comprehensive_benchmarking_demo/
comprehensive_benchmarking_demo.rs

1#![allow(clippy::pedantic, clippy::unnecessary_wraps)]
2//! Comprehensive Benchmarking Framework Example
3//!
4//! This example demonstrates the benchmarking framework for comparing quantum ML models
5//! across different algorithms, hardware backends, and problem sizes.
6
7use quantrs2_ml::benchmarking::algorithm_benchmarks::{QAOABenchmark, QNNBenchmark, VQEBenchmark};
8use quantrs2_ml::benchmarking::benchmark_utils::create_benchmark_backends;
9use quantrs2_ml::benchmarking::{Benchmark, BenchmarkConfig, BenchmarkFramework, BenchmarkResults};
10use quantrs2_ml::prelude::*;
11use quantrs2_ml::simulator_backends::Backend;
12use std::time::Duration;
13
14// Placeholder type for missing BenchmarkContext
15#[derive(Debug, Clone)]
16pub struct BenchmarkContext {
17    pub config: String,
18}
19
20impl Default for BenchmarkContext {
21    fn default() -> Self {
22        Self::new()
23    }
24}
25
26impl BenchmarkContext {
27    #[must_use]
28    pub fn new() -> Self {
29        Self {
30            config: "default".to_string(),
31        }
32    }
33}
34
35fn main() -> Result<()> {
36    println!("=== Comprehensive Quantum ML Benchmarking Demo ===\n");
37
38    // Step 1: Initialize benchmarking framework
39    println!("1. Initializing benchmarking framework...");
40
41    let config = BenchmarkConfig {
42        repetitions: 3,
43        warmup_runs: 1,
44        max_time_per_benchmark: 60.0, // 1 minute per benchmark
45        profile_memory: true,
46        analyze_convergence: true,
47        confidence_level: 0.95,
48        ..Default::default()
49    };
50
51    let mut framework = BenchmarkFramework::new().with_config(config);
52
53    println!("   - Framework initialized");
54    println!("   - Output directory: benchmark_results/");
55    println!("   - Repetitions per benchmark: 3");
56
57    // Step 2: Register benchmarks
58    println!("\n2. Registering benchmarks...");
59
60    // VQE benchmarks for different qubit counts
61    framework.register_benchmark("vqe_4q", Box::new(VQEBenchmark::new(4, 8)));
62    framework.register_benchmark("vqe_6q", Box::new(VQEBenchmark::new(6, 12)));
63    framework.register_benchmark("vqe_8q", Box::new(VQEBenchmark::new(8, 16)));
64
65    // QAOA benchmarks
66    framework.register_benchmark("qaoa_4q", Box::new(QAOABenchmark::new(4, 2, 8)));
67    framework.register_benchmark("qaoa_6q", Box::new(QAOABenchmark::new(6, 3, 12)));
68
69    // QNN benchmarks
70    framework.register_benchmark("qnn_4q", Box::new(QNNBenchmark::new(4, 2, 100)));
71    framework.register_benchmark("qnn_6q", Box::new(QNNBenchmark::new(6, 3, 100)));
72
73    println!("   - Registered 7 benchmarks total");
74
75    // Step 3: Create backend configurations
76    println!("\n3. Setting up backends...");
77
78    let backends = create_benchmark_backends();
79    let backend_refs: Vec<&Backend> = backends.iter().collect();
80
81    println!("   - Created {} backends", backends.len());
82    for backend in &backends {
83        println!("     - {}", backend.name());
84    }
85
86    // Step 4: Run all benchmarks
87    println!("\n4. Running all benchmarks...");
88
89    framework.run_all_benchmarks(&backend_refs)?;
90
91    println!("   - All benchmarks completed");
92
93    // Step 5: Generate and display report
94    println!("\n5. Generating benchmark report...");
95
96    let report = framework.generate_report();
97    println!("\n{}", report.to_string());
98
99    // Step 6: Print detailed results
100    println!("\n6. Detailed Results Analysis:");
101
102    // Get results again for analysis since we can't hold onto the reference
103    let results = framework.run_all_benchmarks(&backend_refs)?;
104    print_performance_summary(results);
105    print_scaling_analysis(results);
106    print_memory_analysis(results);
107
108    println!("\n=== Comprehensive Benchmarking Demo Complete ===");
109
110    Ok(())
111}
112
113fn print_performance_summary(results: &BenchmarkResults) {
114    println!("\n   Performance Summary:");
115    println!("   ===================");
116
117    // Print summaries for each benchmark
118    for (name, summary) in results.summaries() {
119        println!("   {name}:");
120        println!("     - Mean time: {:.3}s", summary.mean_time.as_secs_f64());
121        println!("     - Min time:  {:.3}s", summary.min_time.as_secs_f64());
122        println!("     - Max time:  {:.3}s", summary.max_time.as_secs_f64());
123        println!("     - Success rate: {:.1}%", summary.success_rate * 100.0);
124        if let Some(memory) = summary.mean_memory {
125            println!(
126                "     - Memory usage: {:.1} MB",
127                memory as f64 / 1024.0 / 1024.0
128            );
129        }
130        println!();
131    }
132}
133
134fn print_scaling_analysis(results: &BenchmarkResults) {
135    println!("   Scaling Analysis:");
136    println!("   =================");
137
138    // Group by algorithm type
139    let mut vqe_results = Vec::new();
140    let mut qaoa_results = Vec::new();
141    let mut qnn_results = Vec::new();
142
143    for (name, summary) in results.summaries() {
144        if name.starts_with("vqe_") {
145            vqe_results.push((name, summary));
146        } else if name.starts_with("qaoa_") {
147            qaoa_results.push((name, summary));
148        } else if name.starts_with("qnn_") {
149            qnn_results.push((name, summary));
150        }
151    }
152
153    // Analyze VQE scaling
154    if !vqe_results.is_empty() {
155        println!("   VQE Algorithm Scaling:");
156        vqe_results.sort_by_key(|(name, _)| (*name).clone());
157        for (name, summary) in vqe_results {
158            let qubits = extract_qubit_count(name);
159            println!(
160                "     - {} qubits: {:.3}s",
161                qubits,
162                summary.mean_time.as_secs_f64()
163            );
164        }
165        println!("     - Scaling trend: Exponential (as expected for VQE)");
166        println!();
167    }
168
169    // Analyze QAOA scaling
170    if !qaoa_results.is_empty() {
171        println!("   QAOA Algorithm Scaling:");
172        qaoa_results.sort_by_key(|(name, _)| (*name).clone());
173        for (name, summary) in qaoa_results {
174            let qubits = extract_qubit_count(name);
175            println!(
176                "     - {} qubits: {:.3}s",
177                qubits,
178                summary.mean_time.as_secs_f64()
179            );
180        }
181        println!("     - Scaling trend: Polynomial (as expected for QAOA)");
182        println!();
183    }
184
185    // Analyze QNN scaling
186    if !qnn_results.is_empty() {
187        println!("   QNN Algorithm Scaling:");
188        qnn_results.sort_by_key(|(name, _)| (*name).clone());
189        for (name, summary) in qnn_results {
190            let qubits = extract_qubit_count(name);
191            println!(
192                "     - {} qubits: {:.3}s",
193                qubits,
194                summary.mean_time.as_secs_f64()
195            );
196        }
197        println!("     - Scaling trend: Polynomial (training overhead)");
198        println!();
199    }
200}
201
202fn print_memory_analysis(results: &BenchmarkResults) {
203    println!("   Memory Usage Analysis:");
204    println!("   =====================");
205
206    let mut memory_data = Vec::new();
207    for (name, summary) in results.summaries() {
208        if let Some(memory) = summary.mean_memory {
209            let qubits = extract_qubit_count(name);
210            memory_data.push((qubits, memory, name));
211        }
212    }
213
214    if !memory_data.is_empty() {
215        memory_data.sort_by_key(|(qubits, _, _)| *qubits);
216
217        println!("   Memory scaling by qubit count:");
218        for (qubits, memory, name) in memory_data {
219            println!(
220                "     - {} qubits ({}): {:.1} MB",
221                qubits,
222                name,
223                memory as f64 / 1024.0 / 1024.0
224            );
225        }
226        println!("     - Expected scaling: O(2^n) for statevector simulation");
227        println!();
228    }
229
230    // Print recommendations
231    println!("   Recommendations:");
232    println!("     - Use statevector backend for circuits ≤ 12 qubits");
233    println!("     - Use MPS backend for larger circuits with limited entanglement");
234    println!("     - Consider circuit optimization for memory-constrained environments");
235}
236
237fn extract_qubit_count(benchmark_name: &str) -> usize {
238    // Extract number from strings like "vqe_4q_statevector", "qaoa_6q_mps", etc.
239    for part in benchmark_name.split('_') {
240        if part.ends_with('q') {
241            if let Ok(num) = part.trim_end_matches('q').parse::<usize>() {
242                return num;
243            }
244        }
245    }
246    0 // Default if not found
247}
248
249// Additional analysis functions
250fn analyze_backend_performance(results: &BenchmarkResults) {
251    println!("   Backend Performance Comparison:");
252    println!("   ==============================");
253
254    // Group results by backend type
255    let mut backend_performance = std::collections::HashMap::new();
256
257    for (name, summary) in results.summaries() {
258        let backend_type = extract_backend_type(name);
259        backend_performance
260            .entry(backend_type)
261            .or_insert_with(Vec::new)
262            .push(summary.mean_time.as_secs_f64());
263    }
264
265    for (backend, times) in backend_performance {
266        let avg_time = times.iter().sum::<f64>() / times.len() as f64;
267        println!("     - {backend} backend: {avg_time:.3}s average");
268    }
269}
270
271fn extract_backend_type(benchmark_name: &str) -> &str {
272    if benchmark_name.contains("statevector") {
273        "Statevector"
274    } else if benchmark_name.contains("mps") {
275        "MPS"
276    } else if benchmark_name.contains("gpu") {
277        "GPU"
278    } else {
279        "Unknown"
280    }
281}
282
283// Test helper functions
284#[cfg(test)]
285mod tests {
286    use super::*;
287
288    #[test]
289    fn test_extract_qubit_count() {
290        assert_eq!(extract_qubit_count("vqe_4q_statevector"), 4);
291        assert_eq!(extract_qubit_count("qaoa_6q_mps"), 6);
292        assert_eq!(extract_qubit_count("qnn_8q_gpu"), 8);
293        assert_eq!(extract_qubit_count("unknown_format"), 0);
294    }
295
296    #[test]
297    fn test_extract_backend_type() {
298        assert_eq!(extract_backend_type("vqe_4q_statevector"), "Statevector");
299        assert_eq!(extract_backend_type("qaoa_6q_mps"), "MPS");
300        assert_eq!(extract_backend_type("qnn_8q_gpu"), "GPU");
301        assert_eq!(extract_backend_type("unknown_backend"), "Unknown");
302    }
303}
304
305// Placeholder functions to satisfy compilation errors
306fn create_algorithm_comparison_benchmarks() -> Result<Vec<Box<dyn Benchmark>>> {
307    let mut benchmarks = Vec::new();
308    Ok(benchmarks)
309}
310
311fn create_scaling_benchmarks() -> Result<Vec<Box<dyn Benchmark>>> {
312    let mut benchmarks = Vec::new();
313    Ok(benchmarks)
314}
315
316fn create_hardware_benchmarks() -> Result<Vec<Box<dyn Benchmark>>> {
317    let mut benchmarks = Vec::new();
318    Ok(benchmarks)
319}
320
321fn create_framework_benchmarks() -> Result<Vec<Box<dyn Benchmark>>> {
322    let mut benchmarks = Vec::new();
323    Ok(benchmarks)
324}
comprehensive_benchmarking_demo/comprehensive_benchmarking_demo.rs

comprehensive_benchmarking_demo/
comprehensive_benchmarking_demo.rs
