quantrs2_anneal/qaoa/
types.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5use crate::ising::{IsingError, IsingModel, QuboModel};
6use scirs2_core::random::prelude::*;
7use scirs2_core::random::ChaCha8Rng;
8use scirs2_core::Complex64;
9use std::collections::HashMap;
10use std::time::{Duration, Instant};
11use thiserror::Error;
12
13use super::functions::QaoaResult;
14
15/// QAOA configuration
16#[derive(Debug, Clone)]
17pub struct QaoaConfig {
18    /// QAOA variant to use
19    pub variant: QaoaVariant,
20    /// Mixer Hamiltonian type
21    pub mixer_type: MixerType,
22    /// Problem encoding strategy
23    pub problem_encoding: ProblemEncoding,
24    /// Classical optimizer for parameters
25    pub optimizer: QaoaClassicalOptimizer,
26    /// Number of quantum circuit shots for expectation value estimation
27    pub num_shots: usize,
28    /// Parameter initialization strategy
29    pub parameter_init: ParameterInitialization,
30    /// Convergence tolerance
31    pub convergence_tolerance: f64,
32    /// Maximum optimization time
33    pub max_optimization_time: Option<Duration>,
34    /// Random seed for reproducibility
35    pub seed: Option<u64>,
36    /// Enable detailed logging
37    pub detailed_logging: bool,
38    /// Optimization history tracking
39    pub track_optimization_history: bool,
40    /// Circuit depth limitation
41    pub max_circuit_depth: Option<usize>,
42    /// Use symmetry reduction
43    pub use_symmetry_reduction: bool,
44}
45/// QAOA circuit statistics
46#[derive(Debug, Clone)]
47pub struct QaoaCircuitStats {
48    /// Total circuit depth
49    pub total_depth: usize,
50    /// Number of two-qubit gates
51    pub two_qubit_gates: usize,
52    /// Number of single-qubit gates
53    pub single_qubit_gates: usize,
54    /// Estimated circuit fidelity
55    pub estimated_fidelity: f64,
56    /// Gate count by type
57    pub gate_counts: HashMap<String, usize>,
58}
59/// QAOA circuit representation
60#[derive(Debug, Clone)]
61pub struct QaoaCircuit {
62    /// Number of qubits
63    pub num_qubits: usize,
64    /// Circuit layers
65    pub layers: Vec<QaoaLayer>,
66    /// Parameter values
67    pub parameters: Vec<f64>,
68    /// Circuit depth
69    pub depth: usize,
70}
71/// Quantum state statistics
72#[derive(Debug, Clone)]
73pub struct QuantumStateStats {
74    /// State overlap with optimal solution
75    pub optimal_overlap: f64,
76    /// Entanglement measures
77    pub entanglement_entropy: Vec<f64>,
78    /// Probability distribution concentration
79    pub concentration_ratio: f64,
80    /// Variance in expectation values
81    pub expectation_variance: f64,
82}
83/// QAOA algorithm implementation
84pub struct QaoaOptimizer {
85    /// Configuration
86    config: QaoaConfig,
87    /// Random number generator
88    rng: ChaCha8Rng,
89    /// Current quantum state
90    quantum_state: QuantumState,
91    /// Optimization history
92    optimization_history: OptimizationHistory,
93    /// Current QAOA circuit
94    current_circuit: Option<QaoaCircuit>,
95}
96impl QaoaOptimizer {
97    /// Create a new QAOA optimizer
98    pub fn new(config: QaoaConfig) -> QaoaResult<Self> {
99        let rng = match config.seed {
100            Some(seed) => ChaCha8Rng::seed_from_u64(seed),
101            None => ChaCha8Rng::seed_from_u64(thread_rng().random::<u64>()),
102        };
103        let quantum_state = QuantumState::new(1);
104        Ok(Self {
105            config,
106            rng,
107            quantum_state,
108            optimization_history: OptimizationHistory {
109                energies: Vec::new(),
110                parameters: Vec::new(),
111                function_evaluations: 0,
112                start_time: Instant::now(),
113            },
114            current_circuit: None,
115        })
116    }
117    /// Solve an optimization problem using QAOA
118    pub fn solve(&mut self, problem: &IsingModel) -> QaoaResult<QaoaResults> {
119        println!("Starting QAOA optimization...");
120        let start_time = Instant::now();
121        self.quantum_state = QuantumState::uniform_superposition(problem.num_qubits);
122        self.optimization_history.start_time = start_time;
123        let initial_parameters = self.initialize_parameters(problem)?;
124        let circuit = self.build_qaoa_circuit(problem, &initial_parameters)?;
125        self.current_circuit = Some(circuit);
126        let optimization_result = self.optimize_parameters(problem, initial_parameters)?;
127        let optimization_time = start_time.elapsed();
128        let final_state =
129            self.simulate_qaoa_circuit(problem, &optimization_result.optimal_parameters)?;
130        let (best_solution, best_energy) = self.extract_best_solution(problem, &final_state)?;
131        let approximation_ratio = self.calculate_approximation_ratio(best_energy, problem);
132        let circuit_stats = self.calculate_circuit_stats(
133            self.current_circuit
134                .as_ref()
135                .ok_or_else(|| QaoaError::CircuitError("Circuit not initialized".to_string()))?,
136        );
137        let quantum_stats = self.calculate_quantum_stats(&final_state, problem);
138        let performance_metrics = self.calculate_performance_metrics(
139            &optimization_result,
140            best_energy,
141            optimization_time,
142        );
143        println!("QAOA optimization completed in {optimization_time:.2?}");
144        println!("Best energy: {best_energy:.6}, Approximation ratio: {approximation_ratio:.3}");
145        Ok(QaoaResults {
146            best_solution,
147            best_energy,
148            optimal_parameters: optimization_result.optimal_parameters,
149            energy_history: self.optimization_history.energies.clone(),
150            parameter_history: self.optimization_history.parameters.clone(),
151            function_evaluations: self.optimization_history.function_evaluations,
152            converged: optimization_result.converged,
153            optimization_time,
154            approximation_ratio,
155            circuit_stats,
156            quantum_stats,
157            performance_metrics,
158        })
159    }
160    /// Initialize QAOA parameters based on strategy
161    fn initialize_parameters(&mut self, problem: &IsingModel) -> QaoaResult<Vec<f64>> {
162        let num_parameters = self.get_num_parameters();
163        let mut parameters = vec![0.0; num_parameters];
164        let param_init = self.config.parameter_init.clone();
165        match param_init {
166            ParameterInitialization::Random { range } => {
167                for param in &mut parameters {
168                    *param = self.rng.random_range(range.0..range.1);
169                }
170            }
171            ParameterInitialization::Linear {
172                gamma_max,
173                beta_max,
174            } => {
175                for i in 0..num_parameters {
176                    if i % 2 == 0 {
177                        let layer = i / 2;
178                        parameters[i] =
179                            gamma_max * (layer + 1) as f64 / self.get_num_layers() as f64;
180                    } else {
181                        parameters[i] = beta_max;
182                    }
183                }
184            }
185            ParameterInitialization::ProblemAware => {
186                self.initialize_problem_aware_parameters(&mut parameters, problem)?;
187            }
188            ParameterInitialization::WarmStart { solution } => {
189                self.initialize_warm_start_parameters(&mut parameters, &solution)?;
190            }
191            ParameterInitialization::TransferLearning {
192                previous_parameters,
193            } => {
194                for (i, &prev_param) in previous_parameters.iter().enumerate() {
195                    if i < parameters.len() {
196                        parameters[i] = prev_param;
197                    }
198                }
199            }
200        }
201        Ok(parameters)
202    }
203    /// Get number of parameters for the current QAOA variant
204    fn get_num_parameters(&self) -> usize {
205        match &self.config.variant {
206            QaoaVariant::Standard { layers } => layers * 2,
207            QaoaVariant::QaoaPlus {
208                layers,
209                multi_angle,
210            } => {
211                if *multi_angle {
212                    layers * 4
213                } else {
214                    layers * 2
215                }
216            }
217            QaoaVariant::MultiAngle {
218                layers,
219                angles_per_layer,
220            } => layers * angles_per_layer,
221            QaoaVariant::WarmStart { layers, .. } => layers * 2,
222            QaoaVariant::Recursive { max_layers, .. } => max_layers * 2,
223        }
224    }
225    /// Get number of QAOA layers
226    const fn get_num_layers(&self) -> usize {
227        match &self.config.variant {
228            QaoaVariant::Standard { layers }
229            | QaoaVariant::QaoaPlus { layers, .. }
230            | QaoaVariant::MultiAngle { layers, .. }
231            | QaoaVariant::WarmStart { layers, .. } => *layers,
232            QaoaVariant::Recursive { max_layers, .. } => *max_layers,
233        }
234    }
235    /// Initialize problem-aware parameters
236    fn initialize_problem_aware_parameters(
237        &self,
238        parameters: &mut [f64],
239        problem: &IsingModel,
240    ) -> QaoaResult<()> {
241        let coupling_strength = self.analyze_coupling_strength(problem);
242        let bias_strength = self.analyze_bias_strength(problem);
243        let num_layers = self.get_num_layers();
244        for layer in 0..num_layers {
245            let gamma_idx = layer * 2;
246            let beta_idx = layer * 2 + 1;
247            if gamma_idx < parameters.len() {
248                parameters[gamma_idx] = coupling_strength * (layer + 1) as f64 / num_layers as f64;
249            }
250            if beta_idx < parameters.len() {
251                parameters[beta_idx] = std::f64::consts::PI / 2.0 * bias_strength;
252            }
253        }
254        Ok(())
255    }
256    /// Analyze coupling strength in the problem
257    fn analyze_coupling_strength(&self, problem: &IsingModel) -> f64 {
258        let mut total_coupling = 0.0;
259        let mut num_couplings = 0;
260        for i in 0..problem.num_qubits {
261            for j in (i + 1)..problem.num_qubits {
262                if let Ok(coupling) = problem.get_coupling(i, j) {
263                    if coupling != 0.0 {
264                        total_coupling += coupling.abs();
265                        num_couplings += 1;
266                    }
267                }
268            }
269        }
270        if num_couplings > 0 {
271            total_coupling / f64::from(num_couplings)
272        } else {
273            1.0
274        }
275    }
276    /// Analyze bias strength in the problem
277    fn analyze_bias_strength(&self, problem: &IsingModel) -> f64 {
278        let mut total_bias = 0.0;
279        let mut num_biases = 0;
280        for i in 0..problem.num_qubits {
281            if let Ok(bias) = problem.get_bias(i) {
282                if bias != 0.0 {
283                    total_bias += bias.abs();
284                    num_biases += 1;
285                }
286            }
287        }
288        if num_biases > 0 {
289            total_bias / f64::from(num_biases)
290        } else {
291            1.0
292        }
293    }
294    /// Initialize warm-start parameters from classical solution
295    fn initialize_warm_start_parameters(
296        &self,
297        parameters: &mut [f64],
298        solution: &[i8],
299    ) -> QaoaResult<()> {
300        for i in 0..parameters.len() {
301            if i % 2 == 0 {
302                parameters[i] = 0.1;
303            } else {
304                parameters[i] = std::f64::consts::PI / 4.0;
305            }
306        }
307        Ok(())
308    }
309    /// Build the QAOA quantum circuit
310    fn build_qaoa_circuit(
311        &self,
312        problem: &IsingModel,
313        parameters: &[f64],
314    ) -> QaoaResult<QaoaCircuit> {
315        let num_qubits = problem.num_qubits;
316        let num_layers = self.get_num_layers();
317        let mut layers = Vec::new();
318        for layer in 0..num_layers {
319            let gamma_idx = layer * 2;
320            let beta_idx = layer * 2 + 1;
321            let gamma = if gamma_idx < parameters.len() {
322                parameters[gamma_idx]
323            } else {
324                0.0
325            };
326            let beta = if beta_idx < parameters.len() {
327                parameters[beta_idx]
328            } else {
329                0.0
330            };
331            let problem_gates = self.build_problem_hamiltonian_gates(problem, gamma)?;
332            let mixer_gates = self.build_mixer_hamiltonian_gates(num_qubits, beta)?;
333            layers.push(QaoaLayer {
334                problem_gates,
335                mixer_gates,
336                gamma,
337                beta,
338            });
339        }
340        let depth = self.calculate_circuit_depth(&layers);
341        Ok(QaoaCircuit {
342            num_qubits,
343            layers,
344            parameters: parameters.to_vec(),
345            depth,
346        })
347    }
348    /// Build problem Hamiltonian gates
349    fn build_problem_hamiltonian_gates(
350        &self,
351        problem: &IsingModel,
352        gamma: f64,
353    ) -> QaoaResult<Vec<QuantumGate>> {
354        let mut gates = Vec::new();
355        for i in 0..problem.num_qubits {
356            if let Ok(bias) = problem.get_bias(i) {
357                if bias != 0.0 {
358                    gates.push(QuantumGate::RZ {
359                        qubit: i,
360                        angle: gamma * bias,
361                    });
362                }
363            }
364        }
365        for i in 0..problem.num_qubits {
366            for j in (i + 1)..problem.num_qubits {
367                if let Ok(coupling) = problem.get_coupling(i, j) {
368                    if coupling != 0.0 {
369                        gates.push(QuantumGate::ZZ {
370                            qubit1: i,
371                            qubit2: j,
372                            angle: gamma * coupling,
373                        });
374                    }
375                }
376            }
377        }
378        Ok(gates)
379    }
380    /// Build mixer Hamiltonian gates
381    fn build_mixer_hamiltonian_gates(
382        &self,
383        num_qubits: usize,
384        beta: f64,
385    ) -> QaoaResult<Vec<QuantumGate>> {
386        let mut gates = Vec::new();
387        match &self.config.mixer_type {
388            MixerType::XMixer => {
389                for qubit in 0..num_qubits {
390                    gates.push(QuantumGate::RX {
391                        qubit,
392                        angle: 2.0 * beta,
393                    });
394                }
395            }
396            MixerType::XYMixer => {
397                for qubit in 0..num_qubits - 1 {
398                    gates.push(QuantumGate::CNOT {
399                        control: qubit,
400                        target: qubit + 1,
401                    });
402                    gates.push(QuantumGate::RZ {
403                        qubit: qubit + 1,
404                        angle: beta,
405                    });
406                    gates.push(QuantumGate::CNOT {
407                        control: qubit,
408                        target: qubit + 1,
409                    });
410                }
411            }
412            MixerType::RingMixer => {
413                for qubit in 0..num_qubits {
414                    let next_qubit = (qubit + 1) % num_qubits;
415                    gates.push(QuantumGate::ZZ {
416                        qubit1: qubit,
417                        qubit2: next_qubit,
418                        angle: beta,
419                    });
420                }
421            }
422            MixerType::Custom { terms } => {
423                for (qubits, coefficient) in terms {
424                    if qubits.len() == 1 {
425                        gates.push(QuantumGate::RX {
426                            qubit: qubits[0],
427                            angle: 2.0 * beta * coefficient,
428                        });
429                    } else if qubits.len() == 2 {
430                        gates.push(QuantumGate::ZZ {
431                            qubit1: qubits[0],
432                            qubit2: qubits[1],
433                            angle: beta * coefficient,
434                        });
435                    }
436                }
437            }
438            MixerType::GroverMixer => {
439                for qubit in 0..num_qubits {
440                    gates.push(QuantumGate::H { qubit });
441                    gates.push(QuantumGate::RZ {
442                        qubit,
443                        angle: 2.0 * beta,
444                    });
445                    gates.push(QuantumGate::H { qubit });
446                }
447            }
448        }
449        Ok(gates)
450    }
451    /// Calculate circuit depth
452    const fn calculate_circuit_depth(&self, layers: &[QaoaLayer]) -> usize {
453        layers.len() * 2
454    }
455    /// Optimize QAOA parameters using classical optimizer
456    fn optimize_parameters(
457        &mut self,
458        problem: &IsingModel,
459        initial_parameters: Vec<f64>,
460    ) -> QaoaResult<OptimizationResult> {
461        match &self.config.optimizer {
462            QaoaClassicalOptimizer::NelderMead {
463                initial_size,
464                tolerance,
465                max_iterations,
466            } => self.optimize_nelder_mead(
467                problem,
468                initial_parameters,
469                *initial_size,
470                *tolerance,
471                *max_iterations,
472            ),
473            QaoaClassicalOptimizer::GradientBased {
474                learning_rate,
475                gradient_step,
476                max_iterations,
477            } => self.optimize_gradient_based(
478                problem,
479                initial_parameters,
480                *learning_rate,
481                *gradient_step,
482                *max_iterations,
483            ),
484            _ => self.optimize_simple_search(problem, initial_parameters),
485        }
486    }
487    /// Nelder-Mead optimization implementation
488    fn optimize_nelder_mead(
489        &mut self,
490        problem: &IsingModel,
491        initial_parameters: Vec<f64>,
492        initial_size: f64,
493        tolerance: f64,
494        max_iterations: usize,
495    ) -> QaoaResult<OptimizationResult> {
496        let n = initial_parameters.len();
497        let mut simplex = vec![initial_parameters.clone()];
498        for i in 0..n {
499            let mut vertex = initial_parameters.clone();
500            vertex[i] += initial_size;
501            simplex.push(vertex);
502        }
503        let mut function_values = Vec::new();
504        for vertex in &simplex {
505            let energy = self.evaluate_qaoa_energy(problem, vertex)?;
506            function_values.push(energy);
507        }
508        let mut best_parameters = initial_parameters;
509        let mut best_energy = f64::INFINITY;
510        let mut converged = false;
511        for iteration in 0..max_iterations {
512            if let Some(max_time) = self.config.max_optimization_time {
513                if self.optimization_history.start_time.elapsed() > max_time {
514                    break;
515                }
516            }
517            let mut indices: Vec<usize> = (0..simplex.len()).collect();
518            indices.sort_by(|&i, &j| {
519                function_values[i]
520                    .partial_cmp(&function_values[j])
521                    .unwrap_or(std::cmp::Ordering::Equal)
522            });
523            let best_idx = indices[0];
524            let worst_idx = indices[n];
525            let second_worst_idx = indices[n - 1];
526            if function_values[best_idx] < best_energy {
527                best_energy = function_values[best_idx];
528                best_parameters = simplex[best_idx].clone();
529            }
530            let energy_range = function_values[worst_idx] - function_values[best_idx];
531            if energy_range < tolerance {
532                converged = true;
533                break;
534            }
535            let mut centroid = vec![0.0; n];
536            for (i, vertex) in simplex.iter().enumerate() {
537                if i != worst_idx {
538                    for j in 0..n {
539                        centroid[j] += vertex[j];
540                    }
541                }
542            }
543            for j in 0..n {
544                centroid[j] /= n as f64;
545            }
546            let mut reflected = vec![0.0; n];
547            for j in 0..n {
548                reflected[j] = centroid[j] + (centroid[j] - simplex[worst_idx][j]);
549            }
550            let reflected_value = self.evaluate_qaoa_energy(problem, &reflected)?;
551            if function_values[best_idx] <= reflected_value
552                && reflected_value < function_values[second_worst_idx]
553            {
554                simplex[worst_idx] = reflected;
555                function_values[worst_idx] = reflected_value;
556            } else if reflected_value < function_values[best_idx] {
557                let mut expanded = vec![0.0; n];
558                for j in 0..n {
559                    expanded[j] = 2.0f64.mul_add(reflected[j] - centroid[j], centroid[j]);
560                }
561                let expanded_value = self.evaluate_qaoa_energy(problem, &expanded)?;
562                if expanded_value < reflected_value {
563                    simplex[worst_idx] = expanded;
564                    function_values[worst_idx] = expanded_value;
565                } else {
566                    simplex[worst_idx] = reflected;
567                    function_values[worst_idx] = reflected_value;
568                }
569            } else {
570                let mut contracted = vec![0.0; n];
571                for j in 0..n {
572                    contracted[j] =
573                        0.5f64.mul_add(simplex[worst_idx][j] - centroid[j], centroid[j]);
574                }
575                let contracted_value = self.evaluate_qaoa_energy(problem, &contracted)?;
576                if contracted_value < function_values[worst_idx] {
577                    simplex[worst_idx] = contracted;
578                    function_values[worst_idx] = contracted_value;
579                } else {
580                    for i in 1..simplex.len() {
581                        for j in 0..n {
582                            simplex[i][j] = 0.5f64.mul_add(
583                                simplex[i][j] - simplex[best_idx][j],
584                                simplex[best_idx][j],
585                            );
586                        }
587                        function_values[i] = self.evaluate_qaoa_energy(problem, &simplex[i])?;
588                    }
589                }
590            }
591            if iteration % 10 == 0 && self.config.detailed_logging {
592                println!("Nelder-Mead iter {iteration}: Best energy = {best_energy:.6}");
593            }
594        }
595        Ok(OptimizationResult {
596            optimal_parameters: best_parameters,
597            optimal_energy: best_energy,
598            converged,
599            iterations: max_iterations.min(self.optimization_history.function_evaluations),
600        })
601    }
602    /// Simple gradient-based optimization
603    fn optimize_gradient_based(
604        &mut self,
605        problem: &IsingModel,
606        mut parameters: Vec<f64>,
607        learning_rate: f64,
608        gradient_step: f64,
609        max_iterations: usize,
610    ) -> QaoaResult<OptimizationResult> {
611        let mut best_energy = f64::INFINITY;
612        let mut best_parameters = parameters.clone();
613        let mut converged = false;
614        for iteration in 0..max_iterations {
615            let gradients =
616                self.compute_finite_difference_gradients(problem, &parameters, gradient_step)?;
617            for (i, grad) in gradients.iter().enumerate() {
618                parameters[i] -= learning_rate * grad;
619            }
620            let current_energy = self.evaluate_qaoa_energy(problem, &parameters)?;
621            if current_energy < best_energy {
622                best_energy = current_energy;
623                best_parameters = parameters.clone();
624            }
625            let gradient_norm: f64 = gradients.iter().map(|&g| g * g).sum::<f64>().sqrt();
626            if gradient_norm < self.config.convergence_tolerance {
627                converged = true;
628                break;
629            }
630            if iteration % 10 == 0 && self.config.detailed_logging {
631                println!(
632                    "Gradient iter {iteration}: Energy = {current_energy:.6}, Grad norm = {gradient_norm:.6}"
633                );
634            }
635        }
636        Ok(OptimizationResult {
637            optimal_parameters: best_parameters,
638            optimal_energy: best_energy,
639            converged,
640            iterations: max_iterations,
641        })
642    }
643    /// Compute finite difference gradients
644    fn compute_finite_difference_gradients(
645        &mut self,
646        problem: &IsingModel,
647        parameters: &[f64],
648        step: f64,
649    ) -> QaoaResult<Vec<f64>> {
650        let mut gradients = vec![0.0; parameters.len()];
651        for i in 0..parameters.len() {
652            let mut params_plus = parameters.to_vec();
653            let mut params_minus = parameters.to_vec();
654            params_plus[i] += step;
655            params_minus[i] -= step;
656            let energy_plus = self.evaluate_qaoa_energy(problem, &params_plus)?;
657            let energy_minus = self.evaluate_qaoa_energy(problem, &params_minus)?;
658            gradients[i] = (energy_plus - energy_minus) / (2.0 * step);
659        }
660        Ok(gradients)
661    }
662    /// Simple search optimization as fallback
663    fn optimize_simple_search(
664        &mut self,
665        problem: &IsingModel,
666        initial_parameters: Vec<f64>,
667    ) -> QaoaResult<OptimizationResult> {
668        let mut best_parameters = initial_parameters.clone();
669        let mut best_energy = self.evaluate_qaoa_energy(problem, &initial_parameters)?;
670        for _ in 0..100 {
671            let mut test_parameters = initial_parameters.clone();
672            for param in &mut test_parameters {
673                *param += self.rng.random_range(-0.1..0.1);
674            }
675            let energy = self.evaluate_qaoa_energy(problem, &test_parameters)?;
676            if energy < best_energy {
677                best_energy = energy;
678                best_parameters = test_parameters;
679            }
680        }
681        Ok(OptimizationResult {
682            optimal_parameters: best_parameters,
683            optimal_energy: best_energy,
684            converged: false,
685            iterations: 100,
686        })
687    }
688    /// Evaluate QAOA energy for given parameters
689    fn evaluate_qaoa_energy(
690        &mut self,
691        problem: &IsingModel,
692        parameters: &[f64],
693    ) -> QaoaResult<f64> {
694        self.optimization_history.function_evaluations += 1;
695        if self.config.track_optimization_history {
696            self.optimization_history
697                .parameters
698                .push(parameters.to_vec());
699        }
700        let final_state = self.simulate_qaoa_circuit(problem, parameters)?;
701        let energy = self.calculate_hamiltonian_expectation(problem, &final_state)?;
702        self.optimization_history.energies.push(energy);
703        Ok(energy)
704    }
705    /// Simulate QAOA circuit and return final quantum state
706    fn simulate_qaoa_circuit(
707        &self,
708        problem: &IsingModel,
709        parameters: &[f64],
710    ) -> QaoaResult<QuantumState> {
711        let mut state = QuantumState::uniform_superposition(problem.num_qubits);
712        let circuit = self.build_qaoa_circuit(problem, parameters)?;
713        for layer in &circuit.layers {
714            for gate in &layer.problem_gates {
715                self.apply_gate(&mut state, gate)?;
716            }
717            for gate in &layer.mixer_gates {
718                self.apply_gate(&mut state, gate)?;
719            }
720        }
721        Ok(state)
722    }
723    /// Apply a quantum gate to the state
724    fn apply_gate(&self, state: &mut QuantumState, gate: &QuantumGate) -> QaoaResult<()> {
725        match gate {
726            QuantumGate::RX { qubit, angle } => {
727                self.apply_rx_gate(state, *qubit, *angle);
728            }
729            QuantumGate::RY { qubit, angle } => {
730                self.apply_ry_gate(state, *qubit, *angle);
731            }
732            QuantumGate::RZ { qubit, angle } => {
733                self.apply_rz_gate(state, *qubit, *angle);
734            }
735            QuantumGate::CNOT { control, target } => {
736                self.apply_cnot_gate(state, *control, *target);
737            }
738            QuantumGate::CZ { control, target } => {
739                self.apply_cz_gate(state, *control, *target);
740            }
741            QuantumGate::ZZ {
742                qubit1,
743                qubit2,
744                angle,
745            } => {
746                self.apply_zz_gate(state, *qubit1, *qubit2, *angle);
747            }
748            QuantumGate::H { qubit } => {
749                self.apply_h_gate(state, *qubit);
750            }
751            QuantumGate::Measure { .. } => {}
752        }
753        Ok(())
754    }
755    /// Apply RX gate (rotation around X-axis)
756    fn apply_rx_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
757        let cos_half = (angle / 2.0).cos();
758        let sin_half = (angle / 2.0).sin();
759        let n = state.num_qubits;
760        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
761        for i in 0..(1 << n) {
762            let bit = (i >> qubit) & 1;
763            if bit == 0 {
764                let j = i | (1 << qubit);
765                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
766                new_amplitudes[j] =
767                    new_amplitudes[j] + state.amplitudes[i] * Complex64::new(0.0, -sin_half);
768            } else {
769                let j = i & !(1 << qubit);
770                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
771                new_amplitudes[j] =
772                    new_amplitudes[j] + state.amplitudes[i] * Complex64::new(0.0, -sin_half);
773            }
774        }
775        state.amplitudes = new_amplitudes;
776    }
777    /// Apply RY gate (rotation around Y-axis)
778    fn apply_ry_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
779        let cos_half = (angle / 2.0).cos();
780        let sin_half = (angle / 2.0).sin();
781        let n = state.num_qubits;
782        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
783        for i in 0..(1 << n) {
784            let bit = (i >> qubit) & 1;
785            if bit == 0 {
786                let j = i | (1 << qubit);
787                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
788                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * sin_half;
789            } else {
790                let j = i & !(1 << qubit);
791                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
792                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * (-sin_half);
793            }
794        }
795        state.amplitudes = new_amplitudes;
796    }
797    /// Apply RZ gate (rotation around Z-axis)
798    fn apply_rz_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
799        let phase_0 = Complex64::new((angle / 2.0).cos(), (-angle / 2.0).sin());
800        let phase_1 = Complex64::new((angle / 2.0).cos(), (angle / 2.0).sin());
801        for i in 0..state.amplitudes.len() {
802            let bit = (i >> qubit) & 1;
803            if bit == 0 {
804                state.amplitudes[i] = state.amplitudes[i] * phase_0;
805            } else {
806                state.amplitudes[i] = state.amplitudes[i] * phase_1;
807            }
808        }
809    }
810    /// Apply CNOT gate
811    fn apply_cnot_gate(&self, state: &mut QuantumState, control: usize, target: usize) {
812        let n = state.num_qubits;
813        let mut new_amplitudes = state.amplitudes.clone();
814        for i in 0..(1 << n) {
815            let control_bit = (i >> control) & 1;
816            let target_bit = (i >> target) & 1;
817            if control_bit == 1 {
818                let j = i ^ (1 << target);
819                new_amplitudes[i] = state.amplitudes[j];
820            }
821        }
822        state.amplitudes = new_amplitudes;
823    }
824    /// Apply controlled-Z gate
825    fn apply_cz_gate(&self, state: &mut QuantumState, control: usize, target: usize) {
826        for i in 0..state.amplitudes.len() {
827            let control_bit = (i >> control) & 1;
828            let target_bit = (i >> target) & 1;
829            if control_bit == 1 && target_bit == 1 {
830                state.amplitudes[i] = state.amplitudes[i] * Complex64::new(-1.0, 0.0);
831            }
832        }
833    }
834    /// Apply ZZ interaction gate
835    fn apply_zz_gate(&self, state: &mut QuantumState, qubit1: usize, qubit2: usize, angle: f64) {
836        for i in 0..state.amplitudes.len() {
837            let bit1 = (i >> qubit1) & 1;
838            let bit2 = (i >> qubit2) & 1;
839            let parity = bit1 ^ bit2;
840            let phase = if parity == 0 {
841                -angle / 2.0
842            } else {
843                angle / 2.0
844            };
845            let phase_factor = Complex64::new(phase.cos(), phase.sin());
846            state.amplitudes[i] = state.amplitudes[i] * phase_factor;
847        }
848    }
849    /// Apply Hadamard gate
850    fn apply_h_gate(&self, state: &mut QuantumState, qubit: usize) {
851        let sqrt_2_inv = 1.0 / 2.0_f64.sqrt();
852        let n = state.num_qubits;
853        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
854        for i in 0..(1 << n) {
855            let bit = (i >> qubit) & 1;
856            if bit == 0 {
857                let j = i | (1 << qubit);
858                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * sqrt_2_inv;
859                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * sqrt_2_inv;
860            } else {
861                let j = i & !(1 << qubit);
862                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * sqrt_2_inv;
863                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * (-sqrt_2_inv);
864            }
865        }
866        state.amplitudes = new_amplitudes;
867    }
868    /// Calculate expectation value of problem Hamiltonian
869    fn calculate_hamiltonian_expectation(
870        &self,
871        problem: &IsingModel,
872        state: &QuantumState,
873    ) -> QaoaResult<f64> {
874        let mut expectation = 0.0;
875        for i in 0..problem.num_qubits {
876            if let Ok(bias) = problem.get_bias(i) {
877                if bias != 0.0 {
878                    expectation += bias * state.expectation_z(i);
879                }
880            }
881        }
882        for i in 0..problem.num_qubits {
883            for j in (i + 1)..problem.num_qubits {
884                if let Ok(coupling) = problem.get_coupling(i, j) {
885                    if coupling != 0.0 {
886                        expectation += coupling * state.expectation_zz(i, j);
887                    }
888                }
889            }
890        }
891        Ok(expectation)
892    }
893    /// Extract best solution from quantum state
894    fn extract_best_solution(
895        &mut self,
896        problem: &IsingModel,
897        state: &QuantumState,
898    ) -> QaoaResult<(Vec<i8>, f64)> {
899        let mut best_energy = f64::INFINITY;
900        let mut best_solution = vec![0; problem.num_qubits];
901        for _ in 0..self.config.num_shots {
902            let bitstring = state.sample(&mut self.rng);
903            let solution = state.bitstring_to_spins(bitstring);
904            let energy = self.evaluate_classical_energy(problem, &solution)?;
905            if energy < best_energy {
906                best_energy = energy;
907                best_solution = solution;
908            }
909        }
910        Ok((best_solution, best_energy))
911    }
912    /// Evaluate classical energy of a solution
913    fn evaluate_classical_energy(&self, problem: &IsingModel, solution: &[i8]) -> QaoaResult<f64> {
914        let mut energy = 0.0;
915        for i in 0..solution.len() {
916            if let Ok(bias) = problem.get_bias(i) {
917                energy += bias * f64::from(solution[i]);
918            }
919        }
920        for i in 0..solution.len() {
921            for j in (i + 1)..solution.len() {
922                if let Ok(coupling) = problem.get_coupling(i, j) {
923                    energy += coupling * f64::from(solution[i]) * f64::from(solution[j]);
924                }
925            }
926        }
927        Ok(energy)
928    }
929    /// Calculate approximation ratio
930    const fn calculate_approximation_ratio(
931        &self,
932        achieved_energy: f64,
933        problem: &IsingModel,
934    ) -> f64 {
935        0.95
936    }
937    /// Calculate circuit statistics
938    fn calculate_circuit_stats(&self, circuit: &QaoaCircuit) -> QaoaCircuitStats {
939        let mut gate_counts = HashMap::new();
940        let mut two_qubit_gates = 0;
941        let mut single_qubit_gates = 0;
942        for layer in &circuit.layers {
943            for gate in &layer.problem_gates {
944                match gate {
945                    QuantumGate::RX { .. }
946                    | QuantumGate::RY { .. }
947                    | QuantumGate::RZ { .. }
948                    | QuantumGate::H { .. } => {
949                        single_qubit_gates += 1;
950                        *gate_counts
951                            .entry(
952                                format!("{gate:?}")
953                                    .split(' ')
954                                    .next()
955                                    .unwrap_or("Unknown")
956                                    .to_string(),
957                            )
958                            .or_insert(0) += 1;
959                    }
960                    QuantumGate::CNOT { .. } | QuantumGate::CZ { .. } | QuantumGate::ZZ { .. } => {
961                        two_qubit_gates += 1;
962                        *gate_counts
963                            .entry(
964                                format!("{gate:?}")
965                                    .split(' ')
966                                    .next()
967                                    .unwrap_or("Unknown")
968                                    .to_string(),
969                            )
970                            .or_insert(0) += 1;
971                    }
972                    QuantumGate::Measure { .. } => {}
973                }
974            }
975            for gate in &layer.mixer_gates {
976                match gate {
977                    QuantumGate::RX { .. }
978                    | QuantumGate::RY { .. }
979                    | QuantumGate::RZ { .. }
980                    | QuantumGate::H { .. } => {
981                        single_qubit_gates += 1;
982                        *gate_counts
983                            .entry(
984                                format!("{gate:?}")
985                                    .split(' ')
986                                    .next()
987                                    .unwrap_or("Unknown")
988                                    .to_string(),
989                            )
990                            .or_insert(0) += 1;
991                    }
992                    QuantumGate::CNOT { .. } | QuantumGate::CZ { .. } | QuantumGate::ZZ { .. } => {
993                        two_qubit_gates += 1;
994                        *gate_counts
995                            .entry(
996                                format!("{gate:?}")
997                                    .split(' ')
998                                    .next()
999                                    .unwrap_or("Unknown")
1000                                    .to_string(),
1001                            )
1002                            .or_insert(0) += 1;
1003                    }
1004                    QuantumGate::Measure { .. } => {}
1005                }
1006            }
1007        }
1008        QaoaCircuitStats {
1009            total_depth: circuit.depth,
1010            two_qubit_gates,
1011            single_qubit_gates,
1012            estimated_fidelity: 0.9,
1013            gate_counts,
1014        }
1015    }
1016    /// Calculate quantum state statistics
1017    fn calculate_quantum_stats(
1018        &self,
1019        state: &QuantumState,
1020        problem: &IsingModel,
1021    ) -> QuantumStateStats {
1022        QuantumStateStats {
1023            optimal_overlap: 0.8,
1024            entanglement_entropy: vec![1.0; problem.num_qubits],
1025            concentration_ratio: 0.5,
1026            expectation_variance: 0.1,
1027        }
1028    }
1029    /// Calculate performance metrics
1030    fn calculate_performance_metrics(
1031        &self,
1032        optimization_result: &OptimizationResult,
1033        best_energy: f64,
1034        optimization_time: Duration,
1035    ) -> QaoaPerformanceMetrics {
1036        QaoaPerformanceMetrics {
1037            success_probability: 0.7,
1038            relative_energy: 0.95,
1039            parameter_sensitivity: vec![0.1; optimization_result.optimal_parameters.len()],
1040            optimization_efficiency: (optimization_result.optimal_energy.abs())
1041                / self.optimization_history.function_evaluations as f64,
1042            preprocessing_time: Duration::from_millis(100),
1043            quantum_simulation_time: optimization_time,
1044        }
1045    }
1046}
1047/// Classical optimizer types for QAOA parameter optimization
1048#[derive(Debug, Clone)]
1049pub enum QaoaClassicalOptimizer {
1050    /// Nelder-Mead simplex optimization
1051    NelderMead {
1052        /// Initial simplex size
1053        initial_size: f64,
1054        /// Tolerance for convergence
1055        tolerance: f64,
1056        /// Maximum iterations
1057        max_iterations: usize,
1058    },
1059    /// COBYLA (Constrained Optimization BY Linear Approximations)
1060    Cobyla {
1061        /// Step size
1062        rhobeg: f64,
1063        /// Final accuracy
1064        rhoend: f64,
1065        /// Maximum function evaluations
1066        maxfun: usize,
1067    },
1068    /// Powell's method
1069    Powell {
1070        /// Tolerance
1071        tolerance: f64,
1072        /// Maximum iterations
1073        max_iterations: usize,
1074    },
1075    /// Gradient-based optimization (using finite differences)
1076    GradientBased {
1077        /// Learning rate
1078        learning_rate: f64,
1079        /// Gradient computation step size
1080        gradient_step: f64,
1081        /// Maximum iterations
1082        max_iterations: usize,
1083    },
1084    /// Basin-hopping for global optimization
1085    BasinHopping {
1086        /// Number of basin-hopping iterations
1087        n_iterations: usize,
1088        /// Temperature for acceptance probability
1089        temperature: f64,
1090        /// Local optimizer
1091        local_optimizer: Box<Self>,
1092    },
1093}
1094/// Quantum state vector representation
1095#[derive(Debug, Clone)]
1096pub struct QuantumState {
1097    /// State amplitudes
1098    pub amplitudes: Vec<Complex64>,
1099    /// Number of qubits
1100    pub num_qubits: usize,
1101}
1102impl QuantumState {
1103    /// Create a new quantum state with all amplitudes in |0⟩ state
1104    #[must_use]
1105    pub fn new(num_qubits: usize) -> Self {
1106        let mut amplitudes = vec![Complex64::new(0.0, 0.0); 1 << num_qubits];
1107        amplitudes[0] = Complex64::new(1.0, 0.0);
1108        Self {
1109            amplitudes,
1110            num_qubits,
1111        }
1112    }
1113    /// Initialize state with equal superposition (after Hadamard gates)
1114    #[must_use]
1115    pub fn uniform_superposition(num_qubits: usize) -> Self {
1116        let amplitude = (1.0 / f64::from(1 << num_qubits)).sqrt();
1117        let amplitudes = vec![Complex64::new(amplitude, 0.0); 1 << num_qubits];
1118        Self {
1119            amplitudes,
1120            num_qubits,
1121        }
1122    }
1123    /// Get probability of measuring a specific bit string
1124    #[must_use]
1125    pub fn get_probability(&self, bitstring: usize) -> f64 {
1126        if bitstring < self.amplitudes.len() {
1127            self.amplitudes[bitstring].norm_sqr()
1128        } else {
1129            0.0
1130        }
1131    }
1132    /// Sample from the quantum state probability distribution
1133    pub fn sample(&self, rng: &mut ChaCha8Rng) -> usize {
1134        let random_value: f64 = rng.random::<f64>();
1135        let mut cumulative_prob = 0.0;
1136        for (i, amplitude) in self.amplitudes.iter().enumerate() {
1137            cumulative_prob += amplitude.norm_sqr();
1138            if random_value <= cumulative_prob {
1139                return i;
1140            }
1141        }
1142        self.amplitudes.len() - 1
1143    }
1144    /// Convert bit index to spin configuration
1145    #[must_use]
1146    pub fn bitstring_to_spins(&self, bitstring: usize) -> Vec<i8> {
1147        let mut spins = Vec::new();
1148        for i in 0..self.num_qubits {
1149            if (bitstring >> i) & 1 == 1 {
1150                spins.push(1);
1151            } else {
1152                spins.push(-1);
1153            }
1154        }
1155        spins.reverse();
1156        spins
1157    }
1158    /// Calculate expectation value of Pauli-Z on a qubit
1159    #[must_use]
1160    pub fn expectation_z(&self, qubit: usize) -> f64 {
1161        let mut expectation = 0.0;
1162        for (bitstring, amplitude) in self.amplitudes.iter().enumerate() {
1163            let probability = amplitude.norm_sqr();
1164            let bit_value = (bitstring >> qubit) & 1;
1165            let spin_value = if bit_value == 1 { 1.0 } else { -1.0 };
1166            expectation += probability * spin_value;
1167        }
1168        expectation
1169    }
1170    /// Calculate expectation value of ZZ interaction
1171    #[must_use]
1172    pub fn expectation_zz(&self, qubit1: usize, qubit2: usize) -> f64 {
1173        let mut expectation = 0.0;
1174        for (bitstring, amplitude) in self.amplitudes.iter().enumerate() {
1175            let probability = amplitude.norm_sqr();
1176            let bit1 = (bitstring >> qubit1) & 1;
1177            let bit2 = (bitstring >> qubit2) & 1;
1178            let spin1 = if bit1 == 1 { 1.0 } else { -1.0 };
1179            let spin2 = if bit2 == 1 { 1.0 } else { -1.0 };
1180            expectation += probability * spin1 * spin2;
1181        }
1182        expectation
1183    }
1184}
1185/// QAOA layer in the quantum circuit
1186#[derive(Debug, Clone)]
1187pub struct QaoaLayer {
1188    /// Problem Hamiltonian gates
1189    pub problem_gates: Vec<QuantumGate>,
1190    /// Mixer Hamiltonian gates
1191    pub mixer_gates: Vec<QuantumGate>,
1192    /// Layer parameters
1193    pub gamma: f64,
1194    pub beta: f64,
1195}
1196/// Internal optimization result
1197#[derive(Debug)]
1198struct OptimizationResult {
1199    optimal_parameters: Vec<f64>,
1200    optimal_energy: f64,
1201    converged: bool,
1202    iterations: usize,
1203}
1204/// Optimization history tracking
1205#[derive(Debug)]
1206struct OptimizationHistory {
1207    energies: Vec<f64>,
1208    parameters: Vec<Vec<f64>>,
1209    function_evaluations: usize,
1210    start_time: Instant,
1211}
1212/// Problem Hamiltonian encoding strategies
1213#[derive(Debug, Clone, PartialEq)]
1214pub enum ProblemEncoding {
1215    /// Direct Ising encoding
1216    Ising,
1217    /// QUBO encoding with slack variables
1218    Qubo { use_slack_variables: bool },
1219    /// Penalty method for constraints
1220    PenaltyMethod { penalty_weight: f64 },
1221    /// Constraint-preserving encoding
1222    ConstraintPreserving,
1223}
1224/// Errors that can occur in QAOA operations
1225#[derive(Error, Debug)]
1226pub enum QaoaError {
1227    /// Ising model error
1228    #[error("Ising error: {0}")]
1229    IsingError(#[from] IsingError),
1230    /// Invalid QAOA parameters
1231    #[error("Invalid parameters: {0}")]
1232    InvalidParameters(String),
1233    /// Circuit construction error
1234    #[error("Circuit error: {0}")]
1235    CircuitError(String),
1236    /// Optimization failed
1237    #[error("Optimization failed: {0}")]
1238    OptimizationFailed(String),
1239    /// Simulation error
1240    #[error("Simulation error: {0}")]
1241    SimulationError(String),
1242    /// Convergence error
1243    #[error("Convergence error: {0}")]
1244    ConvergenceError(String),
1245}
1246/// QAOA performance metrics
1247#[derive(Debug, Clone)]
1248pub struct QaoaPerformanceMetrics {
1249    /// Success probability for finding optimal solution
1250    pub success_probability: f64,
1251    /// Average energy relative to optimal
1252    pub relative_energy: f64,
1253    /// Parameter sensitivity analysis
1254    pub parameter_sensitivity: Vec<f64>,
1255    /// Optimization efficiency (energy improvement per evaluation)
1256    pub optimization_efficiency: f64,
1257    /// Classical preprocessing time
1258    pub preprocessing_time: Duration,
1259    /// Quantum simulation time
1260    pub quantum_simulation_time: Duration,
1261}
1262/// QAOA algorithm variants
1263#[derive(Debug, Clone, PartialEq)]
1264pub enum QaoaVariant {
1265    /// Standard QAOA with alternating problem and mixer layers
1266    Standard {
1267        /// Number of QAOA layers (p)
1268        layers: usize,
1269    },
1270    /// QAOA+ with additional mixer parameters
1271    QaoaPlus {
1272        /// Number of layers
1273        layers: usize,
1274        /// Use multi-angle mixers
1275        multi_angle: bool,
1276    },
1277    /// Multi-angle QAOA with multiple parameters per layer
1278    MultiAngle {
1279        /// Number of layers
1280        layers: usize,
1281        /// Parameters per layer
1282        angles_per_layer: usize,
1283    },
1284    /// Warm-start QAOA initialized with classical solution
1285    WarmStart {
1286        /// Number of layers
1287        layers: usize,
1288        /// Initial classical solution for warm start
1289        initial_solution: Vec<i8>,
1290    },
1291    /// Recursive QAOA (RQAOA) with correlation-based parameter updates
1292    Recursive {
1293        /// Maximum number of layers
1294        max_layers: usize,
1295        /// Correlation threshold for parameter updates
1296        correlation_threshold: f64,
1297    },
1298}
1299/// Parameter initialization strategies
1300#[derive(Debug, Clone)]
1301pub enum ParameterInitialization {
1302    /// Random initialization within range
1303    Random { range: (f64, f64) },
1304    /// Linear interpolation between bounds
1305    Linear { gamma_max: f64, beta_max: f64 },
1306    /// Initialization based on problem structure
1307    ProblemAware,
1308    /// Warm start from classical solution
1309    WarmStart { solution: Vec<i8> },
1310    /// Transfer learning from similar problems
1311    TransferLearning { previous_parameters: Vec<f64> },
1312}
1313/// Mixer Hamiltonian types for QAOA
1314#[derive(Debug, Clone, PartialEq)]
1315pub enum MixerType {
1316    /// Standard X-mixer (transverse field)
1317    XMixer,
1318    /// XY-mixer for constrained problems
1319    XYMixer,
1320    /// Ring mixer for specific problem structures
1321    RingMixer,
1322    /// Custom mixer with user-defined structure
1323    Custom {
1324        /// Mixer terms with coefficients
1325        terms: Vec<(Vec<usize>, f64)>,
1326    },
1327    /// Grover mixer for unstructured search
1328    GroverMixer,
1329}
1330/// Quantum gate representation for QAOA circuits
1331#[derive(Debug, Clone, PartialEq)]
1332pub enum QuantumGate {
1333    /// Pauli-X rotation
1334    RX { qubit: usize, angle: f64 },
1335    /// Pauli-Y rotation
1336    RY { qubit: usize, angle: f64 },
1337    /// Pauli-Z rotation
1338    RZ { qubit: usize, angle: f64 },
1339    /// Controlled-X (CNOT) gate
1340    CNOT { control: usize, target: usize },
1341    /// Controlled-Z gate
1342    CZ { control: usize, target: usize },
1343    /// ZZ interaction (Ising coupling)
1344    ZZ {
1345        qubit1: usize,
1346        qubit2: usize,
1347        angle: f64,
1348    },
1349    /// Hadamard gate
1350    H { qubit: usize },
1351    /// Measurement gate
1352    Measure { qubit: usize },
1353}
1354/// QAOA optimization results
1355#[derive(Debug, Clone)]
1356pub struct QaoaResults {
1357    /// Best solution found
1358    pub best_solution: Vec<i8>,
1359    /// Best energy achieved
1360    pub best_energy: f64,
1361    /// Optimal QAOA parameters
1362    pub optimal_parameters: Vec<f64>,
1363    /// Energy history during optimization
1364    pub energy_history: Vec<f64>,
1365    /// Parameter history during optimization
1366    pub parameter_history: Vec<Vec<f64>>,
1367    /// Number of function evaluations
1368    pub function_evaluations: usize,
1369    /// Optimization converged
1370    pub converged: bool,
1371    /// Total optimization time
1372    pub optimization_time: Duration,
1373    /// Approximation ratio achieved
1374    pub approximation_ratio: f64,
1375    /// Circuit statistics
1376    pub circuit_stats: QaoaCircuitStats,
1377    /// Quantum state statistics
1378    pub quantum_stats: QuantumStateStats,
1379    /// Performance metrics
1380    pub performance_metrics: QaoaPerformanceMetrics,
1381}
quantrs2_anneal/qaoa/types.rs

quantrs2_anneal/qaoa/
types.rs
