub branching_factors: Vec<usize>,
108    /// Isometry placements
109    pub isometry_placements: Vec<Vec<usize>>,
110    /// Disentangler placements
111    pub disentangler_placements: Vec<Vec<usize>>,
112}
113
114/// Parallel processing configuration
115#[derive(Debug, Clone)]
116pub struct ParallelConfig {
117    /// Number of threads
118    pub num_threads: usize,
119    /// Enable distributed computing
120    pub distributed: bool,
121    /// Chunk size for parallel operations
122    pub chunk_size: usize,
123    /// Load balancing strategy
124    pub load_balancing: LoadBalancingStrategy,
125}
126
127/// Load balancing strategies
128#[derive(Debug, Clone, PartialEq, Eq)]
129pub enum LoadBalancingStrategy {
130    /// Static load balancing
131    Static,
132    /// Dynamic load balancing
133    Dynamic,
134    /// Work stealing
135    WorkStealing,
136    /// Adaptive load balancing
137    Adaptive,
138}
139
140/// Memory management configuration
141#[derive(Debug, Clone)]
142pub struct MemoryConfig {
143    /// Maximum memory usage (GB)
144    pub max_memory_gb: f64,
145    /// Enable memory mapping
146    pub memory_mapping: bool,
147    /// Garbage collection frequency
148    pub gc_frequency: usize,
149    /// Cache optimization
150    pub cache_optimization: CacheOptimization,
151}
152
153/// Cache optimization strategies
154#[derive(Debug, Clone, PartialEq, Eq)]
155pub enum CacheOptimization {
156    /// No optimization
157    None,
158    /// Spatial locality optimization
159    Spatial,
160    /// Temporal locality optimization
161    Temporal,
162    /// Combined optimization
163    Combined,
164}
165
166/// Tensor network representation
167#[derive(Debug)]
168pub struct TensorNetwork {
169    /// Network tensors
170    pub tensors: Vec<Tensor>,
171    /// Bond dimensions
172    pub bond_dimensions: HashMap<(usize, usize), usize>,
173    /// Network topology
174    pub topology: NetworkTopology,
175    /// Symmetries
176    pub symmetries: Vec<Box<dyn TensorSymmetry>>,
177    /// Canonical form
178    pub canonical_form: CanonicalForm,
179}
180
181/// Individual tensor in the network
182#[derive(Debug, Clone)]
183pub struct Tensor {
184    /// Tensor identifier
185    pub id: usize,
186    /// Tensor data
187    pub data: ArrayD<f64>,
188    /// Index labels
189    pub indices: Vec<IndexLabel>,
190    /// Tensor symmetries
191    pub symmetries: Vec<SymmetryAction>,
192    /// Compression status
193    pub compression_info: CompressionInfo,
194}
195
196/// Index label for tensor indices
197#[derive(Debug, Clone, PartialEq, Eq)]
198pub struct IndexLabel {
199    /// Index name
200    pub name: String,
201    /// Index type
202    pub index_type: IndexType,
203    /// Index dimension
204    pub dimension: usize,
205    /// Quantum numbers (for symmetric tensors)
206    pub quantum_numbers: Vec<i32>,
207}
208
209/// Types of tensor indices
210#[derive(Debug, Clone, PartialEq, Eq)]
211pub enum IndexType {
212    /// Physical index
213    Physical,
214    /// Virtual bond index
215    Virtual,
216    /// Auxiliary index
217    Auxiliary,
218    /// Time index
219    Time,
220}
221
222/// Symmetry action on tensors
223#[derive(Debug, Clone)]
224pub struct SymmetryAction {
225    /// Symmetry type
226    pub symmetry_type: SymmetryType,
227    /// Action matrix
228    pub action_matrix: Array2<f64>,
229    /// Quantum numbers
230    pub quantum_numbers: Vec<i32>,
231}
232
233/// Types of symmetries
234#[derive(Debug, Clone, PartialEq, Eq)]
235pub enum SymmetryType {
236    /// U(1) symmetry
237    U1,
238    /// Z2 symmetry
239    Z2,
240    /// SU(2) symmetry
241    SU2,
242    /// Translation symmetry
243    Translation,
244    /// Reflection symmetry
245    Reflection,
246    /// Custom symmetry
247    Custom { name: String },
248}
249
250/// Compression information
251#[derive(Debug, Clone)]
252pub struct CompressionInfo {
253    /// Original bond dimension
254    pub original_dimension: usize,
255    /// Compressed bond dimension
256    pub compressed_dimension: usize,
257    /// Compression ratio
258    pub compression_ratio: f64,
259    /// Truncation error
260    pub truncation_error: f64,
261    /// Compression method used
262    pub method: CompressionMethod,
263}
264
265/// Compression methods
266#[derive(Debug, Clone, PartialEq, Eq)]
267pub enum CompressionMethod {
268    /// Singular Value Decomposition
269    SVD,
270    /// QR decomposition
271    QR,
272    /// Randomized SVD
273    RandomizedSVD,
274    /// Tensor Train decomposition
275    TensorTrain,
276    /// Tucker decomposition
277    Tucker,
278    /// CANDECOMP/PARAFAC
279    CP,
280}
281
282/// Network topology
283#[derive(Debug, Clone)]
284pub struct NetworkTopology {
285    /// Adjacency matrix
286    pub adjacency: Array2<bool>,
287    /// Network type
288    pub topology_type: TopologyType,
289    /// Connectivity graph
290    pub connectivity: ConnectivityGraph,
291    /// Boundary conditions
292    pub boundary_conditions: BoundaryConditions,
293}
294
295/// Types of network topologies
296#[derive(Debug, Clone, PartialEq, Eq)]
297pub enum TopologyType {
298    /// Chain topology
299    Chain,
300    /// Ladder topology
301    Ladder,
302    /// Square lattice
303    SquareLattice,
304    /// Triangular lattice
305    TriangularLattice,
306    /// Hexagonal lattice
307    HexagonalLattice,
308    /// Tree topology
309    Tree,
310    /// Complete graph
311    CompleteGraph,
312    /// Custom topology
313    Custom,
314}
315
316/// Connectivity graph
317#[derive(Debug, Clone)]
318pub struct ConnectivityGraph {
319    /// Nodes
320    pub nodes: Vec<GraphNode>,
321    /// Edges
322    pub edges: Vec<GraphEdge>,
323    /// Coordination numbers
324    pub coordination_numbers: Array1<usize>,
325    /// Graph diameter
326    pub diameter: usize,
327}
328
329/// Graph node
330#[derive(Debug, Clone)]
331pub struct GraphNode {
332    /// Node identifier
333    pub id: usize,
334    /// Spatial coordinates
335    pub coordinates: Vec<f64>,
336    /// Node type
337    pub node_type: NodeType,
338    /// Associated tensor
339    pub tensor_id: usize,
340}
341
342/// Node types
343#[derive(Debug, Clone, PartialEq, Eq)]
344pub enum NodeType {
345    /// Physical site
346    Physical,
347    /// Virtual bond
348    Virtual,
349    /// Auxiliary node
350    Auxiliary,
351}
352
353/// Graph edge
354#[derive(Debug, Clone)]
355pub struct GraphEdge {
356    /// Edge identifier
357    pub id: usize,
358    /// Connected nodes
359    pub nodes: (usize, usize),
360    /// Edge weight
361    pub weight: f64,
362    /// Bond dimension
363    pub bond_dimension: usize,
364}
365
366/// Boundary conditions
367#[derive(Debug, Clone, PartialEq)]
368pub enum BoundaryConditions {
369    /// Open boundary conditions
370    Open,
371    /// Periodic boundary conditions
372    Periodic,
373    /// Mixed boundary conditions
374    Mixed { open_directions: Vec<usize> },
375    /// Twisted boundary conditions
376    Twisted { twist_angles: Vec<f64> },
377}
378
379/// Tensor symmetry trait
380pub trait TensorSymmetry: Send + Sync + std::fmt::Debug {
381    /// Apply symmetry transformation
382    fn apply_symmetry(&self, tensor: &Tensor) -> Result<Tensor, TensorNetworkError>;
383
384    /// Check if tensor respects symmetry
385    fn check_symmetry(&self, tensor: &Tensor) -> bool;
386
387    /// Get symmetry quantum numbers
388    fn get_quantum_numbers(&self) -> Vec<i32>;
389
390    /// Get symmetry name
391    fn get_symmetry_name(&self) -> &str;
392}
393
394/// Canonical forms for tensor networks
395#[derive(Debug, Clone, PartialEq, Eq)]
396pub enum CanonicalForm {
397    /// Left canonical form
398    LeftCanonical,
399    /// Right canonical form
400    RightCanonical,
401    /// Mixed canonical form
402    MixedCanonical { orthogonality_center: usize },
403    /// Not canonical
404    NotCanonical,
405}
406
407/// Tensor optimization algorithms
408#[derive(Debug)]
409pub struct TensorOptimization {
410    /// Optimization configuration
411    pub config: OptimizationConfig,
412    /// Available algorithms
413    pub algorithms: Vec<Box<dyn TensorOptimizationAlgorithm>>,
414    /// Convergence monitors
415    pub convergence_monitors: Vec<Box<dyn ConvergenceMonitor>>,
416    /// Performance trackers
417    pub performance_trackers: Vec<Box<dyn PerformanceTracker>>,
418}
419
420/// Optimization configuration
421#[derive(Debug, Clone)]
422pub struct OptimizationConfig {
423    /// Optimization algorithm
424    pub algorithm: OptimizationAlgorithm,
425    /// Maximum iterations
426    pub max_iterations: usize,
427    /// Convergence tolerance
428    pub tolerance: f64,
429    /// Learning rate
430    pub learning_rate: f64,
431    /// Regularization parameters
432    pub regularization: RegularizationConfig,
433    /// Line search parameters
434    pub line_search: LineSearchConfig,
435}
436
437/// Optimization algorithms
438#[derive(Debug, Clone, PartialEq, Eq)]
439pub enum OptimizationAlgorithm {
440    /// Density Matrix Renormalization Group
441    DMRG,
442    /// Time Evolving Block Decimation
443    TEBD,
444    /// Variational Matrix Product State
445    VMPS,
446    /// Alternating Least Squares
447    ALS,
448    /// Gradient descent
449    GradientDescent,
450    /// Conjugate gradient
451    ConjugateGradient,
452    /// L-BFGS
453    LBFGS,
454    /// Trust region methods
455    TrustRegion,
456}
457
458/// Regularization configuration
459#[derive(Debug, Clone)]
460pub struct RegularizationConfig {
461    /// L1 regularization strength
462    pub l1_strength: f64,
463    /// L2 regularization strength
464    pub l2_strength: f64,
465    /// Bond dimension penalty
466    pub bond_dimension_penalty: f64,
467    /// Entanglement entropy regularization
468    pub entropy_regularization: f64,
469}
470
471/// Line search configuration
472#[derive(Debug, Clone)]
473pub struct LineSearchConfig {
474    /// Line search method
475    pub method: LineSearchMethod,
476    /// Maximum step size
477    pub max_step_size: f64,
478    /// Backtracking parameters
479    pub backtracking_params: (f64, f64),
480    /// Wolfe conditions
481    pub wolfe_conditions: bool,
482}
483
484/// Line search methods
485#[derive(Debug, Clone, PartialEq, Eq)]
486pub enum LineSearchMethod {
487    /// Backtracking line search
488    Backtracking,
489    /// Wolfe line search
490    Wolfe,
491    /// Exact line search
492    Exact,
493    /// No line search
494    None,
495}
496
497/// Tensor optimization algorithm trait
498pub trait TensorOptimizationAlgorithm: Send + Sync + std::fmt::Debug {
499    /// Optimize tensor network
500    fn optimize(
501        &self,
502        network: &mut TensorNetwork,
503        target: &Tensor,
504    ) -> Result<OptimizationResult, TensorNetworkError>;
505
506    /// Get algorithm name
507    fn get_algorithm_name(&self) -> &str;
508
509    /// Get algorithm parameters
510    fn get_parameters(&self) -> HashMap<String, f64>;
511}
512
513/// Optimization result
514#[derive(Debug, Clone)]
515pub struct OptimizationResult {
516    /// Final energy/objective value
517    pub final_energy: f64,
518    /// Number of iterations
519    pub iterations: usize,
520    /// Convergence achieved
521    pub converged: bool,
522    /// Final gradient norm
523    pub gradient_norm: f64,
524    /// Optimization time
525    pub optimization_time: f64,
526    /// Memory usage
527    pub memory_usage: f64,
528}
529
530/// Convergence monitor trait
531pub trait ConvergenceMonitor: Send + Sync + std::fmt::Debug {
532    /// Check convergence
533    fn check_convergence(&self, iteration: usize, energy: f64, gradient_norm: f64) -> bool;
534
535    /// Get monitor name
536    fn get_monitor_name(&self) -> &str;
537}
538
539/// Performance tracker trait
540pub trait PerformanceTracker: Send + Sync + std::fmt::Debug {
541    /// Track performance metrics
542    fn track_performance(&self, iteration: usize, metrics: &TensorNetworkMetrics);
543
544    /// Get tracker name
545    fn get_tracker_name(&self) -> &str;
546}
547
548/// Tensor compression algorithms
549#[derive(Debug)]
550pub struct TensorCompression {
551    /// Compression configuration
552    pub config: CompressionConfig,
553    /// Available compression methods
554    pub methods: Vec<Box<dyn CompressionAlgorithm>>,
555    /// Quality assessors
556    pub quality_assessors: Vec<Box<dyn CompressionQualityAssessor>>,
557}
558
559/// Compression configuration
560#[derive(Debug, Clone)]
561pub struct CompressionConfig {
562    /// Target compression ratio
563    pub target_compression_ratio: f64,
564    /// Maximum allowed error
565    pub max_error: f64,
566    /// Compression method
567    pub method: CompressionMethod,
568    /// Adaptive compression
569    pub adaptive_compression: bool,
570    /// Quality control
571    pub quality_control: QualityControlConfig,
572}
573
574/// Quality control configuration
575#[derive(Debug, Clone)]
576pub struct QualityControlConfig {
577    /// Error tolerance
578    pub error_tolerance: f64,
579    /// Quality metrics
580    pub quality_metrics: Vec<QualityMetric>,
581    /// Validation frequency
582    pub validation_frequency: usize,
583    /// Recovery strategies
584    pub recovery_strategies: Vec<RecoveryStrategy>,
585}
586
587/// Quality metrics for compression
588#[derive(Debug, Clone, PartialEq, Eq)]
589pub enum QualityMetric {
590    /// Relative error
591    RelativeError,
592    /// Spectral norm error
593    SpectralNormError,
594    /// Frobenius norm error
595    FrobeniusNormError,
596    /// Information loss
597    InformationLoss,
598    /// Entanglement preservation
599    EntanglementPreservation,
600}
601
602/// Recovery strategies
603#[derive(Debug, Clone, PartialEq, Eq)]
604pub enum RecoveryStrategy {
605    /// Increase bond dimension
606    IncreaseBondDimension,
607    /// Switch compression method
608    SwitchMethod,
609    /// Adaptive refinement
610    AdaptiveRefinement,
611    /// Rollback to previous state
612    Rollback,
613}
614
615/// Compression algorithm trait
616pub trait CompressionAlgorithm: Send + Sync + std::fmt::Debug {
617    /// Compress tensor
618    fn compress(
619        &self,
620        tensor: &Tensor,
621        target_dimension: usize,
622    ) -> Result<Tensor, TensorNetworkError>;
623
624    /// Get compression method name
625    fn get_method_name(&self) -> &str;
626
627    /// Estimate compression quality
628    fn estimate_quality(&self, original: &Tensor, compressed: &Tensor) -> f64;
629}
630
631/// Compression quality assessor trait
632pub trait CompressionQualityAssessor: Send + Sync + std::fmt::Debug {
633    /// Assess compression quality
634    fn assess_quality(&self, original: &Tensor, compressed: &Tensor) -> QualityAssessment;
635
636    /// Get assessor name
637    fn get_assessor_name(&self) -> &str;
638}
639
640/// Quality assessment result
641#[derive(Debug, Clone)]
642pub struct QualityAssessment {
643    /// Overall quality score
644    pub overall_score: f64,
645    /// Individual metric scores
646    pub metric_scores: HashMap<QualityMetric, f64>,
647    /// Quality rating
648    pub rating: QualityRating,
649    /// Recommendations
650    pub recommendations: Vec<String>,
651}
652
653/// Quality ratings
654#[derive(Debug, Clone, PartialEq, Eq)]
655pub enum QualityRating {
656    /// Excellent quality
657    Excellent,
658    /// Good quality
659    Good,
660    /// Fair quality
661    Fair,
662    /// Poor quality
663    Poor,
664    /// Unacceptable quality
665    Unacceptable,
666}
667
668/// Tensor network performance metrics
669#[derive(Debug, Clone)]
670pub struct TensorNetworkMetrics {
671    /// Compression efficiency
672    pub compression_efficiency: f64,
673    /// Optimization convergence rate
674    pub convergence_rate: f64,
675    /// Memory usage efficiency
676    pub memory_efficiency: f64,
677    /// Computational speed
678    pub computational_speed: f64,
679    /// Approximation accuracy
680    pub approximation_accuracy: f64,
681    /// Entanglement measures
682    pub entanglement_measures: EntanglementMeasures,
683    /// Overall performance score
684    pub overall_performance: f64,
685}
686
687/// Entanglement measures
688#[derive(Debug, Clone)]
689pub struct EntanglementMeasures {
690    /// Entanglement entropy
691    pub entanglement_entropy: Array1<f64>,
692    /// Mutual information
693    pub mutual_information: Array2<f64>,
694    /// Entanglement spectrum
695    pub entanglement_spectrum: Vec<Array1<f64>>,
696    /// Topological entanglement entropy
697    pub topological_entropy: f64,
698}
699
700/// Tensor network errors
701#[derive(Debug, Clone)]
702pub enum TensorNetworkError {
703    /// Invalid tensor dimensions
704    InvalidDimensions(String),
705    /// Compression failed
706    CompressionFailed(String),
707    /// Optimization failed
708    OptimizationFailed(String),
709    /// Memory allocation failed
710    MemoryAllocationFailed(String),
711    /// Symmetry violation
712    SymmetryViolation(String),
713    /// Convergence failed
714    ConvergenceFailed(String),
715    /// Numerical error
716    NumericalError(String),
717}
718
719impl std::fmt::Display for TensorNetworkError {
720    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
721        match self {
722            Self::InvalidDimensions(msg) => write!(f, "Invalid dimensions: {msg}"),
723            Self::CompressionFailed(msg) => write!(f, "Compression failed: {msg}"),
724            Self::OptimizationFailed(msg) => {
725                write!(f, "Optimization failed: {msg}")
726            }
727            Self::MemoryAllocationFailed(msg) => {
728                write!(f, "Memory allocation failed: {msg}")
729            }
730            Self::SymmetryViolation(msg) => write!(f, "Symmetry violation: {msg}"),
731            Self::ConvergenceFailed(msg) => write!(f, "Convergence failed: {msg}"),
732            Self::NumericalError(msg) => write!(f, "Numerical error: {msg}"),
733        }
734    }
735}
736
737impl std::error::Error for TensorNetworkError {}
738
739impl TensorNetworkSampler {
740    /// Create new tensor network sampler
741    pub fn new(config: TensorNetworkConfig) -> Self {
742        Self {
743            tensor_network: TensorNetwork::new(&config),
744            optimization: TensorOptimization::new(),
745            compression: TensorCompression::new(),
746            metrics: TensorNetworkMetrics::default(),
747            config,
748        }
749    }
750
751    /// Sample from tensor network
752    pub fn sample(
753        &mut self,
754        hamiltonian: &ArrayD<f64>,
755        num_samples: usize,
756    ) -> Result<Vec<SampleResult>, TensorNetworkError> {
757        println!("Starting tensor network sampling with {num_samples} samples");
758
759        // Step 1: Initialize tensor network from Hamiltonian
760        self.initialize_from_hamiltonian(hamiltonian)?;
761
762        // Step 2: Optimize tensor network
763        let optimization_result = self.optimize_network()?;
764
765        // Step 3: Compress tensor network if needed
766        self.compress_network()?;
767
768        // Step 4: Generate samples
769        let samples = self.generate_samples(num_samples)?;
770
771        // Step 5: Update metrics
772        self.update_metrics(&optimization_result);
773
774        println!("Tensor network sampling completed");
775        println!(
776            "Compression efficiency: {:.4}",
777            self.metrics.compression_efficiency
778        );
779        println!(
780            "Approximation accuracy: {:.4}",
781            self.metrics.approximation_accuracy
782        );
783
784        Ok(samples)
785    }
786
787    /// Initialize tensor network from Hamiltonian
788    fn initialize_from_hamiltonian(
789        &mut self,
790        hamiltonian: &ArrayD<f64>,
791    ) -> Result<(), TensorNetworkError> {
792        match &self.config.network_type {
793            TensorNetworkType::MPS { bond_dimension } => {
794                self.initialize_mps(hamiltonian, *bond_dimension)?;
795            }
796            TensorNetworkType::PEPS {
797                bond_dimension,
798                lattice_shape,
799            } => {
800                self.initialize_peps(hamiltonian, *bond_dimension, *lattice_shape)?;
801            }
802            TensorNetworkType::MERA {
803                layers,
804                branching_factor,
805            } => {
806                self.initialize_mera(hamiltonian, *layers, *branching_factor)?;
807            }
808            _ => {
809                return Err(TensorNetworkError::InvalidDimensions(
810                    "Unsupported network type".to_string(),
811                ));
812            }
813        }
814
815        Ok(())
816    }
817
818    /// Initialize Matrix Product State
819    fn initialize_mps(
820        &mut self,
821        hamiltonian: &ArrayD<f64>,
822        bond_dimension: usize,
823    ) -> Result<(), TensorNetworkError> {
824        let num_sites = hamiltonian.shape()[0];
825        let mut tensors = Vec::new();
826
827        // Create MPS tensors
828        for i in 0..num_sites {
829            let left_dim = if i == 0 {
830                1
831            } else {
832                bond_dimension.min(2_usize.pow(i as u32))
833            };
834            let right_dim = if i == num_sites - 1 {
835                1
836            } else {
837                bond_dimension.min(2_usize.pow((num_sites - i - 1) as u32))
838            };
839            let physical_dim = 2; // Assuming spin-1/2
840
841            let shape = vec![left_dim, physical_dim, right_dim];
842            let mut rng = thread_rng();
843            let data = ArrayD::from_shape_fn(shape.clone(), |_| rng.gen_range(-0.1..0.1));
844
845            let tensor = Tensor {
846                id: i,
847                data,
848                indices: vec![
849                    IndexLabel {
850                        name: format!("left_{i}"),
851                        index_type: IndexType::Virtual,
852                        dimension: left_dim,
853                        quantum_numbers: vec![],
854                    },
855                    IndexLabel {
856                        name: format!("phys_{i}"),
857                        index_type: IndexType::Physical,
858                        dimension: physical_dim,
859                        quantum_numbers: vec![],
860                    },
861                    IndexLabel {
862                        name: format!("right_{i}"),
863                        index_type: IndexType::Virtual,
864                        dimension: right_dim,
865                        quantum_numbers: vec![],
866                    },
867                ],
868                symmetries: vec![],
869                compression_info: CompressionInfo {
870                    original_dimension: bond_dimension,
871                    compressed_dimension: bond_dimension,
872                    compression_ratio: 1.0,
873                    truncation_error: 0.0,
874                    method: CompressionMethod::SVD,
875                },
876            };
877
878            tensors.push(tensor);
879        }
880
881        self.tensor_network.tensors = tensors;
882        self.tensor_network.canonical_form = CanonicalForm::NotCanonical;
883
884        Ok(())
885    }
886
887    /// Initialize Projected Entangled Pair State
888    fn initialize_peps(
889        &mut self,
890        _hamiltonian: &ArrayD<f64>,
891        bond_dimension: usize,
892        lattice_shape: (usize, usize),
893    ) -> Result<(), TensorNetworkError> {
894        let (rows, cols) = lattice_shape;
895        let mut tensors = Vec::new();
896
897        // Create PEPS tensors
898        for i in 0..rows {
899            for j in 0..cols {
900                let tensor_id = i * cols + j;
901                let physical_dim = 2; // Assuming spin-1/2
902
903                // Determine bond dimensions for each direction
904                let up_dim = if i == 0 { 1 } else { bond_dimension };
905                let down_dim = if i == rows - 1 { 1 } else { bond_dimension };
906                let left_dim = if j == 0 { 1 } else { bond_dimension };
907                let right_dim = if j == cols - 1 { 1 } else { bond_dimension };
908
909                let shape = vec![up_dim, down_dim, left_dim, right_dim, physical_dim];
910                let mut rng = thread_rng();
911                let data = ArrayD::from_shape_fn(shape.clone(), |_| rng.gen_range(-0.1..0.1));
912
913                let tensor = Tensor {
914                    id: tensor_id,
915                    data,
916                    indices: vec![
917                        IndexLabel {
918                            name: format!("up_{i}_{j}"),
919                            index_type: IndexType::Virtual,
920                            dimension: up_dim,
921                            quantum_numbers: vec![],
922                        },
923                        IndexLabel {
924                            name: format!("down_{i}_{j}"),
925                            index_type: IndexType::Virtual,
926                            dimension: down_dim,
927                            quantum_numbers: vec![],
928                        },
929                        IndexLabel {
930                            name: format!("left_{i}_{j}"),
931                            index_type: IndexType::Virtual,
932                            dimension: left_dim,
933                            quantum_numbers: vec![],
934                        },
935                        IndexLabel {
936                            name: format!("right_{i}_{j}"),
937                            index_type: IndexType::Virtual,
938                            dimension: right_dim,
939                            quantum_numbers: vec![],
940                        },
941                        IndexLabel {
942                            name: format!("phys_{i}_{j}"),
943                            index_type: IndexType::Physical,
944                            dimension: physical_dim,
945                            quantum_numbers: vec![],
946                        },
947                    ],
948                    symmetries: vec![],
949                    compression_info: CompressionInfo {
950                        original_dimension: bond_dimension,
951                        compressed_dimension: bond_dimension,
952                        compression_ratio: 1.0,
953                        truncation_error: 0.0,
954                        method: CompressionMethod::SVD,
955                    },
956                };
957
958                tensors.push(tensor);
959            }
960        }
961
962        self.tensor_network.tensors = tensors;
963        self.tensor_network.canonical_form = CanonicalForm::NotCanonical;
964
965        Ok(())
966    }
967
968    /// Initialize Multi-scale Entanglement Renormalization Ansatz
969    fn initialize_mera(
970        &mut self,
971        hamiltonian: &ArrayD<f64>,
972        layers: usize,
973        branching_factor: usize,
974    ) -> Result<(), TensorNetworkError> {
975        let num_sites = hamiltonian.shape()[0];
976        let mut tensors = Vec::new();
977
978        // Create MERA tensors layer by layer
979        let mut current_sites = num_sites;
980
981        for layer in 0..layers {
982            // Disentanglers
983            for i in (0..current_sites).step_by(2) {
984                let tensor_id = tensors.len();
985                let shape = vec![2, 2, 2, 2]; // 2 inputs, 2 outputs
986                let mut rng = thread_rng();
987                let data = ArrayD::from_shape_fn(shape.clone(), |_| rng.gen_range(-0.1..0.1));
988
989                let tensor = Tensor {
990                    id: tensor_id,
991                    data,
992                    indices: vec![
993                        IndexLabel {
994                            name: format!("dis_in1_{layer}_{i}"),
995                            index_type: IndexType::Virtual,
996                            dimension: 2,
997                            quantum_numbers: vec![],
998                        },
999                        IndexLabel {
1000                            name: format!("dis_in2_{layer}_{i}"),
1001                            index_type: IndexType::Virtual,
1002                            dimension: 2,
1003                            quantum_numbers: vec![],
1004                        },
1005                        IndexLabel {
1006                            name: format!("dis_out1_{layer}_{i}"),
1007                            index_type: IndexType::Virtual,
1008                            dimension: 2,
1009                            quantum_numbers: vec![],
1010                        },
1011                        IndexLabel {
1012                            name: format!("dis_out2_{layer}_{i}"),
1013                            index_type: IndexType::Virtual,
1014                            dimension: 2,
1015                            quantum_numbers: vec![],
1016                        },
1017                    ],
1018                    symmetries: vec![],
1019                    compression_info: CompressionInfo {
1020                        original_dimension: 2,
1021                        compressed_dimension: 2,
1022                        compression_ratio: 1.0,
1023                        truncation_error: 0.0,
1024                        method: CompressionMethod::SVD,
1025                    },
1026                };
1027
1028                tensors.push(tensor);
1029            }
1030
1031            // Isometries
1032            current_sites /= branching_factor;
1033            for i in 0..current_sites {
1034                let tensor_id = tensors.len();
1035                let shape = vec![2, 2, 2]; // 2 inputs, 1 output (coarse-grained)
1036                let mut rng = thread_rng();
1037                let data = ArrayD::from_shape_fn(shape.clone(), |_| rng.gen_range(-0.1..0.1));
1038
1039                let tensor = Tensor {
1040                    id: tensor_id,
1041                    data,
1042                    indices: vec![
1043                        IndexLabel {
1044                            name: format!("iso_in1_{layer}_{i}"),
1045                            index_type: IndexType::Virtual,
1046                            dimension: 2,
1047                            quantum_numbers: vec![],
1048                        },
1049                        IndexLabel {
1050                            name: format!("iso_in2_{layer}_{i}"),
1051                            index_type: IndexType::Virtual,
1052                            dimension: 2,
1053                            quantum_numbers: vec![],
1054                        },
1055                        IndexLabel {
1056                            name: format!("iso_out_{layer}_{i}"),
1057                            index_type: IndexType::Virtual,
1058                            dimension: 2,
1059                            quantum_numbers: vec![],
1060                        },
1061                    ],
1062                    symmetries: vec![],
1063                    compression_info: CompressionInfo {
1064                        original_dimension: 2,
1065                        compressed_dimension: 2,
1066                        compression_ratio: 1.0,
1067                        truncation_error: 0.0,
1068                        method: CompressionMethod::SVD,
1069                    },
1070                };
1071
1072                tensors.push(tensor);
1073            }
1074        }
1075
1076        self.tensor_network.tensors = tensors;
1077        self.tensor_network.canonical_form = CanonicalForm::NotCanonical;
1078
1079        Ok(())
1080    }
1081
1082    /// Optimize tensor network
1083    fn optimize_network(&mut self) -> Result<OptimizationResult, TensorNetworkError> {
1084        println!("Optimizing tensor network...");
1085
1086        let mut energy = f64::INFINITY;
1087        let mut converged = false;
1088        let start_time = std::time::Instant::now();
1089
1090        for iteration in 0..self.config.num_sweeps {
1091            let old_energy = energy;
1092
1093            // Perform optimization sweep
1094            energy = self.perform_optimization_sweep()?;
1095
1096            // Check convergence
1097            if (old_energy - energy).abs() < self.config.convergence_tolerance {
1098                converged = true;
1099                println!("Optimization converged at iteration {iteration}");
1100                break;
1101            }
1102
1103            if iteration % 10 == 0 {
1104                println!("Iteration {iteration}: Energy = {energy:.8}");
1105            }
1106        }
1107
1108        let optimization_time = start_time.elapsed().as_secs_f64();
1109
1110        Ok(OptimizationResult {
1111            final_energy: energy,
1112            iterations: self.config.num_sweeps,
1113            converged,
1114            gradient_norm: 0.01, // Placeholder
1115            optimization_time,
1116            memory_usage: self.estimate_memory_usage(),
1117        })
1118    }
1119
1120    /// Perform one optimization sweep
1121    fn perform_optimization_sweep(&mut self) -> Result<f64, TensorNetworkError> {
1122        match &self.config.network_type {
1123            TensorNetworkType::MPS { .. } => self.sweep_mps(),
1124            TensorNetworkType::PEPS { .. } => self.sweep_peps(),
1125            TensorNetworkType::MERA { .. } => self.sweep_mera(),
1126            _ => Ok(0.0), // Placeholder
1127        }
1128    }
1129
1130    /// Sweep optimization for MPS
1131    fn sweep_mps(&mut self) -> Result<f64, TensorNetworkError> {
1132        let num_sites = self.tensor_network.tensors.len();
1133        let mut total_energy = 0.0;
1134
1135        // Right-to-left sweep
1136        for i in (0..num_sites).rev() {
1137            let local_energy = self.optimize_mps_tensor(i)?;
1138            total_energy += local_energy;
1139        }
1140
1141        // Left-to-right sweep
1142        for i in 0..num_sites {
1143            let local_energy = self.optimize_mps_tensor(i)?;
1144            total_energy += local_energy;
1145        }
1146
1147        Ok(total_energy / (2.0 * num_sites as f64))
1148    }
1149
1150    /// Optimize single MPS tensor
1151    fn optimize_mps_tensor(&mut self, site: usize) -> Result<f64, TensorNetworkError> {
1152        // Simplified tensor optimization
1153        if site >= self.tensor_network.tensors.len() {
1154            return Ok(0.0);
1155        }
1156
1157        // Add small random perturbation
1158        let mut rng = thread_rng();
1159        let perturbation_strength = 0.01;
1160
1161        for value in &mut self.tensor_network.tensors[site].data {
1162            *value += rng.gen_range(-perturbation_strength..perturbation_strength);
1163        }
1164
1165        // Return mock energy
1166        Ok(rng.gen_range(-1.0..0.0))
1167    }
1168
1169    /// Sweep optimization for PEPS
1170    fn sweep_peps(&mut self) -> Result<f64, TensorNetworkError> {
1171        let num_tensors = self.tensor_network.tensors.len();
1172        let mut total_energy = 0.0;
1173
1174        // Optimize each tensor
1175        for i in 0..num_tensors {
1176            let local_energy = self.optimize_peps_tensor(i)?;
1177            total_energy += local_energy;
1178        }
1179
1180        Ok(total_energy / num_tensors as f64)
1181    }
1182
1183    /// Optimize single PEPS tensor
1184    fn optimize_peps_tensor(&self, tensor_id: usize) -> Result<f64, TensorNetworkError> {
1185        // Simplified PEPS tensor optimization
1186        if tensor_id >= self.tensor_network.tensors.len() {
1187            return Ok(0.0);
1188        }
1189
1190        // Mock optimization
1191        let mut rng = thread_rng();
1192        Ok(rng.gen_range(-1.0..0.0))
1193    }
1194
1195    /// Sweep optimization for MERA
1196    fn sweep_mera(&mut self) -> Result<f64, TensorNetworkError> {
1197        let num_tensors = self.tensor_network.tensors.len();
1198        let mut total_energy = 0.0;
1199
1200        // Optimize each tensor
1201        for i in 0..num_tensors {
1202            let local_energy = self.optimize_mera_tensor(i)?;
1203            total_energy += local_energy;
1204        }
1205
1206        Ok(total_energy / num_tensors as f64)
1207    }
1208
1209    /// Optimize single MERA tensor
1210    fn optimize_mera_tensor(&self, tensor_id: usize) -> Result<f64, TensorNetworkError> {
1211        // Simplified MERA tensor optimization
1212        if tensor_id >= self.tensor_network.tensors.len() {
1213            return Ok(0.0);
1214        }
1215
1216        // Mock optimization
1217        let mut rng = thread_rng();
1218        Ok(rng.gen_range(-1.0..0.0))
1219    }
1220
1221    /// Compress tensor network
1222    fn compress_network(&mut self) -> Result<(), TensorNetworkError> {
1223        if !self.needs_compression() {
1224            return Ok(());
1225        }
1226
1227        println!("Compressing tensor network...");
1228
1229        let indices_to_compress: Vec<usize> = self
1230            .tensor_network
1231            .tensors
1232            .iter()
1233            .enumerate()
1234            .filter(|(_, tensor)| {
1235                tensor.compression_info.compressed_dimension > self.config.max_bond_dimension
1236            })
1237            .map(|(i, _)| i)
1238            .collect();
1239
1240        for index in indices_to_compress {
1241            // Clone the tensor for compression
1242            if let Some(tensor) = self.tensor_network.tensors.get(index) {
1243                let mut tensor_copy = tensor.clone();
1244                self.compress_tensor(&mut tensor_copy)?;
1245                // Update the tensor in the network
1246                if let Some(network_tensor) = self.tensor_network.tensors.get_mut(index) {
1247                    *network_tensor = tensor_copy;
1248                }
1249            }
1250        }
1251
1252        Ok(())
1253    }
1254
1255    /// Check if compression is needed
1256    fn needs_compression(&self) -> bool {
1257        self.tensor_network.tensors.iter().any(|tensor| {
1258            tensor.compression_info.compressed_dimension > self.config.max_bond_dimension
1259        })
1260    }
1261
1262    /// Compress individual tensor
1263    fn compress_tensor(&self, tensor: &mut Tensor) -> Result<(), TensorNetworkError> {
1264        // Simplified SVD compression
1265        let _original_size = tensor.data.len();
1266        let compression_factor = self.config.max_bond_dimension as f64
1267            / tensor.compression_info.compressed_dimension as f64;
1268
1269        if compression_factor < 1.0 {
1270            // Update compression info
1271            tensor.compression_info.original_dimension =
1272                tensor.compression_info.compressed_dimension;
1273            tensor.compression_info.compressed_dimension = self.config.max_bond_dimension;
1274            tensor.compression_info.compression_ratio = compression_factor;
1275            tensor.compression_info.truncation_error = (1.0 - compression_factor) * 0.1; // Mock error
1276            tensor.compression_info.method = CompressionMethod::SVD;
1277        }
1278
1279        Ok(())
1280    }
1281
1282    /// Generate samples from tensor network
1283    fn generate_samples(
1284        &self,
1285        num_samples: usize,
1286    ) -> Result<Vec<SampleResult>, TensorNetworkError> {
1287        let mut samples = Vec::new();
1288        let mut rng = thread_rng();
1289
1290        for _ in 0..num_samples {
1291            let sample = self.generate_single_sample(&mut rng)?;
1292            samples.push(sample);
1293        }
1294
1295        Ok(samples)
1296    }
1297
1298    /// Generate single sample
1299    fn generate_single_sample(
1300        &self,
1301        rng: &mut ThreadRng,
1302    ) -> Result<SampleResult, TensorNetworkError> {
1303        match &self.config.network_type {
1304            TensorNetworkType::MPS { .. } => self.sample_from_mps(rng),
1305            TensorNetworkType::PEPS { .. } => self.sample_from_peps(rng),
1306            TensorNetworkType::MERA { .. } => self.sample_from_mera(rng),
1307            _ => self.sample_default(rng),
1308        }
1309    }
1310
1311    /// Sample from MPS
1312    fn sample_from_mps(&self, rng: &mut ThreadRng) -> Result<SampleResult, TensorNetworkError> {
1313        let num_sites = self.tensor_network.tensors.len();
1314        let mut sample = Vec::new();
1315
1316        // Sequential sampling for MPS
1317        for _i in 0..num_sites {
1318            let local_sample = i32::from(rng.gen::<f64>() >= 0.5);
1319            sample.push(local_sample);
1320        }
1321
1322        let energy = self.calculate_sample_energy(&sample)?;
1323
1324        Ok(SampleResult {
1325            assignments: sample
1326                .into_iter()
1327                .enumerate()
1328                .map(|(i, val)| (format!("x{i}"), val != 0))
1329                .collect(),
1330            energy,
1331            occurrences: 1,
1332        })
1333    }
1334
1335    /// Sample from PEPS
1336    fn sample_from_peps(&self, rng: &mut ThreadRng) -> Result<SampleResult, TensorNetworkError> {
1337        let num_tensors = self.tensor_network.tensors.len();
1338        let mut sample = Vec::new();
1339
1340        // Parallel sampling for PEPS (simplified)
1341        for _ in 0..num_tensors {
1342            let local_sample = i32::from(rng.gen::<f64>() >= 0.5);
1343            sample.push(local_sample);
1344        }
1345
1346        let energy = self.calculate_sample_energy(&sample)?;
1347
1348        Ok(SampleResult {
1349            assignments: sample
1350                .into_iter()
1351                .enumerate()
1352                .map(|(i, val)| (format!("x{i}"), val != 0))
1353                .collect(),
1354            energy,
1355            occurrences: 1,
1356        })
1357    }
1358
1359    /// Sample from MERA
1360    fn sample_from_mera(&self, rng: &mut ThreadRng) -> Result<SampleResult, TensorNetworkError> {
1361        // Hierarchical sampling for MERA (simplified)
1362        let num_sites = 16; // Mock number of sites
1363        let mut sample = Vec::new();
1364
1365        for _ in 0..num_sites {
1366            let local_sample = i32::from(rng.gen::<f64>() >= 0.5);
1367            sample.push(local_sample);
1368        }
1369
1370        let energy = self.calculate_sample_energy(&sample)?;
1371
1372        Ok(SampleResult {
1373            assignments: sample
1374                .into_iter()
1375                .enumerate()
1376                .map(|(i, val)| (format!("x{i}"), val != 0))
1377                .collect(),
1378            energy,
1379            occurrences: 1,
1380        })
1381    }
1382
1383    /// Default sampling method
1384    fn sample_default(&self, rng: &mut ThreadRng) -> Result<SampleResult, TensorNetworkError> {
1385        let num_sites = 10; // Default
1386        let mut sample = Vec::new();
1387
1388        for _ in 0..num_sites {
1389            let local_sample = i32::from(rng.gen::<f64>() >= 0.5);
1390            sample.push(local_sample);
1391        }
1392
1393        let energy = self.calculate_sample_energy(&sample)?;
1394
1395        Ok(SampleResult {
1396            assignments: sample
1397                .into_iter()
1398                .enumerate()
1399                .map(|(i, val)| (format!("x{i}"), val != 0))
1400                .collect(),
1401            energy,
1402            occurrences: 1,
1403        })
1404    }
1405
1406    /// Calculate energy of a sample
1407    fn calculate_sample_energy(&self, sample: &[i32]) -> Result<f64, TensorNetworkError> {
1408        // Simplified energy calculation
1409        let mut energy = 0.0;
1410
1411        for i in 0..sample.len() {
1412            energy += sample[i] as f64;
1413
1414            if i > 0 {
1415                energy += -(sample[i] as f64 * sample[i - 1] as f64);
1416            }
1417        }
1418
1419        Ok(energy)
1420    }
1421
1422    /// Update performance metrics
1423    fn update_metrics(&mut self, optimization_result: &OptimizationResult) {
1424        self.metrics.compression_efficiency = self.calculate_compression_efficiency();
1425        self.metrics.convergence_rate = if optimization_result.converged {
1426            1.0
1427        } else {
1428            0.5
1429        };
1430        self.metrics.memory_efficiency = 1.0 / (optimization_result.memory_usage + 1.0);
1431        self.metrics.computational_speed = 1.0 / (optimization_result.optimization_time + 1.0);
1432        self.metrics.approximation_accuracy = 1.0 - optimization_result.final_energy.abs() / 10.0;
1433
1434        // Update entanglement measures
1435        self.metrics.entanglement_measures = self.calculate_entanglement_measures();
1436
1437        // Overall performance
1438        self.metrics.overall_performance = self.metrics.approximation_accuracy.mul_add(
1439            0.2,
1440            self.metrics.computational_speed.mul_add(
1441                0.2,
1442                self.metrics.memory_efficiency.mul_add(
1443                    0.2,
1444                    self.metrics
1445                        .compression_efficiency
1446                        .mul_add(0.2, self.metrics.convergence_rate * 0.2),
1447                ),
1448            ),
1449        );
1450    }
1451
1452    /// Calculate compression efficiency
1453    fn calculate_compression_efficiency(&self) -> f64 {
1454        let mut total_compression = 0.0;
1455        let mut count = 0;
1456
1457        for tensor in &self.tensor_network.tensors {
1458            total_compression += tensor.compression_info.compression_ratio;
1459            count += 1;
1460        }
1461
1462        if count > 0 {
1463            total_compression / count as f64
1464        } else {
1465            1.0
1466        }
1467    }
1468
1469    /// Calculate entanglement measures
1470    fn calculate_entanglement_measures(&self) -> EntanglementMeasures {
1471        let num_bonds = self.tensor_network.tensors.len();
1472
1473        EntanglementMeasures {
1474            entanglement_entropy: Array1::ones(num_bonds) * 0.5,
1475            mutual_information: Array2::ones((num_bonds, num_bonds)) * 0.1,
1476            entanglement_spectrum: vec![Array1::from_vec(vec![0.7, 0.3]); num_bonds],
1477            topological_entropy: 0.1,
1478        }
1479    }
1480
1481    /// Estimate memory usage
1482    fn estimate_memory_usage(&self) -> f64 {
1483        let mut total_memory = 0.0;
1484
1485        for tensor in &self.tensor_network.tensors {
1486            total_memory += tensor.data.len() as f64 * 8.0; // 8 bytes per f64
1487        }
1488
1489        total_memory / (1024.0 * 1024.0 * 1024.0) // Convert to GB
1490    }
1491}
1492
1493impl TensorNetwork {
1494    /// Create new tensor network
1495    pub fn new(config: &TensorNetworkConfig) -> Self {
1496        Self {
1497            tensors: Vec::new(),
1498            bond_dimensions: HashMap::new(),
1499            topology: NetworkTopology::new(&config.network_type),
1500            symmetries: Vec::new(),
1501            canonical_form: CanonicalForm::NotCanonical,
1502        }
1503    }
1504}
1505
1506impl NetworkTopology {
1507    /// Create network topology
1508    pub fn new(network_type: &TensorNetworkType) -> Self {
1509        match network_type {
1510            TensorNetworkType::MPS { .. } => Self::create_chain_topology(),
1511            TensorNetworkType::PEPS { lattice_shape, .. } => {
1512                Self::create_lattice_topology(*lattice_shape)
1513            }
1514            _ => Self::create_default_topology(),
1515        }
1516    }
1517
1518    /// Create chain topology for MPS
1519    fn create_chain_topology() -> Self {
1520        Self {
1521            adjacency: {
1522                let mut adj = Array2::from_elem((10, 10), false);
1523                for i in 0..10 {
1524                    adj[(i, i)] = true;
1525                }
1526                adj
1527            }, // Default size
1528            topology_type: TopologyType::Chain,
1529            connectivity: ConnectivityGraph {
1530                nodes: Vec::new(),
1531                edges: Vec::new(),
1532                coordination_numbers: Array1::ones(10),
1533                diameter: 10,
1534            },
1535            boundary_conditions: BoundaryConditions::Open,
1536        }
1537    }
1538
1539    /// Create lattice topology for PEPS
1540    fn create_lattice_topology(lattice_shape: (usize, usize)) -> Self {
1541        let (rows, cols) = lattice_shape;
1542        let num_sites = rows * cols;
1543
1544        Self {
1545            adjacency: {
1546                let mut adj = Array2::from_elem((num_sites, num_sites), false);
1547                for i in 0..num_sites {
1548                    adj[(i, i)] = true;
1549                }
1550                adj
1551            },
1552            topology_type: TopologyType::SquareLattice,
1553            connectivity: ConnectivityGraph {
1554                nodes: Vec::new(),
1555                edges: Vec::new(),
1556                coordination_numbers: Array1::from_elem(num_sites, 4),
1557                diameter: rows + cols,
1558            },
1559            boundary_conditions: BoundaryConditions::Open,
1560        }
1561    }
1562
1563    /// Create default topology
1564    fn create_default_topology() -> Self {
1565        Self {
1566            adjacency: {
1567                let mut adj = Array2::from_elem((1, 1), false);
1568                adj[(0, 0)] = true;
1569                adj
1570            },
1571            topology_type: TopologyType::Chain,
1572            connectivity: ConnectivityGraph {
1573                nodes: Vec::new(),
1574                edges: Vec::new(),
1575                coordination_numbers: Array1::ones(1),
1576                diameter: 1,
1577            },
1578            boundary_conditions: BoundaryConditions::Open,
1579        }
1580    }
1581}
1582
1583impl Default for TensorOptimization {
1584    fn default() -> Self {
1585        Self::new()
1586    }
1587}
1588
1589impl TensorOptimization {
1590    /// Create new tensor optimization
1591    pub fn new() -> Self {
1592        Self {
1593            config: OptimizationConfig::default(),
1594            algorithms: Vec::new(),
1595            convergence_monitors: Vec::new(),
1596            performance_trackers: Vec::new(),
1597        }
1598    }
1599}
1600
1601impl Default for OptimizationConfig {
1602    fn default() -> Self {
1603        Self {
1604            algorithm: OptimizationAlgorithm::DMRG,
1605            max_iterations: 1000,
1606            tolerance: 1e-8,
1607            learning_rate: 0.01,
1608            regularization: RegularizationConfig {
1609                l1_strength: 0.0,
1610                l2_strength: 0.001,
1611                bond_dimension_penalty: 0.0,
1612                entropy_regularization: 0.0,
1613            },
1614            line_search: LineSearchConfig {
1615                method: LineSearchMethod::Backtracking,
1616                max_step_size: 1.0,
1617                backtracking_params: (0.5, 1e-4),
1618                wolfe_conditions: false,
1619            },
1620        }
1621    }
1622}
1623
1624impl Default for TensorCompression {
1625    fn default() -> Self {
1626        Self::new()
1627    }
1628}
1629
1630impl TensorCompression {
1631    /// Create new tensor compression
1632    pub fn new() -> Self {
1633        Self {
1634            config: CompressionConfig::default(),
1635            methods: Vec::new(),
1636            quality_assessors: Vec::new(),
1637        }
1638    }
1639}
1640
1641impl Default for CompressionConfig {
1642    fn default() -> Self {
1643        Self {
1644            target_compression_ratio: 0.5,
1645            max_error: 1e-6,
1646            method: CompressionMethod::SVD,
1647            adaptive_compression: true,
1648            quality_control: QualityControlConfig {
1649                error_tolerance: 1e-8,
1650                quality_metrics: vec![
1651                    QualityMetric::RelativeError,
1652                    QualityMetric::FrobeniusNormError,
1653                ],
1654                validation_frequency: 10,
1655                recovery_strategies: vec![RecoveryStrategy::IncreaseBondDimension],
1656            },
1657        }
1658    }
1659}
1660
1661impl Default for TensorNetworkMetrics {
1662    fn default() -> Self {
1663        Self {
1664            compression_efficiency: 1.0,
1665            convergence_rate: 1.0,
1666            memory_efficiency: 1.0,
1667            computational_speed: 1.0,
1668            approximation_accuracy: 1.0,
1669            entanglement_measures: EntanglementMeasures {
1670                entanglement_entropy: Array1::zeros(1),
1671                mutual_information: Array2::zeros((1, 1)),
1672                entanglement_spectrum: vec![Array1::zeros(1)],
1673                topological_entropy: 0.0,
1674            },
1675            overall_performance: 1.0,
1676        }
1677    }
1678}
1679
1680/// Create default tensor network configuration
1681pub const fn create_default_tensor_config() -> TensorNetworkConfig {
1682    TensorNetworkConfig {
1683        network_type: TensorNetworkType::MPS { bond_dimension: 64 },
1684        max_bond_dimension: 128,
1685        compression_tolerance: 1e-10,
1686        num_sweeps: 100,
1687        convergence_tolerance: 1e-8,
1688        use_gpu: false,
1689        parallel_config: ParallelConfig {
1690            num_threads: 4,
1691            distributed: false,
1692            chunk_size: 1000,
1693            load_balancing: LoadBalancingStrategy::Dynamic,
1694        },
1695        memory_config: MemoryConfig {
1696            max_memory_gb: 8.0,
1697            memory_mapping: false,
1698            gc_frequency: 100,
1699            cache_optimization: CacheOptimization::Combined,
1700        },
1701    }
1702}
1703
1704/// Create MPS-based tensor network sampler
1705pub fn create_mps_sampler(bond_dimension: usize) -> TensorNetworkSampler {
1706    let mut config = create_default_tensor_config();
1707    config.network_type = TensorNetworkType::MPS { bond_dimension };
1708    config.max_bond_dimension = bond_dimension * 2;
1709    TensorNetworkSampler::new(config)
1710}
1711
1712/// Create PEPS-based tensor network sampler
1713pub fn create_peps_sampler(
1714    bond_dimension: usize,
1715    lattice_shape: (usize, usize),
1716) -> TensorNetworkSampler {
1717    let mut config = create_default_tensor_config();
1718    config.network_type = TensorNetworkType::PEPS {
1719        bond_dimension,
1720        lattice_shape,
1721    };
1722    config.max_bond_dimension = bond_dimension * 2;
1723    TensorNetworkSampler::new(config)
1724}
1725
1726/// Create MERA-based tensor network sampler
1727pub fn create_mera_sampler(layers: usize) -> TensorNetworkSampler {
1728    let mut config = create_default_tensor_config();
1729    config.network_type = TensorNetworkType::MERA {
1730        layers,
1731        branching_factor: 2,
1732    };
1733    TensorNetworkSampler::new(config)
1734}
1735
1736// Implement Sampler trait for TensorNetworkSampler
1737impl Sampler for TensorNetworkSampler {
1738    fn run_qubo(
1739        &self,
1740        _qubo: &(
1741            scirs2_core::ndarray::Array2<f64>,
1742            std::collections::HashMap<String, usize>,
1743        ),
1744        _num_reads: usize,
1745    ) -> SamplerResult<Vec<crate::sampler::SampleResult>> {
1746        Err(SamplerError::NotImplemented(
1747            "Use run_hobo instead ".to_string(),
1748        ))
1749    }
1750
1751    fn run_hobo(
1752        &self,
1753        problem: &(
1754            scirs2_core::ndarray::ArrayD<f64>,
1755            std::collections::HashMap<String, usize>,
1756        ),
1757        num_reads: usize,
1758    ) -> SamplerResult<Vec<crate::sampler::SampleResult>> {
1759        let (hamiltonian, _var_map) = problem;
1760
1761        // Create a mutable copy for sampling
1762        let mut sampler_copy = Self::new(self.config.clone());
1763
1764        match sampler_copy.sample(hamiltonian, num_reads) {
1765            Ok(results) => Ok(results),
1766            Err(e) => Err(SamplerError::InvalidParameter(e.to_string())),
1767        }
1768    }
1769}
1770
1771#[cfg(test)]
1772mod tests {
1773    use super::*;
1774
1775    #[test]
1776    fn test_tensor_network_sampler_creation() {
1777        let sampler = create_mps_sampler(32);
1778        assert_eq!(sampler.config.max_bond_dimension, 64);
1779
1780        if let TensorNetworkType::MPS { bond_dimension } = sampler.config.network_type {
1781            assert_eq!(bond_dimension, 32);
1782        } else {
1783            panic!("Expected MPS network type ");
1784        }
1785    }
1786
1787    #[test]
1788    fn test_peps_sampler_creation() {
1789        let sampler = create_peps_sampler(16, (4, 4));
1790
1791        if let TensorNetworkType::PEPS {
1792            bond_dimension,
1793            lattice_shape,
1794        } = sampler.config.network_type
1795        {
1796            assert_eq!(bond_dimension, 16);
1797            assert_eq!(lattice_shape, (4, 4));
1798        } else {
1799            panic!("Expected PEPS network type ");
1800        }
1801    }
1802
1803    #[test]
1804    fn test_mera_sampler_creation() {
1805        let sampler = create_mera_sampler(3);
1806
1807        if let TensorNetworkType::MERA {
1808            layers,
1809            branching_factor,
1810        } = sampler.config.network_type
1811        {
1812            assert_eq!(layers, 3);
1813            assert_eq!(branching_factor, 2);
1814        } else {
1815            panic!("Expected MERA network type ");
1816        }
1817    }
1818
1819    #[test]
1820    fn test_tensor_network_topology() {
1821        let mut config = create_default_tensor_config();
1822        let topology = NetworkTopology::new(&config.network_type);
1823        assert_eq!(topology.topology_type, TopologyType::Chain);
1824    }
1825
1826    #[test]
1827    fn test_compression_config() {
1828        let mut config = CompressionConfig::default();
1829        assert_eq!(config.target_compression_ratio, 0.5);
1830        assert_eq!(config.method, CompressionMethod::SVD);
1831    }
1832}
quantrs2_tytan/tensor_network_sampler.rs

quantrs2_tytan/
tensor_network_sampler.rs
