quantrs2_device/vqa_support/
executor.rs

1//! Main VQA execution coordinator
2//!
3//! This module provides the main execution engine for variational
4//! quantum algorithms with comprehensive orchestration capabilities.
5
6use super::{
7    circuits::ParametricCircuit,
8    hardware::HardwareConfig,
9    noise::NoiseMitigationConfig,
10    objectives::{ObjectiveEvaluator, ObjectiveFunction, ObjectiveResult},
11    statistical::VQAStatistics,
12};
13use crate::DeviceResult;
14use scirs2_core::ndarray::Array1;
15use scirs2_core::random::prelude::*;
16use std::collections::HashMap;
17use std::time::{Duration, Instant};
18
19/// VQA execution configuration
20#[derive(Debug, Clone)]
21pub struct VQAExecutorConfig {
22    /// Maximum iterations
23    pub max_iterations: usize,
24    /// Convergence tolerance
25    pub tolerance: f64,
26    /// Hardware configuration
27    pub hardware: HardwareConfig,
28    /// Noise mitigation configuration
29    pub noise_mitigation: NoiseMitigationConfig,
30    /// Optimizer settings
31    pub optimizer: OptimizerConfig,
32}
33
34/// Optimizer configuration
35#[derive(Debug, Clone)]
36pub struct OptimizerConfig {
37    /// Optimizer type
38    pub optimizer_type: OptimizerType,
39    /// Learning rate
40    pub learning_rate: f64,
41    /// Additional parameters
42    pub parameters: HashMap<String, f64>,
43}
44
45/// Available optimizer types
46#[derive(Debug, Clone)]
47pub enum OptimizerType {
48    /// Gradient descent
49    GradientDescent,
50    /// Adam optimizer
51    Adam,
52    /// L-BFGS-B
53    LBFGSB,
54    /// COBYLA
55    COBYLA,
56}
57
58impl Default for VQAExecutorConfig {
59    fn default() -> Self {
60        Self {
61            max_iterations: 1000,
62            tolerance: 1e-6,
63            hardware: HardwareConfig::default(),
64            noise_mitigation: NoiseMitigationConfig::default(),
65            optimizer: OptimizerConfig::default(),
66        }
67    }
68}
69
70impl Default for OptimizerConfig {
71    fn default() -> Self {
72        Self {
73            optimizer_type: OptimizerType::Adam,
74            learning_rate: 0.01,
75            parameters: HashMap::new(),
76        }
77    }
78}
79
80/// VQA execution result
81#[derive(Debug, Clone)]
82pub struct VQAResult {
83    /// Optimal parameters found
84    pub optimal_parameters: Vec<f64>,
85    /// Best objective value achieved
86    pub best_value: f64,
87    /// Number of iterations performed
88    pub iterations: usize,
89    /// Execution time
90    pub execution_time: Duration,
91    /// Convergence achieved
92    pub converged: bool,
93    /// Statistical analysis
94    pub statistics: VQAStatistics,
95    /// Optimization history
96    pub history: Vec<f64>,
97}
98
99/// Main VQA executor
100#[derive(Debug)]
101pub struct VQAExecutor {
102    /// Configuration
103    pub config: VQAExecutorConfig,
104}
105
106impl VQAExecutor {
107    /// Create new VQA executor
108    pub fn new(
109        config: super::config::VQAConfig,
110        _calibration_manager: crate::calibration::CalibrationManager,
111        _device: Option<String>,
112    ) -> Self {
113        Self {
114            config: VQAExecutorConfig::default(),
115        }
116    }
117
118    /// Create new VQA executor with config
119    pub const fn with_config(config: VQAExecutorConfig) -> Self {
120        Self { config }
121    }
122
123    /// Execute VQA optimization
124    pub fn execute(
125        &self,
126        circuit: &mut ParametricCircuit,
127        objective: &ObjectiveEvaluator,
128    ) -> DeviceResult<VQAResult> {
129        let start_time = Instant::now();
130        let mut best_value = f64::INFINITY;
131        let mut best_params = circuit.parameters.clone();
132        let mut history = Vec::new();
133        let mut converged = false;
134
135        for iteration in 0..self.config.max_iterations {
136            // Evaluate objective
137            let result = objective.evaluate(&Array1::from_vec(circuit.parameters.clone()))?;
138            history.push(result.value);
139
140            // Update best if improved
141            if result.value < best_value {
142                best_value = result.value;
143                best_params.clone_from(&circuit.parameters);
144            }
145
146            // Check convergence
147            if result.value.abs() < self.config.tolerance {
148                converged = true;
149                break;
150            }
151
152            // Basic parameter update (simplified optimizer)
153            self.update_parameters(circuit, &result)?;
154        }
155
156        let execution_time = start_time.elapsed();
157        let statistics = super::statistical::analyze_convergence(&history);
158
159        Ok(VQAResult {
160            optimal_parameters: best_params,
161            best_value,
162            iterations: history.len(),
163            execution_time,
164            converged,
165            statistics,
166            history,
167        })
168    }
169
170    /// Update circuit parameters based on objective result
171    fn update_parameters(
172        &self,
173        circuit: &mut ParametricCircuit,
174        _result: &ObjectiveResult,
175    ) -> DeviceResult<()> {
176        // Simple random perturbation for demonstration
177        use scirs2_core::random::prelude::*;
178        let mut rng = thread_rng();
179
180        for param in &mut circuit.parameters {
181            *param += rng.gen_range(-0.1..0.1) * self.config.optimizer.learning_rate;
182        }
183
184        Ok(())
185    }
186}
quantrs2_device/vqa_support/executor.rs

quantrs2_device/vqa_support/
executor.rs
