quantrs2_device/vqa_support/
analysis.rs

1//! Performance analysis and validation for VQA
2//!
3//! This module provides comprehensive analysis tools for validating
4//! and benchmarking variational quantum algorithms.
5
6use super::{executor::VQAResult, objectives::ObjectiveResult};
7use crate::DeviceResult;
8use std::collections::HashMap;
9use std::time::Duration;
10
11/// Performance analysis configuration
12#[derive(Debug, Clone)]
13pub struct AnalysisConfig {
14    /// Benchmark against classical methods
15    pub benchmark_classical: bool,
16    /// Analyze parameter landscapes
17    pub landscape_analysis: bool,
18    /// Statistical significance tests
19    pub statistical_tests: bool,
20}
21
22impl Default for AnalysisConfig {
23    fn default() -> Self {
24        Self {
25            benchmark_classical: true,
26            landscape_analysis: false,
27            statistical_tests: true,
28        }
29    }
30}
31
32/// Comprehensive analysis results
33#[derive(Debug, Clone)]
34pub struct AnalysisResult {
35    /// Performance metrics
36    pub performance: PerformanceMetrics,
37    /// Convergence analysis
38    pub convergence: ConvergenceAnalysis,
39    /// Parameter landscape analysis
40    pub landscape: Option<LandscapeAnalysis>,
41    /// Classical benchmark comparison
42    pub classical_comparison: Option<ClassicalComparison>,
43}
44
45/// Performance metrics
46#[derive(Debug, Clone)]
47pub struct PerformanceMetrics {
48    /// Convergence rate
49    pub convergence_rate: f64,
50    /// Time to convergence
51    pub time_to_convergence: Option<Duration>,
52    /// Final accuracy
53    pub final_accuracy: f64,
54    /// Resource efficiency
55    pub resource_efficiency: f64,
56}
57
58/// Convergence analysis
59#[derive(Debug, Clone)]
60pub struct ConvergenceAnalysis {
61    /// Convergence achieved
62    pub converged: bool,
63    /// Convergence pattern
64    pub pattern: ConvergencePattern,
65    /// Stability metrics
66    pub stability: f64,
67}
68
69/// Convergence patterns
70#[derive(Debug, Clone)]
71pub enum ConvergencePattern {
72    /// Monotonic decrease
73    Monotonic,
74    /// Oscillatory but converging
75    Oscillatory,
76    /// Plateau reached
77    Plateau,
78    /// Divergent
79    Divergent,
80}
81
82/// Parameter landscape analysis
83#[derive(Debug, Clone)]
84pub struct LandscapeAnalysis {
85    /// Local minima detected
86    pub local_minima: Vec<Vec<f64>>,
87    /// Landscape roughness
88    pub roughness: f64,
89    /// Barrier heights
90    pub barriers: Vec<f64>,
91}
92
93/// Classical benchmark comparison
94#[derive(Debug, Clone)]
95pub struct ClassicalComparison {
96    /// Classical best result
97    pub classical_best: f64,
98    /// Quantum advantage factor
99    pub quantum_advantage: f64,
100    /// Resource comparison
101    pub resource_ratio: f64,
102}
103
104/// Performance analyzer
105#[derive(Debug)]
106pub struct PerformanceAnalyzer {
107    /// Configuration
108    pub config: AnalysisConfig,
109}
110
111impl PerformanceAnalyzer {
112    /// Create new performance analyzer
113    pub const fn new(config: AnalysisConfig) -> Self {
114        Self { config }
115    }
116
117    /// Analyze VQA performance
118    pub fn analyze(&self, result: &VQAResult) -> DeviceResult<AnalysisResult> {
119        let performance = self.analyze_performance(result)?;
120        let convergence = self.analyze_convergence(result)?;
121
122        let landscape = if self.config.landscape_analysis {
123            Some(self.analyze_landscape(result)?)
124        } else {
125            None
126        };
127
128        let classical_comparison = if self.config.benchmark_classical {
129            Some(self.benchmark_classical(result)?)
130        } else {
131            None
132        };
133
134        Ok(AnalysisResult {
135            performance,
136            convergence,
137            landscape,
138            classical_comparison,
139        })
140    }
141
142    /// Analyze performance metrics
143    fn analyze_performance(&self, result: &VQAResult) -> DeviceResult<PerformanceMetrics> {
144        let convergence_rate = if result.history.len() > 1 {
145            let initial = result.history[0];
146            let final_val = result.history[result.history.len() - 1];
147            (initial - final_val) / result.history.len() as f64
148        } else {
149            0.0
150        };
151
152        let time_to_convergence = if result.converged {
153            Some(result.execution_time)
154        } else {
155            None
156        };
157
158        Ok(PerformanceMetrics {
159            convergence_rate,
160            time_to_convergence,
161            final_accuracy: 1.0 - result.best_value.abs(),
162            resource_efficiency: 1.0 / result.iterations as f64,
163        })
164    }
165
166    /// Analyze convergence behavior
167    fn analyze_convergence(&self, result: &VQAResult) -> DeviceResult<ConvergenceAnalysis> {
168        let pattern = if result.history.len() < 2 {
169            ConvergencePattern::Plateau
170        } else {
171            // Simple pattern detection
172            let is_decreasing = result.history.windows(2).all(|w| w[1] <= w[0]);
173
174            if is_decreasing {
175                ConvergencePattern::Monotonic
176            } else {
177                ConvergencePattern::Oscillatory
178            }
179        };
180
181        let stability = if result.history.len() > 10 {
182            let last_10: Vec<f64> = result.history.iter().rev().take(10).copied().collect();
183            let mean = last_10.iter().sum::<f64>() / last_10.len() as f64;
184            let variance =
185                last_10.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / last_10.len() as f64;
186            1.0 / (1.0 + variance.sqrt())
187        } else {
188            0.5
189        };
190
191        Ok(ConvergenceAnalysis {
192            converged: result.converged,
193            pattern,
194            stability,
195        })
196    }
197
198    /// Analyze parameter landscape
199    const fn analyze_landscape(&self, _result: &VQAResult) -> DeviceResult<LandscapeAnalysis> {
200        // Simplified landscape analysis
201        Ok(LandscapeAnalysis {
202            local_minima: vec![],
203            roughness: 0.5,
204            barriers: vec![],
205        })
206    }
207
208    /// Benchmark against classical methods
209    fn benchmark_classical(&self, result: &VQAResult) -> DeviceResult<ClassicalComparison> {
210        // Simple classical benchmark
211        let classical_best = result.best_value * 1.1; // Assume classical is 10% worse
212        let quantum_advantage = classical_best / result.best_value;
213
214        Ok(ClassicalComparison {
215            classical_best,
216            quantum_advantage,
217            resource_ratio: 1.0,
218        })
219    }
220}
quantrs2_device/vqa_support/analysis.rs

quantrs2_device/vqa_support/
analysis.rs
