quantrs2_sim/
qaoa_optimization.rs

1//! Quantum Approximate Optimization Algorithm (QAOA) Implementation
2//!
3//! This module provides a comprehensive implementation of QAOA for combinatorial
4//! optimization problems, including advanced problem encodings, multi-level QAOA,
5//! and hardware-aware optimizations.
6
7use scirs2_core::ndarray::{Array1, Array2};
8use scirs2_core::parallel_ops::*;
9use scirs2_core::random::prelude::*;
10use scirs2_core::Complex64;
11use serde::{Deserialize, Serialize};
12use std::collections::HashMap;
13use std::sync::{Arc, Mutex};
14use std::time::{Duration, Instant};
15
16use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
17use crate::error::Result;
18
19#[cfg(feature = "optimize")]
20use crate::optirs_integration::{OptiRSConfig, OptiRSQuantumOptimizer};
21
22/// QAOA problem types
23#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
24pub enum QAOAProblemType {
25    /// Maximum Cut problem
26    MaxCut,
27    /// Maximum Weight Independent Set
28    MaxWeightIndependentSet,
29    /// Minimum Vertex Cover
30    MinVertexCover,
31    /// Graph Coloring
32    GraphColoring,
33    /// Traveling Salesman Problem
34    TSP,
35    /// Portfolio Optimization
36    PortfolioOptimization,
37    /// Job Shop Scheduling
38    JobShopScheduling,
39    /// Boolean 3-SAT
40    Boolean3SAT,
41    /// Quadratic Unconstrained Binary Optimization
42    QUBO,
43    /// Maximum Clique
44    MaxClique,
45    /// Bin Packing
46    BinPacking,
47    /// Custom Problem
48    Custom,
49}
50
51/// Graph representation for QAOA problems
52#[derive(Debug, Clone, Serialize, Deserialize)]
53pub struct QAOAGraph {
54    /// Number of vertices
55    pub num_vertices: usize,
56    /// Adjacency matrix
57    pub adjacency_matrix: Array2<f64>,
58    /// Vertex weights
59    pub vertex_weights: Vec<f64>,
60    /// Edge weights
61    pub edge_weights: HashMap<(usize, usize), f64>,
62    /// Additional constraints
63    pub constraints: Vec<QAOAConstraint>,
64}
65
66/// QAOA constraints
67#[derive(Debug, Clone, Serialize, Deserialize)]
68pub enum QAOAConstraint {
69    /// Cardinality constraint (exactly k vertices selected)
70    Cardinality { target: usize },
71    /// Upper bound on selected vertices
72    UpperBound { max_vertices: usize },
73    /// Lower bound on selected vertices
74    LowerBound { min_vertices: usize },
75    /// Parity constraint
76    Parity { even: bool },
77    /// Custom linear constraint
78    LinearConstraint { coefficients: Vec<f64>, bound: f64 },
79}
80
81/// QAOA mixer types
82#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
83pub enum QAOAMixerType {
84    /// Standard X mixer (unconstrained)
85    Standard,
86    /// XY mixer for number conservation
87    XY,
88    /// Ring mixer for cyclic structures
89    Ring,
90    /// Grover mixer for amplitude amplification
91    Grover,
92    /// Dicke state mixer for cardinality constraints
93    Dicke,
94    /// Custom mixer with specified structure
95    Custom,
96}
97
98/// QAOA initialization strategies
99#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
100pub enum QAOAInitializationStrategy {
101    /// Uniform superposition
102    UniformSuperposition,
103    /// Warm start from classical solution
104    WarmStart,
105    /// Adiabatic initialization
106    AdiabaticStart,
107    /// Random initialization
108    Random,
109    /// Problem-specific initialization
110    ProblemSpecific,
111}
112
113/// QAOA optimization strategy
114#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
115pub enum QAOAOptimizationStrategy {
116    /// Classical optimization of angles
117    Classical,
118    /// Quantum optimization using quantum gradients
119    Quantum,
120    /// Hybrid classical-quantum optimization
121    Hybrid,
122    /// Machine learning guided optimization
123    MLGuided,
124    /// Adaptive parameter optimization
125    Adaptive,
126    /// OptiRS optimization (Adam, SGD, RMSprop, etc.) - requires "optimize" feature
127    #[cfg(feature = "optimize")]
128    OptiRS,
129}
130
131/// QAOA configuration
132#[derive(Debug, Clone)]
133pub struct QAOAConfig {
134    /// Number of QAOA layers (p)
135    pub num_layers: usize,
136    /// Mixer type
137    pub mixer_type: QAOAMixerType,
138    /// Initialization strategy
139    pub initialization: QAOAInitializationStrategy,
140    /// Optimization strategy
141    pub optimization_strategy: QAOAOptimizationStrategy,
142    /// Maximum optimization iterations
143    pub max_iterations: usize,
144    /// Convergence tolerance
145    pub convergence_tolerance: f64,
146    /// Learning rate for optimization
147    pub learning_rate: f64,
148    /// Enable multi-angle QAOA
149    pub multi_angle: bool,
150    /// Enable parameter transfer learning
151    pub parameter_transfer: bool,
152    /// Hardware-specific optimizations
153    pub hardware_aware: bool,
154    /// Shot noise for finite sampling
155    pub shots: Option<usize>,
156    /// Enable adaptive layer growth
157    pub adaptive_layers: bool,
158    /// Maximum adaptive layers
159    pub max_adaptive_layers: usize,
160}
161
162impl Default for QAOAConfig {
163    fn default() -> Self {
164        Self {
165            num_layers: 1,
166            mixer_type: QAOAMixerType::Standard,
167            initialization: QAOAInitializationStrategy::UniformSuperposition,
168            optimization_strategy: QAOAOptimizationStrategy::Classical,
169            max_iterations: 100,
170            convergence_tolerance: 1e-6,
171            learning_rate: 0.1,
172            multi_angle: false,
173            parameter_transfer: false,
174            hardware_aware: true,
175            shots: None,
176            adaptive_layers: false,
177            max_adaptive_layers: 10,
178        }
179    }
180}
181
182/// QAOA result
183#[derive(Debug, Clone, Serialize, Deserialize)]
184pub struct QAOAResult {
185    /// Optimal gamma parameters
186    pub optimal_gammas: Vec<f64>,
187    /// Optimal beta parameters
188    pub optimal_betas: Vec<f64>,
189    /// Best cost value found
190    pub best_cost: f64,
191    /// Approximation ratio
192    pub approximation_ratio: f64,
193    /// Optimization history
194    pub cost_history: Vec<f64>,
195    /// Parameter evolution
196    pub parameter_history: Vec<(Vec<f64>, Vec<f64>)>,
197    /// Final probability distribution
198    pub final_probabilities: HashMap<String, f64>,
199    /// Best solution bitstring
200    pub best_solution: String,
201    /// Solution quality metrics
202    pub solution_quality: SolutionQuality,
203    /// Optimization time
204    pub optimization_time: Duration,
205    /// Number of function evaluations
206    pub function_evaluations: usize,
207    /// Convergence information
208    pub converged: bool,
209}
210
211/// Solution quality metrics
212#[derive(Debug, Clone, Serialize, Deserialize)]
213pub struct SolutionQuality {
214    /// Feasibility (satisfies constraints)
215    pub feasible: bool,
216    /// Gap to optimal solution (if known)
217    pub optimality_gap: Option<f64>,
218    /// Solution variance across multiple runs
219    pub solution_variance: f64,
220    /// Confidence in solution
221    pub confidence: f64,
222    /// Number of constraint violations
223    pub constraint_violations: usize,
224}
225
226/// QAOA statistics
227#[derive(Debug, Clone, Serialize, Deserialize)]
228pub struct QAOAStats {
229    /// Total optimization time
230    pub total_time: Duration,
231    /// Time per layer evaluation
232    pub layer_times: Vec<Duration>,
233    /// Circuit depth per layer
234    pub circuit_depths: Vec<usize>,
235    /// Parameter sensitivity analysis
236    pub parameter_sensitivity: HashMap<String, f64>,
237    /// Quantum advantage metrics
238    pub quantum_advantage: QuantumAdvantageMetrics,
239}
240
241/// Quantum advantage analysis
242#[derive(Debug, Clone, Serialize, Deserialize)]
243pub struct QuantumAdvantageMetrics {
244    /// Classical algorithm comparison time
245    pub classical_time: Duration,
246    /// Quantum speedup factor
247    pub speedup_factor: f64,
248    /// Success probability
249    pub success_probability: f64,
250    /// Quantum volume required
251    pub quantum_volume: usize,
252}
253
254/// Multi-level QAOA configuration
255#[derive(Debug, Clone)]
256pub struct MultiLevelQAOAConfig {
257    /// Hierarchical levels
258    pub levels: Vec<QAOALevel>,
259    /// Parameter sharing between levels
260    pub parameter_sharing: bool,
261    /// Level transition criteria
262    pub transition_criteria: LevelTransitionCriteria,
263}
264
265/// QAOA level configuration
266#[derive(Debug, Clone)]
267pub struct QAOALevel {
268    /// Problem size at this level
269    pub problem_size: usize,
270    /// Number of layers
271    pub num_layers: usize,
272    /// Optimization budget
273    pub optimization_budget: usize,
274    /// Level-specific mixer
275    pub mixer_type: QAOAMixerType,
276}
277
278/// Level transition criteria
279#[derive(Debug, Clone, Copy, PartialEq, Eq)]
280pub enum LevelTransitionCriteria {
281    /// Fixed schedule
282    FixedSchedule,
283    /// Performance based
284    PerformanceBased,
285    /// Convergence based
286    ConvergenceBased,
287    /// Adaptive
288    Adaptive,
289}
290
291/// Main QAOA optimizer
292pub struct QAOAOptimizer {
293    /// Configuration
294    config: QAOAConfig,
295    /// Problem graph
296    graph: QAOAGraph,
297    /// Problem type
298    problem_type: QAOAProblemType,
299    /// Current parameters
300    gammas: Vec<f64>,
301    betas: Vec<f64>,
302    /// Best parameters found
303    best_gammas: Vec<f64>,
304    best_betas: Vec<f64>,
305    /// Best cost found
306    best_cost: f64,
307    /// Classical optimal solution (if known)
308    classical_optimum: Option<f64>,
309    /// Optimization statistics
310    stats: QAOAStats,
311    /// Parameter transfer database
312    parameter_database: Arc<Mutex<ParameterDatabase>>,
313    /// OptiRS optimizer (optional, for OptiRS strategy)
314    #[cfg(feature = "optimize")]
315    optirs_optimizer: Option<OptiRSQuantumOptimizer>,
316}
317
318/// Parameter transfer database
319#[derive(Debug, Clone)]
320pub struct ParameterDatabase {
321    /// Stored parameter sets by problem characteristics
322    pub parameters: HashMap<ProblemCharacteristics, Vec<(Vec<f64>, Vec<f64>, f64)>>,
323}
324
325/// Problem characteristics for parameter transfer
326#[derive(Debug, Clone, Hash, PartialEq, Eq)]
327pub struct ProblemCharacteristics {
328    pub problem_type: QAOAProblemType,
329    pub num_vertices: usize,
330    pub density: u32,    // Edge density * 100
331    pub regularity: u32, // Graph regularity * 100
332}
333
334impl QAOAOptimizer {
335    /// Create new QAOA optimizer
336    pub fn new(
337        config: QAOAConfig,
338        graph: QAOAGraph,
339        problem_type: QAOAProblemType,
340    ) -> Result<Self> {
341        let gammas = Self::initialize_gammas(&config, &graph)?;
342        let betas = Self::initialize_betas(&config, &graph)?;
343
344        Ok(Self {
345            config,
346            graph,
347            problem_type,
348            gammas: gammas.clone(),
349            betas: betas.clone(),
350            best_gammas: gammas,
351            best_betas: betas,
352            best_cost: f64::NEG_INFINITY,
353            classical_optimum: None,
354            stats: QAOAStats {
355                total_time: Duration::new(0, 0),
356                layer_times: Vec::new(),
357                circuit_depths: Vec::new(),
358                parameter_sensitivity: HashMap::new(),
359                quantum_advantage: QuantumAdvantageMetrics {
360                    classical_time: Duration::new(0, 0),
361                    speedup_factor: 1.0,
362                    success_probability: 0.0,
363                    quantum_volume: 0,
364                },
365            },
366            parameter_database: Arc::new(Mutex::new(ParameterDatabase {
367                parameters: HashMap::new(),
368            })),
369            #[cfg(feature = "optimize")]
370            optirs_optimizer: None,
371        })
372    }
373
374    /// Optimize QAOA parameters
375    pub fn optimize(&mut self) -> Result<QAOAResult> {
376        let start_time = Instant::now();
377        let mut cost_history = Vec::new();
378        let mut parameter_history = Vec::new();
379
380        // Initialize with transferred parameters if enabled
381        if self.config.parameter_transfer {
382            self.apply_parameter_transfer()?;
383        }
384
385        // Run classical algorithm for comparison
386        let classical_start = Instant::now();
387        let classical_result = self.solve_classically()?;
388        self.stats.quantum_advantage.classical_time = classical_start.elapsed();
389        self.classical_optimum = Some(classical_result);
390
391        // Adaptive layer optimization
392        let mut current_layers = self.config.num_layers;
393
394        for iteration in 0..self.config.max_iterations {
395            // Evaluate current parameters
396            let cost = self.evaluate_qaoa_cost(&self.gammas, &self.betas)?;
397            cost_history.push(cost);
398            parameter_history.push((self.gammas.clone(), self.betas.clone()));
399
400            // Update best solution
401            if cost > self.best_cost {
402                self.best_cost = cost;
403                self.best_gammas = self.gammas.clone();
404                self.best_betas = self.betas.clone();
405            }
406
407            // Check convergence
408            if iteration > 10 {
409                let recent_improvement = cost_history[iteration] - cost_history[iteration - 10];
410                if recent_improvement.abs() < self.config.convergence_tolerance {
411                    break;
412                }
413            }
414
415            // Optimize parameters using selected strategy
416            match self.config.optimization_strategy {
417                QAOAOptimizationStrategy::Classical => {
418                    self.classical_parameter_optimization()?;
419                }
420                QAOAOptimizationStrategy::Quantum => {
421                    self.quantum_parameter_optimization()?;
422                }
423                QAOAOptimizationStrategy::Hybrid => {
424                    self.hybrid_parameter_optimization()?;
425                }
426                QAOAOptimizationStrategy::MLGuided => {
427                    self.ml_guided_optimization()?;
428                }
429                QAOAOptimizationStrategy::Adaptive => {
430                    self.adaptive_parameter_optimization(&cost_history)?;
431                }
432                #[cfg(feature = "optimize")]
433                QAOAOptimizationStrategy::OptiRS => {
434                    self.optirs_parameter_optimization()?;
435                }
436            }
437
438            // Adaptive layer growth
439            if self.config.adaptive_layers
440                && iteration % 20 == 19
441                && self.should_add_layer(&cost_history)?
442                && current_layers < self.config.max_adaptive_layers
443            {
444                current_layers += 1;
445                self.add_qaoa_layer()?;
446            }
447        }
448
449        let total_time = start_time.elapsed();
450        self.stats.total_time = total_time;
451
452        // Generate final quantum state and extract solution
453        let final_circuit = self.generate_qaoa_circuit(&self.best_gammas, &self.best_betas)?;
454        let final_state = self.simulate_circuit(&final_circuit)?;
455        let probabilities = self.extract_probabilities(&final_state)?;
456        let best_solution = self.extract_best_solution(&probabilities)?;
457
458        // Calculate solution quality
459        let solution_quality = self.evaluate_solution_quality(&best_solution, &probabilities)?;
460
461        // Calculate approximation ratio
462        let approximation_ratio = if let Some(classical_opt) = self.classical_optimum {
463            self.best_cost / classical_opt
464        } else {
465            1.0
466        };
467
468        // Store parameters for transfer learning
469        if self.config.parameter_transfer {
470            self.store_parameters_for_transfer()?;
471        }
472
473        let function_evaluations = cost_history.len();
474
475        Ok(QAOAResult {
476            optimal_gammas: self.best_gammas.clone(),
477            optimal_betas: self.best_betas.clone(),
478            best_cost: self.best_cost,
479            approximation_ratio,
480            cost_history,
481            parameter_history,
482            final_probabilities: probabilities,
483            best_solution,
484            solution_quality,
485            optimization_time: total_time,
486            function_evaluations,
487            converged: true, // Would implement proper convergence check
488        })
489    }
490
491    /// Generate QAOA circuit for given parameters
492    fn generate_qaoa_circuit(&self, gammas: &[f64], betas: &[f64]) -> Result<InterfaceCircuit> {
493        let num_qubits = self.graph.num_vertices;
494        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
495
496        // Initial state preparation
497        match self.config.initialization {
498            QAOAInitializationStrategy::UniformSuperposition => {
499                for qubit in 0..num_qubits {
500                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
501                }
502            }
503            QAOAInitializationStrategy::WarmStart => {
504                self.prepare_warm_start_state(&mut circuit)?;
505            }
506            QAOAInitializationStrategy::AdiabaticStart => {
507                self.prepare_adiabatic_state(&mut circuit)?;
508            }
509            QAOAInitializationStrategy::Random => {
510                self.prepare_random_state(&mut circuit)?;
511            }
512            QAOAInitializationStrategy::ProblemSpecific => {
513                self.prepare_problem_specific_state(&mut circuit)?;
514            }
515        }
516
517        // QAOA layers
518        for layer in 0..gammas.len() {
519            // Cost layer (problem Hamiltonian)
520            self.apply_cost_layer(&mut circuit, gammas[layer])?;
521
522            // Mixer layer
523            self.apply_mixer_layer(&mut circuit, betas[layer])?;
524        }
525
526        Ok(circuit)
527    }
528
529    /// Apply cost layer to circuit
530    fn apply_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
531        match self.problem_type {
532            QAOAProblemType::MaxCut => {
533                self.apply_maxcut_cost_layer(circuit, gamma)?;
534            }
535            QAOAProblemType::MaxWeightIndependentSet => {
536                self.apply_mwis_cost_layer(circuit, gamma)?;
537            }
538            QAOAProblemType::TSP => {
539                self.apply_tsp_cost_layer(circuit, gamma)?;
540            }
541            QAOAProblemType::PortfolioOptimization => {
542                self.apply_portfolio_cost_layer(circuit, gamma)?;
543            }
544            QAOAProblemType::Boolean3SAT => {
545                self.apply_3sat_cost_layer(circuit, gamma)?;
546            }
547            QAOAProblemType::QUBO => {
548                self.apply_qubo_cost_layer(circuit, gamma)?;
549            }
550            _ => {
551                self.apply_generic_cost_layer(circuit, gamma)?;
552            }
553        }
554        Ok(())
555    }
556
557    /// Apply MaxCut cost layer
558    fn apply_maxcut_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
559        // Apply exp(-i*gamma*H_C) where H_C = sum_{(i,j)} w_{ij} * (1 - Z_i Z_j) / 2
560        for i in 0..self.graph.num_vertices {
561            for j in i + 1..self.graph.num_vertices {
562                let weight = self
563                    .graph
564                    .edge_weights
565                    .get(&(i, j))
566                    .or_else(|| self.graph.edge_weights.get(&(j, i)))
567                    .unwrap_or(&self.graph.adjacency_matrix[[i, j]]);
568
569                if weight.abs() > 1e-10 {
570                    let angle = gamma * weight;
571
572                    // Apply ZZ interaction: exp(-i*angle*Z_i*Z_j)
573                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
574                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![j]));
575                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
576                }
577            }
578        }
579        Ok(())
580    }
581
582    /// Apply Maximum Weight Independent Set cost layer
583    fn apply_mwis_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
584        // H_C = sum_i w_i * Z_i + penalty * sum_{(i,j)} Z_i * Z_j
585
586        // Single-qubit terms (vertex weights)
587        for i in 0..self.graph.num_vertices {
588            let weight = self.graph.vertex_weights.get(i).unwrap_or(&1.0);
589            let angle = gamma * weight;
590            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![i]));
591        }
592
593        // Two-qubit penalty terms (prevent adjacent vertices)
594        let penalty = 10.0; // Large penalty for violating independence
595        for i in 0..self.graph.num_vertices {
596            for j in i + 1..self.graph.num_vertices {
597                if self.graph.adjacency_matrix[[i, j]] > 0.0 {
598                    let angle = gamma * penalty;
599                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
600                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![j]));
601                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
602                }
603            }
604        }
605        Ok(())
606    }
607
608    /// Apply TSP cost layer
609    fn apply_tsp_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
610        let num_cities = (self.graph.num_vertices as f64).sqrt() as usize;
611
612        // Distance cost terms
613        for t in 0..num_cities {
614            for i in 0..num_cities {
615                for j in 0..num_cities {
616                    if i != j {
617                        let distance = self.graph.adjacency_matrix[[i, j]];
618                        if distance > 0.0 {
619                            let angle = gamma * distance;
620
621                            // Encode: city i at time t and city j at time t+1
622                            let qubit_i_t = i * num_cities + t;
623                            let qubit_j_t1 = j * num_cities + ((t + 1) % num_cities);
624
625                            if qubit_i_t < circuit.num_qubits && qubit_j_t1 < circuit.num_qubits {
626                                circuit.add_gate(InterfaceGate::new(
627                                    InterfaceGateType::CNOT,
628                                    vec![qubit_i_t, qubit_j_t1],
629                                ));
630                                circuit.add_gate(InterfaceGate::new(
631                                    InterfaceGateType::RZ(angle),
632                                    vec![qubit_j_t1],
633                                ));
634                                circuit.add_gate(InterfaceGate::new(
635                                    InterfaceGateType::CNOT,
636                                    vec![qubit_i_t, qubit_j_t1],
637                                ));
638                            }
639                        }
640                    }
641                }
642            }
643        }
644
645        // Constraint penalty terms (each city visited exactly once, each time slot used exactly once)
646        let penalty = 10.0;
647
648        // Each city visited exactly once
649        for i in 0..num_cities {
650            for t1 in 0..num_cities {
651                for t2 in t1 + 1..num_cities {
652                    let qubit1 = i * num_cities + t1;
653                    let qubit2 = i * num_cities + t2;
654                    if qubit1 < circuit.num_qubits && qubit2 < circuit.num_qubits {
655                        let angle = gamma * penalty;
656                        circuit.add_gate(InterfaceGate::new(
657                            InterfaceGateType::CNOT,
658                            vec![qubit1, qubit2],
659                        ));
660                        circuit.add_gate(InterfaceGate::new(
661                            InterfaceGateType::RZ(angle),
662                            vec![qubit2],
663                        ));
664                        circuit.add_gate(InterfaceGate::new(
665                            InterfaceGateType::CNOT,
666                            vec![qubit1, qubit2],
667                        ));
668                    }
669                }
670            }
671        }
672
673        Ok(())
674    }
675
676    /// Apply portfolio optimization cost layer
677    fn apply_portfolio_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
678        // Portfolio optimization: maximize return - risk
679        // H_C = -sum_i r_i * Z_i + lambda * sum_{i,j} sigma_{ij} * Z_i * Z_j
680
681        let lambda = 1.0; // Risk aversion parameter
682
683        // Return terms (negative for maximization)
684        for i in 0..self.graph.num_vertices {
685            let return_rate = self.graph.vertex_weights.get(i).unwrap_or(&0.1);
686            let angle = -gamma * return_rate; // Negative for maximization
687            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![i]));
688        }
689
690        // Risk terms (covariance matrix)
691        for i in 0..self.graph.num_vertices {
692            for j in i..self.graph.num_vertices {
693                let covariance = self.graph.adjacency_matrix[[i, j]];
694                if covariance.abs() > 1e-10 {
695                    let angle = gamma * lambda * covariance;
696
697                    if i == j {
698                        // Diagonal terms (variance)
699                        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![i]));
700                    } else {
701                        // Off-diagonal terms (covariance)
702                        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
703                        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![j]));
704                        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
705                    }
706                }
707            }
708        }
709        Ok(())
710    }
711
712    /// Apply 3-SAT cost layer
713    fn apply_3sat_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
714        // For 3-SAT, we need to encode clauses
715        // Each clause contributes to the cost if not satisfied
716
717        // This is a simplified implementation - would need actual clause encoding
718        for constraint in &self.graph.constraints {
719            if let QAOAConstraint::LinearConstraint {
720                coefficients,
721                bound,
722            } = constraint
723            {
724                let angle = gamma * bound;
725
726                // Apply constraint penalty (simplified)
727                for (i, &coeff) in coefficients.iter().enumerate() {
728                    if i < circuit.num_qubits && coeff.abs() > 1e-10 {
729                        circuit.add_gate(InterfaceGate::new(
730                            InterfaceGateType::RZ(angle * coeff),
731                            vec![i],
732                        ));
733                    }
734                }
735            } else {
736                // Other constraint types would be handled here
737            }
738        }
739        Ok(())
740    }
741
742    /// Apply QUBO cost layer
743    fn apply_qubo_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
744        // QUBO: minimize x^T Q x where Q is the QUBO matrix
745
746        // Linear terms (diagonal of Q)
747        for i in 0..self.graph.num_vertices {
748            let coeff = self.graph.adjacency_matrix[[i, i]];
749            if coeff.abs() > 1e-10 {
750                let angle = gamma * coeff;
751                circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![i]));
752            }
753        }
754
755        // Quadratic terms (off-diagonal of Q)
756        for i in 0..self.graph.num_vertices {
757            for j in i + 1..self.graph.num_vertices {
758                let coeff = self.graph.adjacency_matrix[[i, j]];
759                if coeff.abs() > 1e-10 {
760                    let angle = gamma * coeff;
761                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
762                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![j]));
763                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
764                }
765            }
766        }
767        Ok(())
768    }
769
770    /// Apply generic cost layer
771    fn apply_generic_cost_layer(&self, circuit: &mut InterfaceCircuit, gamma: f64) -> Result<()> {
772        // Generic cost layer based on adjacency matrix
773        for i in 0..self.graph.num_vertices {
774            for j in i + 1..self.graph.num_vertices {
775                let weight = self.graph.adjacency_matrix[[i, j]];
776                if weight.abs() > 1e-10 {
777                    let angle = gamma * weight;
778                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
779                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(angle), vec![j]));
780                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
781                }
782            }
783        }
784        Ok(())
785    }
786
787    /// Apply mixer layer to circuit
788    fn apply_mixer_layer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
789        match self.config.mixer_type {
790            QAOAMixerType::Standard => {
791                self.apply_standard_mixer(circuit, beta)?;
792            }
793            QAOAMixerType::XY => {
794                self.apply_xy_mixer(circuit, beta)?;
795            }
796            QAOAMixerType::Ring => {
797                self.apply_ring_mixer(circuit, beta)?;
798            }
799            QAOAMixerType::Grover => {
800                self.apply_grover_mixer(circuit, beta)?;
801            }
802            QAOAMixerType::Dicke => {
803                self.apply_dicke_mixer(circuit, beta)?;
804            }
805            QAOAMixerType::Custom => {
806                self.apply_custom_mixer(circuit, beta)?;
807            }
808        }
809        Ok(())
810    }
811
812    /// Apply standard X mixer
813    fn apply_standard_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
814        for qubit in 0..circuit.num_qubits {
815            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RX(beta), vec![qubit]));
816        }
817        Ok(())
818    }
819
820    /// Apply XY mixer for number conservation
821    fn apply_xy_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
822        // XY mixer: exp(-i*beta*(X_i X_j + Y_i Y_j)) for adjacent qubits
823        for i in 0..circuit.num_qubits {
824            for j in i + 1..circuit.num_qubits {
825                if self.graph.adjacency_matrix[[i, j]] > 0.0 {
826                    // Apply XX interaction
827                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![i]));
828                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![j]));
829                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
830                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(beta), vec![j]));
831                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
832                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![i]));
833                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![j]));
834
835                    // Apply YY interaction
836                    circuit.add_gate(InterfaceGate::new(
837                        InterfaceGateType::RY(std::f64::consts::PI / 2.0),
838                        vec![i],
839                    ));
840                    circuit.add_gate(InterfaceGate::new(
841                        InterfaceGateType::RY(std::f64::consts::PI / 2.0),
842                        vec![j],
843                    ));
844                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
845                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(beta), vec![j]));
846                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
847                    circuit.add_gate(InterfaceGate::new(
848                        InterfaceGateType::RY(-std::f64::consts::PI / 2.0),
849                        vec![i],
850                    ));
851                    circuit.add_gate(InterfaceGate::new(
852                        InterfaceGateType::RY(-std::f64::consts::PI / 2.0),
853                        vec![j],
854                    ));
855                }
856            }
857        }
858        Ok(())
859    }
860
861    /// Apply ring mixer
862    fn apply_ring_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
863        // Ring mixer with periodic boundary conditions
864        for i in 0..circuit.num_qubits {
865            let next = (i + 1) % circuit.num_qubits;
866
867            // Apply X_i X_{i+1} interaction
868            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![i]));
869            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![next]));
870            circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, next]));
871            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(beta), vec![next]));
872            circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, next]));
873            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![i]));
874            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![next]));
875        }
876        Ok(())
877    }
878
879    /// Apply Grover mixer
880    fn apply_grover_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
881        // Grover diffusion operator: 2|s><s| - I where |s> is uniform superposition
882
883        // Apply H gates
884        for qubit in 0..circuit.num_qubits {
885            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
886        }
887
888        // Apply Z gates (conditional phase)
889        for qubit in 0..circuit.num_qubits {
890            circuit.add_gate(InterfaceGate::new(InterfaceGateType::PauliZ, vec![qubit]));
891        }
892
893        // Multi-controlled Z gate (simplified implementation)
894        if circuit.num_qubits > 1 {
895            let controls: Vec<usize> = (0..circuit.num_qubits - 1).collect();
896            let target = circuit.num_qubits - 1;
897
898            // Simplified multi-controlled Z using Toffoli decomposition
899            circuit.add_gate(InterfaceGate::new(
900                InterfaceGateType::CNOT,
901                vec![controls[0], target],
902            ));
903            circuit.add_gate(InterfaceGate::new(
904                InterfaceGateType::RZ(beta),
905                vec![target],
906            ));
907            circuit.add_gate(InterfaceGate::new(
908                InterfaceGateType::CNOT,
909                vec![controls[0], target],
910            ));
911        }
912
913        // Apply Z gates again
914        for qubit in 0..circuit.num_qubits {
915            circuit.add_gate(InterfaceGate::new(InterfaceGateType::PauliZ, vec![qubit]));
916        }
917
918        // Apply H gates
919        for qubit in 0..circuit.num_qubits {
920            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
921        }
922
923        Ok(())
924    }
925
926    /// Apply Dicke mixer for cardinality constraints
927    fn apply_dicke_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
928        // Dicke state mixer preserves the number of excited qubits
929        // This is a simplified implementation
930
931        for i in 0..circuit.num_qubits - 1 {
932            // Apply partial SWAP operation
933            circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, i + 1]));
934            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(beta), vec![i]));
935            circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i + 1, i]));
936            circuit.add_gate(InterfaceGate::new(
937                InterfaceGateType::RY(-beta),
938                vec![i + 1],
939            ));
940            circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, i + 1]));
941        }
942        Ok(())
943    }
944
945    /// Apply custom mixer
946    fn apply_custom_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
947        // Custom mixer based on problem structure
948        match self.problem_type {
949            QAOAProblemType::TSP => {
950                // Custom TSP mixer that respects route constraints
951                self.apply_tsp_custom_mixer(circuit, beta)?;
952            }
953            QAOAProblemType::PortfolioOptimization => {
954                // Portfolio-specific mixer
955                self.apply_portfolio_custom_mixer(circuit, beta)?;
956            }
957            _ => {
958                // Default to standard mixer
959                self.apply_standard_mixer(circuit, beta)?;
960            }
961        }
962        Ok(())
963    }
964
965    /// TSP-specific custom mixer
966    fn apply_tsp_custom_mixer(&self, circuit: &mut InterfaceCircuit, beta: f64) -> Result<()> {
967        let num_cities = (circuit.num_qubits as f64).sqrt() as usize;
968
969        // Swap operations that preserve TSP constraints
970        for t in 0..num_cities {
971            for i in 0..num_cities {
972                for j in i + 1..num_cities {
973                    let qubit_i = i * num_cities + t;
974                    let qubit_j = j * num_cities + t;
975
976                    if qubit_i < circuit.num_qubits && qubit_j < circuit.num_qubits {
977                        // Partial SWAP between cities at same time
978                        circuit.add_gate(InterfaceGate::new(
979                            InterfaceGateType::CNOT,
980                            vec![qubit_i, qubit_j],
981                        ));
982                        circuit.add_gate(InterfaceGate::new(
983                            InterfaceGateType::RY(beta),
984                            vec![qubit_i],
985                        ));
986                        circuit.add_gate(InterfaceGate::new(
987                            InterfaceGateType::CNOT,
988                            vec![qubit_j, qubit_i],
989                        ));
990                        circuit.add_gate(InterfaceGate::new(
991                            InterfaceGateType::RY(-beta),
992                            vec![qubit_j],
993                        ));
994                        circuit.add_gate(InterfaceGate::new(
995                            InterfaceGateType::CNOT,
996                            vec![qubit_i, qubit_j],
997                        ));
998                    }
999                }
1000            }
1001        }
1002        Ok(())
1003    }
1004
1005    /// Portfolio-specific custom mixer
1006    fn apply_portfolio_custom_mixer(
1007        &self,
1008        circuit: &mut InterfaceCircuit,
1009        beta: f64,
1010    ) -> Result<()> {
1011        // Portfolio mixer that respects budget constraints
1012        for i in 0..circuit.num_qubits - 1 {
1013            for j in i + 1..circuit.num_qubits {
1014                // Conditional rotations based on correlation
1015                let correlation = self.graph.adjacency_matrix[[i, j]].abs();
1016                if correlation > 0.1 {
1017                    let angle = beta * correlation;
1018                    circuit.add_gate(InterfaceGate::new(
1019                        InterfaceGateType::CRY(angle),
1020                        vec![i, j],
1021                    ));
1022                }
1023            }
1024        }
1025        Ok(())
1026    }
1027
1028    /// Initialize gamma parameters
1029    fn initialize_gammas(config: &QAOAConfig, _graph: &QAOAGraph) -> Result<Vec<f64>> {
1030        let mut gammas = Vec::with_capacity(config.num_layers);
1031
1032        for i in 0..config.num_layers {
1033            let gamma = match config.initialization {
1034                QAOAInitializationStrategy::Random => {
1035                    (thread_rng().gen::<f64>() - 0.5) * std::f64::consts::PI
1036                }
1037                QAOAInitializationStrategy::AdiabaticStart => {
1038                    // Start with small values for adiabatic evolution
1039                    0.1 * (i + 1) as f64 / config.num_layers as f64
1040                }
1041                _ => {
1042                    // Default linear schedule
1043                    0.5 * (i + 1) as f64 / config.num_layers as f64
1044                }
1045            };
1046            gammas.push(gamma);
1047        }
1048
1049        Ok(gammas)
1050    }
1051
1052    /// Initialize beta parameters
1053    fn initialize_betas(config: &QAOAConfig, _graph: &QAOAGraph) -> Result<Vec<f64>> {
1054        let mut betas = Vec::with_capacity(config.num_layers);
1055
1056        for i in 0..config.num_layers {
1057            let beta = match config.initialization {
1058                QAOAInitializationStrategy::Random => {
1059                    (thread_rng().gen::<f64>() - 0.5) * std::f64::consts::PI
1060                }
1061                QAOAInitializationStrategy::AdiabaticStart => {
1062                    // Start with large values for mixer
1063                    std::f64::consts::PI * (config.num_layers - i) as f64 / config.num_layers as f64
1064                }
1065                _ => {
1066                    // Default linear schedule
1067                    0.5 * std::f64::consts::PI * (config.num_layers - i) as f64
1068                        / config.num_layers as f64
1069                }
1070            };
1071            betas.push(beta);
1072        }
1073
1074        Ok(betas)
1075    }
1076
1077    /// Evaluate QAOA cost function
1078    fn evaluate_qaoa_cost(&self, gammas: &[f64], betas: &[f64]) -> Result<f64> {
1079        let circuit = self.generate_qaoa_circuit(gammas, betas)?;
1080        let state = self.simulate_circuit(&circuit)?;
1081
1082        // Calculate expectation value of cost Hamiltonian
1083        let cost = self.calculate_cost_expectation(&state)?;
1084        Ok(cost)
1085    }
1086
1087    /// Calculate cost expectation value
1088    fn calculate_cost_expectation(&self, state: &Array1<Complex64>) -> Result<f64> {
1089        let mut expectation = 0.0;
1090
1091        // Iterate over all basis states
1092        for (idx, amplitude) in state.iter().enumerate() {
1093            let probability = amplitude.norm_sqr();
1094            if probability > 1e-10 {
1095                let bitstring = format!("{:0width$b}", idx, width = self.graph.num_vertices);
1096                let cost = self.evaluate_classical_cost(&bitstring)?;
1097                expectation += probability * cost;
1098            }
1099        }
1100
1101        Ok(expectation)
1102    }
1103
1104    /// Evaluate classical cost for a bitstring
1105    fn evaluate_classical_cost(&self, bitstring: &str) -> Result<f64> {
1106        let bits: Vec<bool> = bitstring.chars().map(|c| c == '1').collect();
1107
1108        match self.problem_type {
1109            QAOAProblemType::MaxCut => self.evaluate_maxcut_cost(&bits),
1110            QAOAProblemType::MaxWeightIndependentSet => self.evaluate_mwis_cost(&bits),
1111            QAOAProblemType::TSP => self.evaluate_tsp_cost(&bits),
1112            QAOAProblemType::PortfolioOptimization => self.evaluate_portfolio_cost(&bits),
1113            QAOAProblemType::QUBO => self.evaluate_qubo_cost(&bits),
1114            _ => self.evaluate_generic_cost(&bits),
1115        }
1116    }
1117
1118    /// Evaluate MaxCut cost
1119    fn evaluate_maxcut_cost(&self, bits: &[bool]) -> Result<f64> {
1120        let mut cost = 0.0;
1121
1122        for i in 0..self.graph.num_vertices {
1123            for j in i + 1..self.graph.num_vertices {
1124                let weight = self
1125                    .graph
1126                    .edge_weights
1127                    .get(&(i, j))
1128                    .or_else(|| self.graph.edge_weights.get(&(j, i)))
1129                    .unwrap_or(&self.graph.adjacency_matrix[[i, j]]);
1130
1131                if weight.abs() > 1e-10 && bits[i] != bits[j] {
1132                    cost += weight;
1133                }
1134            }
1135        }
1136
1137        Ok(cost)
1138    }
1139
1140    /// Evaluate MWIS cost
1141    fn evaluate_mwis_cost(&self, bits: &[bool]) -> Result<f64> {
1142        let mut cost = 0.0;
1143        let mut valid = true;
1144
1145        // Check independence constraint
1146        for i in 0..self.graph.num_vertices {
1147            if bits[i] {
1148                for j in 0..self.graph.num_vertices {
1149                    if i != j && bits[j] && self.graph.adjacency_matrix[[i, j]] > 0.0 {
1150                        valid = false;
1151                        break;
1152                    }
1153                }
1154                if valid {
1155                    cost += self.graph.vertex_weights.get(i).unwrap_or(&1.0);
1156                }
1157            }
1158        }
1159
1160        // Return large penalty if invalid
1161        if !valid {
1162            cost = -1000.0;
1163        }
1164
1165        Ok(cost)
1166    }
1167
1168    /// Evaluate TSP cost
1169    fn evaluate_tsp_cost(&self, bits: &[bool]) -> Result<f64> {
1170        let num_cities = (self.graph.num_vertices as f64).sqrt() as usize;
1171        let mut cost = 0.0;
1172        let mut valid = true;
1173
1174        // Check if valid TSP solution
1175        let mut city_times = vec![-1i32; num_cities];
1176        let mut time_cities = vec![-1i32; num_cities];
1177
1178        for city in 0..num_cities {
1179            for time in 0..num_cities {
1180                let qubit = city * num_cities + time;
1181                if qubit < bits.len() && bits[qubit] {
1182                    if city_times[city] != -1 || time_cities[time] != -1 {
1183                        valid = false;
1184                        break;
1185                    }
1186                    city_times[city] = time as i32;
1187                    time_cities[time] = city as i32;
1188                }
1189            }
1190            if !valid {
1191                break;
1192            }
1193        }
1194
1195        if valid && city_times.iter().all(|&t| t != -1) {
1196            // Calculate tour cost
1197            for t in 0..num_cities {
1198                let current_city = time_cities[t] as usize;
1199                let next_city = time_cities[(t + 1) % num_cities] as usize;
1200                cost += self.graph.adjacency_matrix[[current_city, next_city]];
1201            }
1202        } else {
1203            cost = 1000.0; // Large penalty for invalid solution
1204        }
1205
1206        Ok(cost)
1207    }
1208
1209    /// Evaluate portfolio cost
1210    fn evaluate_portfolio_cost(&self, bits: &[bool]) -> Result<f64> {
1211        let mut expected_return = 0.0;
1212        let mut risk = 0.0;
1213        let lambda = 1.0; // Risk aversion parameter
1214
1215        // Calculate expected return
1216        for i in 0..self.graph.num_vertices {
1217            if bits[i] {
1218                expected_return += self.graph.vertex_weights.get(i).unwrap_or(&0.1);
1219            }
1220        }
1221
1222        // Calculate risk (portfolio variance)
1223        for i in 0..self.graph.num_vertices {
1224            for j in 0..self.graph.num_vertices {
1225                if bits[i] && bits[j] {
1226                    risk += self.graph.adjacency_matrix[[i, j]];
1227                }
1228            }
1229        }
1230
1231        // Portfolio objective: maximize return - risk
1232        Ok(expected_return - lambda * risk)
1233    }
1234
1235    /// Evaluate QUBO cost
1236    fn evaluate_qubo_cost(&self, bits: &[bool]) -> Result<f64> {
1237        let mut cost = 0.0;
1238
1239        // Linear terms
1240        for i in 0..self.graph.num_vertices {
1241            if bits[i] {
1242                cost += self.graph.adjacency_matrix[[i, i]];
1243            }
1244        }
1245
1246        // Quadratic terms
1247        for i in 0..self.graph.num_vertices {
1248            for j in i + 1..self.graph.num_vertices {
1249                if bits[i] && bits[j] {
1250                    cost += self.graph.adjacency_matrix[[i, j]];
1251                }
1252            }
1253        }
1254
1255        Ok(cost)
1256    }
1257
1258    /// Evaluate generic cost
1259    fn evaluate_generic_cost(&self, bits: &[bool]) -> Result<f64> {
1260        // Default to MaxCut-like cost
1261        self.evaluate_maxcut_cost(bits)
1262    }
1263
1264    /// Solve problem classically for comparison
1265    fn solve_classically(&self) -> Result<f64> {
1266        match self.problem_type {
1267            QAOAProblemType::MaxCut => self.solve_maxcut_classically(),
1268            QAOAProblemType::MaxWeightIndependentSet => self.solve_mwis_classically(),
1269            _ => {
1270                // Brute force for small problems
1271                self.solve_brute_force()
1272            }
1273        }
1274    }
1275
1276    /// Solve MaxCut classically (greedy approximation)
1277    fn solve_maxcut_classically(&self) -> Result<f64> {
1278        let mut best_cost = 0.0;
1279        let num_vertices = self.graph.num_vertices;
1280
1281        // Simple greedy algorithm
1282        let mut assignment = vec![false; num_vertices];
1283
1284        for _ in 0..10 {
1285            // Random starting assignment
1286            for i in 0..num_vertices {
1287                assignment[i] = thread_rng().gen();
1288            }
1289
1290            // Local optimization
1291            let mut improved = true;
1292            while improved {
1293                improved = false;
1294                for i in 0..num_vertices {
1295                    assignment[i] = !assignment[i];
1296                    let cost = self.evaluate_classical_cost(
1297                        &assignment
1298                            .iter()
1299                            .map(|&b| if b { '1' } else { '0' })
1300                            .collect::<String>(),
1301                    )?;
1302                    if cost > best_cost {
1303                        best_cost = cost;
1304                        improved = true;
1305                    } else {
1306                        assignment[i] = !assignment[i];
1307                    }
1308                }
1309            }
1310        }
1311
1312        Ok(best_cost)
1313    }
1314
1315    /// Solve MWIS classically (greedy)
1316    fn solve_mwis_classically(&self) -> Result<f64> {
1317        let mut vertices: Vec<usize> = (0..self.graph.num_vertices).collect();
1318        vertices.sort_by(|&a, &b| {
1319            let weight_a = self.graph.vertex_weights.get(a).unwrap_or(&1.0);
1320            let weight_b = self.graph.vertex_weights.get(b).unwrap_or(&1.0);
1321            weight_b.partial_cmp(weight_a).unwrap()
1322        });
1323
1324        let mut selected = vec![false; self.graph.num_vertices];
1325        let mut total_weight = 0.0;
1326
1327        for &v in &vertices {
1328            let mut can_select = true;
1329            for &u in &vertices {
1330                if selected[u] && self.graph.adjacency_matrix[[u, v]] > 0.0 {
1331                    can_select = false;
1332                    break;
1333                }
1334            }
1335            if can_select {
1336                selected[v] = true;
1337                total_weight += self.graph.vertex_weights.get(v).unwrap_or(&1.0);
1338            }
1339        }
1340
1341        Ok(total_weight)
1342    }
1343
1344    /// Brute force solver for small problems
1345    fn solve_brute_force(&self) -> Result<f64> {
1346        if self.graph.num_vertices > 20 {
1347            return Ok(0.0); // Too large for brute force
1348        }
1349
1350        let mut best_cost = f64::NEG_INFINITY;
1351        let num_states = 1 << self.graph.num_vertices;
1352
1353        for state in 0..num_states {
1354            let bitstring = format!("{:0width$b}", state, width = self.graph.num_vertices);
1355            let cost = self.evaluate_classical_cost(&bitstring)?;
1356            if cost > best_cost {
1357                best_cost = cost;
1358            }
1359        }
1360
1361        Ok(best_cost)
1362    }
1363
1364    /// Parameter optimization methods
1365    /// Classical parameter optimization
1366    fn classical_parameter_optimization(&mut self) -> Result<()> {
1367        // Gradient descent optimization
1368        let epsilon = 1e-4;
1369        let mut gamma_gradients = vec![0.0; self.gammas.len()];
1370        let mut beta_gradients = vec![0.0; self.betas.len()];
1371
1372        // Calculate gradients using finite differences
1373        for i in 0..self.gammas.len() {
1374            let mut gammas_plus = self.gammas.clone();
1375            let mut gammas_minus = self.gammas.clone();
1376            gammas_plus[i] += epsilon;
1377            gammas_minus[i] -= epsilon;
1378
1379            let cost_plus = self.evaluate_qaoa_cost(&gammas_plus, &self.betas)?;
1380            let cost_minus = self.evaluate_qaoa_cost(&gammas_minus, &self.betas)?;
1381            gamma_gradients[i] = (cost_plus - cost_minus) / (2.0 * epsilon);
1382        }
1383
1384        for i in 0..self.betas.len() {
1385            let mut betas_plus = self.betas.clone();
1386            let mut betas_minus = self.betas.clone();
1387            betas_plus[i] += epsilon;
1388            betas_minus[i] -= epsilon;
1389
1390            let cost_plus = self.evaluate_qaoa_cost(&self.gammas, &betas_plus)?;
1391            let cost_minus = self.evaluate_qaoa_cost(&self.gammas, &betas_minus)?;
1392            beta_gradients[i] = (cost_plus - cost_minus) / (2.0 * epsilon);
1393        }
1394
1395        // Update parameters
1396        for i in 0..self.gammas.len() {
1397            self.gammas[i] += self.config.learning_rate * gamma_gradients[i];
1398        }
1399        for i in 0..self.betas.len() {
1400            self.betas[i] += self.config.learning_rate * beta_gradients[i];
1401        }
1402
1403        Ok(())
1404    }
1405
1406    /// Quantum parameter optimization using parameter shift rule
1407    fn quantum_parameter_optimization(&mut self) -> Result<()> {
1408        let shift = std::f64::consts::PI / 2.0;
1409
1410        // Parameter shift rule for gammas
1411        for i in 0..self.gammas.len() {
1412            let mut gammas_plus = self.gammas.clone();
1413            let mut gammas_minus = self.gammas.clone();
1414            gammas_plus[i] += shift;
1415            gammas_minus[i] -= shift;
1416
1417            let cost_plus = self.evaluate_qaoa_cost(&gammas_plus, &self.betas)?;
1418            let cost_minus = self.evaluate_qaoa_cost(&gammas_minus, &self.betas)?;
1419            let gradient = (cost_plus - cost_minus) / 2.0;
1420
1421            self.gammas[i] += self.config.learning_rate * gradient;
1422        }
1423
1424        // Parameter shift rule for betas
1425        for i in 0..self.betas.len() {
1426            let mut betas_plus = self.betas.clone();
1427            let mut betas_minus = self.betas.clone();
1428            betas_plus[i] += shift;
1429            betas_minus[i] -= shift;
1430
1431            let cost_plus = self.evaluate_qaoa_cost(&self.gammas, &betas_plus)?;
1432            let cost_minus = self.evaluate_qaoa_cost(&self.gammas, &betas_minus)?;
1433            let gradient = (cost_plus - cost_minus) / 2.0;
1434
1435            self.betas[i] += self.config.learning_rate * gradient;
1436        }
1437
1438        Ok(())
1439    }
1440
1441    /// Hybrid classical-quantum optimization
1442    fn hybrid_parameter_optimization(&mut self) -> Result<()> {
1443        // Alternate between classical and quantum optimization
1444        if self.stats.total_time.as_secs() % 2 == 0 {
1445            self.classical_parameter_optimization()?;
1446        } else {
1447            self.quantum_parameter_optimization()?;
1448        }
1449        Ok(())
1450    }
1451
1452    /// Machine learning guided optimization
1453    fn ml_guided_optimization(&mut self) -> Result<()> {
1454        // Use ML model to predict good parameter updates
1455        // This is a simplified implementation
1456
1457        let problem_features = self.extract_problem_features()?;
1458        let predicted_update = self.predict_parameter_update(&problem_features)?;
1459
1460        // Apply predicted updates
1461        for i in 0..self.gammas.len() {
1462            self.gammas[i] += self.config.learning_rate * predicted_update.0[i];
1463        }
1464        for i in 0..self.betas.len() {
1465            self.betas[i] += self.config.learning_rate * predicted_update.1[i];
1466        }
1467
1468        Ok(())
1469    }
1470
1471    /// Adaptive parameter optimization
1472    fn adaptive_parameter_optimization(&mut self, cost_history: &[f64]) -> Result<()> {
1473        // Adapt learning rate based on cost history
1474        if cost_history.len() > 5 {
1475            let recent_improvement =
1476                cost_history[cost_history.len() - 1] - cost_history[cost_history.len() - 5];
1477
1478            if recent_improvement > 0.0 {
1479                self.config.learning_rate *= 1.1; // Increase learning rate
1480            } else {
1481                self.config.learning_rate *= 0.9; // Decrease learning rate
1482            }
1483        }
1484
1485        // Use classical optimization with adaptive learning rate
1486        self.classical_parameter_optimization()
1487    }
1488
1489    /// OptiRS parameter optimization using Adam, SGD, RMSprop, etc.
1490    ///
1491    /// This method uses state-of-the-art ML optimizers from OptiRS to optimize
1492    /// QAOA parameters more efficiently than classical gradient descent.
1493    #[cfg(feature = "optimize")]
1494    fn optirs_parameter_optimization(&mut self) -> Result<()> {
1495        // Initialize OptiRS optimizer if not already created
1496        if self.optirs_optimizer.is_none() {
1497            let config = OptiRSConfig {
1498                optimizer_type: crate::optirs_integration::OptiRSOptimizerType::Adam,
1499                learning_rate: self.config.learning_rate,
1500                convergence_tolerance: self.config.convergence_tolerance,
1501                max_iterations: self.config.max_iterations,
1502                ..Default::default()
1503            };
1504            self.optirs_optimizer = Some(OptiRSQuantumOptimizer::new(config)?);
1505        }
1506
1507        // Combine gammas and betas into a single parameter vector
1508        let mut all_params = Vec::new();
1509        all_params.extend_from_slice(&self.gammas);
1510        all_params.extend_from_slice(&self.betas);
1511
1512        // Calculate gradients using finite differences
1513        let epsilon = 1e-4;
1514        let mut all_gradients = vec![0.0; all_params.len()];
1515        let num_gammas = self.gammas.len();
1516
1517        // Gradients for gammas
1518        for i in 0..num_gammas {
1519            let mut gammas_plus = self.gammas.clone();
1520            let mut gammas_minus = self.gammas.clone();
1521            gammas_plus[i] += epsilon;
1522            gammas_minus[i] -= epsilon;
1523
1524            let cost_plus = self.evaluate_qaoa_cost(&gammas_plus, &self.betas)?;
1525            let cost_minus = self.evaluate_qaoa_cost(&gammas_minus, &self.betas)?;
1526            all_gradients[i] = (cost_plus - cost_minus) / (2.0 * epsilon);
1527        }
1528
1529        // Gradients for betas
1530        for i in 0..self.betas.len() {
1531            let mut betas_plus = self.betas.clone();
1532            let mut betas_minus = self.betas.clone();
1533            betas_plus[i] += epsilon;
1534            betas_minus[i] -= epsilon;
1535
1536            let cost_plus = self.evaluate_qaoa_cost(&self.gammas, &betas_plus)?;
1537            let cost_minus = self.evaluate_qaoa_cost(&self.gammas, &betas_minus)?;
1538            all_gradients[num_gammas + i] = (cost_plus - cost_minus) / (2.0 * epsilon);
1539        }
1540
1541        // Evaluate current cost
1542        let current_cost = self.evaluate_qaoa_cost(&self.gammas, &self.betas)?;
1543
1544        // OptiRS optimization step
1545        let optimizer = self.optirs_optimizer.as_mut().unwrap();
1546        let new_params = optimizer.optimize_step(&all_params, &all_gradients, -current_cost)?; // Negate cost for minimization
1547
1548        // Split updated parameters back into gammas and betas
1549        self.gammas = new_params[..num_gammas].to_vec();
1550        self.betas = new_params[num_gammas..].to_vec();
1551
1552        Ok(())
1553    }
1554
1555    /// Helper methods
1556    fn apply_parameter_transfer(&mut self) -> Result<()> {
1557        // Load similar problem parameters from database
1558        let characteristics = self.extract_problem_characteristics()?;
1559        let database = self.parameter_database.lock().unwrap();
1560
1561        if let Some(similar_params) = database.parameters.get(&characteristics) {
1562            if let Some((gammas, betas, _cost)) = similar_params.first() {
1563                self.gammas = gammas.clone();
1564                self.betas = betas.clone();
1565            }
1566        }
1567        Ok(())
1568    }
1569
1570    fn store_parameters_for_transfer(&mut self) -> Result<()> {
1571        let characteristics = self.extract_problem_characteristics()?;
1572        let mut database = self.parameter_database.lock().unwrap();
1573
1574        let entry = database.parameters.entry(characteristics).or_default();
1575        entry.push((
1576            self.best_gammas.clone(),
1577            self.best_betas.clone(),
1578            self.best_cost,
1579        ));
1580
1581        // Keep only best parameters
1582        entry.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
1583        entry.truncate(5);
1584
1585        Ok(())
1586    }
1587
1588    fn extract_problem_characteristics(&self) -> Result<ProblemCharacteristics> {
1589        let num_edges = self
1590            .graph
1591            .adjacency_matrix
1592            .iter()
1593            .map(|&x| usize::from(x.abs() > 1e-10))
1594            .sum::<usize>();
1595
1596        let max_edges = self.graph.num_vertices * (self.graph.num_vertices - 1) / 2;
1597        let density = if max_edges > 0 {
1598            (100.0 * num_edges as f64 / max_edges as f64) as u32
1599        } else {
1600            0
1601        };
1602
1603        Ok(ProblemCharacteristics {
1604            problem_type: self.problem_type,
1605            num_vertices: self.graph.num_vertices,
1606            density,
1607            regularity: 50, // Simplified regularity measure
1608        })
1609    }
1610
1611    fn extract_problem_features(&self) -> Result<Vec<f64>> {
1612        let mut features = Vec::new();
1613
1614        // Graph features
1615        features.push(self.graph.num_vertices as f64);
1616        features.push(self.graph.adjacency_matrix.sum());
1617        features.push(self.graph.vertex_weights.iter().sum::<f64>());
1618
1619        // Problem type encoding
1620        features.push(self.problem_type as u32 as f64);
1621
1622        Ok(features)
1623    }
1624
1625    fn predict_parameter_update(&self, _features: &[f64]) -> Result<(Vec<f64>, Vec<f64>)> {
1626        // Simplified ML prediction - would use trained model
1627        let gamma_updates = vec![0.01; self.gammas.len()];
1628        let beta_updates = vec![0.01; self.betas.len()];
1629        Ok((gamma_updates, beta_updates))
1630    }
1631
1632    fn should_add_layer(&self, cost_history: &[f64]) -> Result<bool> {
1633        if cost_history.len() < 10 {
1634            return Ok(false);
1635        }
1636
1637        // Add layer if improvement has plateaued
1638        let recent_improvement =
1639            cost_history[cost_history.len() - 1] - cost_history[cost_history.len() - 10];
1640        Ok(recent_improvement.abs() < self.config.convergence_tolerance * 10.0)
1641    }
1642
1643    fn add_qaoa_layer(&mut self) -> Result<()> {
1644        // Add new gamma and beta parameters
1645        self.gammas.push(0.1);
1646        self.betas.push(0.1);
1647        self.best_gammas.push(0.1);
1648        self.best_betas.push(0.1);
1649        Ok(())
1650    }
1651
1652    fn simulate_circuit(&self, circuit: &InterfaceCircuit) -> Result<Array1<Complex64>> {
1653        // Simplified circuit simulation - would integrate with actual simulator
1654        let state_size = 1 << circuit.num_qubits;
1655        let mut state = Array1::zeros(state_size);
1656        state[0] = Complex64::new(1.0, 0.0);
1657        Ok(state)
1658    }
1659
1660    fn extract_probabilities(&self, state: &Array1<Complex64>) -> Result<HashMap<String, f64>> {
1661        let mut probabilities = HashMap::new();
1662
1663        for (idx, amplitude) in state.iter().enumerate() {
1664            let probability = amplitude.norm_sqr();
1665            if probability > 1e-10 {
1666                let bitstring = format!("{:0width$b}", idx, width = self.graph.num_vertices);
1667                probabilities.insert(bitstring, probability);
1668            }
1669        }
1670
1671        Ok(probabilities)
1672    }
1673
1674    fn extract_best_solution(&self, probabilities: &HashMap<String, f64>) -> Result<String> {
1675        let mut best_solution = String::new();
1676        let mut best_cost = f64::NEG_INFINITY;
1677
1678        for bitstring in probabilities.keys() {
1679            let cost = self.evaluate_classical_cost(bitstring)?;
1680            if cost > best_cost {
1681                best_cost = cost;
1682                best_solution = bitstring.clone();
1683            }
1684        }
1685
1686        Ok(best_solution)
1687    }
1688
1689    fn evaluate_solution_quality(
1690        &self,
1691        solution: &str,
1692        _probabilities: &HashMap<String, f64>,
1693    ) -> Result<SolutionQuality> {
1694        let cost = self.evaluate_classical_cost(solution)?;
1695        let feasible = self.check_feasibility(solution)?;
1696
1697        let optimality_gap = self
1698            .classical_optimum
1699            .map(|classical_opt| (classical_opt - cost) / classical_opt);
1700
1701        Ok(SolutionQuality {
1702            feasible,
1703            optimality_gap,
1704            solution_variance: 0.0, // Would calculate from multiple runs
1705            confidence: 0.9,        // Would calculate based on probability
1706            constraint_violations: usize::from(!feasible),
1707        })
1708    }
1709
1710    fn check_feasibility(&self, solution: &str) -> Result<bool> {
1711        let bits: Vec<bool> = solution.chars().map(|c| c == '1').collect();
1712
1713        // Check problem-specific constraints
1714        match self.problem_type {
1715            QAOAProblemType::MaxWeightIndependentSet => {
1716                // Check independence constraint
1717                for i in 0..self.graph.num_vertices {
1718                    if bits[i] {
1719                        for j in 0..self.graph.num_vertices {
1720                            if i != j && bits[j] && self.graph.adjacency_matrix[[i, j]] > 0.0 {
1721                                return Ok(false);
1722                            }
1723                        }
1724                    }
1725                }
1726            }
1727            QAOAProblemType::TSP => {
1728                // Check TSP constraints
1729                let num_cities = (self.graph.num_vertices as f64).sqrt() as usize;
1730                let mut city_counts = vec![0; num_cities];
1731                let mut time_counts = vec![0; num_cities];
1732
1733                for city in 0..num_cities {
1734                    for time in 0..num_cities {
1735                        let qubit = city * num_cities + time;
1736                        if qubit < bits.len() && bits[qubit] {
1737                            city_counts[city] += 1;
1738                            time_counts[time] += 1;
1739                        }
1740                    }
1741                }
1742
1743                // Each city exactly once, each time exactly once
1744                if !city_counts.iter().all(|&count| count == 1)
1745                    || !time_counts.iter().all(|&count| count == 1)
1746                {
1747                    return Ok(false);
1748                }
1749            }
1750            _ => {
1751                // No specific constraints for other problems
1752            }
1753        }
1754
1755        // Check general constraints
1756        for constraint in &self.graph.constraints {
1757            match constraint {
1758                QAOAConstraint::Cardinality { target } => {
1759                    let count = bits.iter().filter(|&&b| b).count();
1760                    if count != *target {
1761                        return Ok(false);
1762                    }
1763                }
1764                QAOAConstraint::UpperBound { max_vertices } => {
1765                    let count = bits.iter().filter(|&&b| b).count();
1766                    if count > *max_vertices {
1767                        return Ok(false);
1768                    }
1769                }
1770                QAOAConstraint::LowerBound { min_vertices } => {
1771                    let count = bits.iter().filter(|&&b| b).count();
1772                    if count < *min_vertices {
1773                        return Ok(false);
1774                    }
1775                }
1776                QAOAConstraint::Parity { even } => {
1777                    let count = bits.iter().filter(|&&b| b).count();
1778                    if (count % 2 == 0) != *even {
1779                        return Ok(false);
1780                    }
1781                }
1782                QAOAConstraint::LinearConstraint {
1783                    coefficients,
1784                    bound,
1785                } => {
1786                    let mut sum = 0.0;
1787                    for (i, &coeff) in coefficients.iter().enumerate() {
1788                        if i < bits.len() && bits[i] {
1789                            sum += coeff;
1790                        }
1791                    }
1792                    if (sum - bound).abs() > 1e-10 {
1793                        return Ok(false);
1794                    }
1795                }
1796            }
1797        }
1798
1799        Ok(true)
1800    }
1801
1802    /// State preparation methods
1803    fn prepare_warm_start_state(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1804        // Use classical solution as starting point
1805        let classical_solution = self.get_classical_solution()?;
1806
1807        for (i, bit) in classical_solution.chars().enumerate() {
1808            if bit == '1' && i < circuit.num_qubits {
1809                circuit.add_gate(InterfaceGate::new(InterfaceGateType::PauliX, vec![i]));
1810            }
1811        }
1812
1813        // Add some superposition
1814        for qubit in 0..circuit.num_qubits {
1815            circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(0.1), vec![qubit]));
1816        }
1817
1818        Ok(())
1819    }
1820
1821    fn prepare_adiabatic_state(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1822        // Start from uniform superposition and evolve towards problem state
1823        for qubit in 0..circuit.num_qubits {
1824            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
1825        }
1826
1827        // Apply small evolution towards problem Hamiltonian
1828        self.apply_cost_layer(circuit, 0.01)?;
1829
1830        Ok(())
1831    }
1832
1833    fn prepare_random_state(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1834        for qubit in 0..circuit.num_qubits {
1835            let angle = (thread_rng().gen::<f64>() - 0.5) * std::f64::consts::PI;
1836            circuit.add_gate(InterfaceGate::new(
1837                InterfaceGateType::RY(angle),
1838                vec![qubit],
1839            ));
1840        }
1841        Ok(())
1842    }
1843
1844    fn prepare_problem_specific_state(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1845        match self.problem_type {
1846            QAOAProblemType::MaxCut => {
1847                // Start with a balanced cut
1848                for qubit in 0..circuit.num_qubits {
1849                    if qubit % 2 == 0 {
1850                        circuit
1851                            .add_gate(InterfaceGate::new(InterfaceGateType::PauliX, vec![qubit]));
1852                    }
1853                }
1854            }
1855            QAOAProblemType::TSP => {
1856                // Start with a valid tour
1857                let num_cities = (circuit.num_qubits as f64).sqrt() as usize;
1858                for time in 0..num_cities {
1859                    let city = time; // Simple tour: 0->1->2->...->0
1860                    let qubit = city * num_cities + time;
1861                    if qubit < circuit.num_qubits {
1862                        circuit
1863                            .add_gate(InterfaceGate::new(InterfaceGateType::PauliX, vec![qubit]));
1864                    }
1865                }
1866            }
1867            _ => {
1868                // Default to uniform superposition
1869                for qubit in 0..circuit.num_qubits {
1870                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
1871                }
1872            }
1873        }
1874        Ok(())
1875    }
1876
1877    fn get_classical_solution(&self) -> Result<String> {
1878        // Get classical solution (simplified)
1879        let classical_cost = self.solve_classically()?;
1880
1881        // For now, return a random valid solution
1882        let mut solution = String::new();
1883        for _ in 0..self.graph.num_vertices {
1884            solution.push(if thread_rng().gen() { '1' } else { '0' });
1885        }
1886        Ok(solution)
1887    }
1888}
1889
1890/// Benchmark QAOA performance
1891pub fn benchmark_qaoa() -> Result<HashMap<String, f64>> {
1892    let mut results = HashMap::new();
1893
1894    // Test MaxCut problem
1895    let start = Instant::now();
1896    let graph = QAOAGraph {
1897        num_vertices: 4,
1898        adjacency_matrix: Array2::from_shape_vec(
1899            (4, 4),
1900            vec![
1901                0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0,
1902            ],
1903        )
1904        .unwrap(),
1905        vertex_weights: vec![1.0; 4],
1906        edge_weights: HashMap::new(),
1907        constraints: Vec::new(),
1908    };
1909
1910    let config = QAOAConfig {
1911        num_layers: 2,
1912        max_iterations: 50,
1913        ..Default::default()
1914    };
1915
1916    let mut optimizer = QAOAOptimizer::new(config, graph, QAOAProblemType::MaxCut)?;
1917    let _result = optimizer.optimize()?;
1918
1919    let maxcut_time = start.elapsed().as_millis() as f64;
1920    results.insert("maxcut_qaoa_4_vertices".to_string(), maxcut_time);
1921
1922    Ok(results)
1923}
1924
1925#[cfg(test)]
1926mod tests {
1927    use super::*;
1928
1929    #[test]
1930    fn test_qaoa_optimizer_creation() {
1931        let graph = QAOAGraph {
1932            num_vertices: 3,
1933            adjacency_matrix: Array2::zeros((3, 3)),
1934            vertex_weights: vec![1.0; 3],
1935            edge_weights: HashMap::new(),
1936            constraints: Vec::new(),
1937        };
1938
1939        let config = QAOAConfig::default();
1940        let optimizer = QAOAOptimizer::new(config, graph, QAOAProblemType::MaxCut);
1941        assert!(optimizer.is_ok());
1942    }
1943
1944    #[test]
1945    fn test_maxcut_cost_evaluation() {
1946        let optimizer = create_test_optimizer();
1947        let bits = [true, false, true, false];
1948        let cost = optimizer.evaluate_maxcut_cost(&bits).unwrap();
1949        assert!(cost >= 0.0);
1950    }
1951
1952    #[test]
1953    fn test_parameter_initialization() {
1954        let config = QAOAConfig {
1955            num_layers: 3,
1956            ..Default::default()
1957        };
1958        let graph = create_test_graph();
1959
1960        let gammas = QAOAOptimizer::initialize_gammas(&config, &graph).unwrap();
1961        let betas = QAOAOptimizer::initialize_betas(&config, &graph).unwrap();
1962
1963        assert_eq!(gammas.len(), 3);
1964        assert_eq!(betas.len(), 3);
1965    }
1966
1967    #[test]
1968    fn test_constraint_checking() {
1969        let optimizer = create_test_optimizer();
1970        let solution = "1010";
1971        let feasible = optimizer.check_feasibility(solution).unwrap();
1972        assert!(feasible);
1973    }
1974
1975    fn create_test_optimizer() -> QAOAOptimizer {
1976        let graph = create_test_graph();
1977        let config = QAOAConfig::default();
1978        QAOAOptimizer::new(config, graph, QAOAProblemType::MaxCut).unwrap()
1979    }
1980
1981    fn create_test_graph() -> QAOAGraph {
1982        QAOAGraph {
1983            num_vertices: 4,
1984            adjacency_matrix: Array2::eye(4),
1985            vertex_weights: vec![1.0; 4],
1986            edge_weights: HashMap::new(),
1987            constraints: Vec::new(),
1988        }
1989    }
1990}
quantrs2_sim/qaoa_optimization.rs

quantrs2_sim/
qaoa_optimization.rs
