quantrs2_device/vqa_support/
mod.rs

1//! Variational Quantum Algorithm (VQA) Support - Modular Implementation
2//!
3//! This module provides comprehensive support for variational quantum algorithms
4//! leveraging SciRS2's advanced optimization, statistical analysis, and machine learning capabilities
5//! for robust and efficient VQA execution on quantum hardware.
6//!
7//! The module is organized into focused submodules for maintainability and clarity:
8//! - `config`: Configuration structures and enums for all VQA types
9//! - `optimization`: Optimization algorithms and strategies
10//! - `statistical`: Statistical analysis and validation tools
11//! - `hardware`: Hardware-aware optimization and calibration
12//! - `noise`: Noise mitigation and error correction
13//! - `circuits`: Parametric circuit definitions and execution
14//! - `objectives`: Objective function definitions and evaluation
15//! - `executor`: Main VQA execution coordinator
16//! - `analysis`: Performance analysis and validation
17
18pub mod analysis;
19pub mod circuits;
20pub mod config;
21pub mod executor;
22pub mod hardware;
23pub mod noise;
24pub mod objectives;
25pub mod optimization;
26pub mod statistical;
27
28// Re-export main types for backward compatibility
29pub use analysis::*;
30pub use circuits::*;
31pub use config::{
32    AdaptiveShotConfig, ConvergenceCriterion, GradientMethod, MultiStartConfig, VQAAlgorithmType,
33    VQAConfig, VQAHardwareConfig, VQANoiseMitigation, VQAOptimizationConfig, VQAOptimizer,
34    VQAStatisticalConfig, VQAValidationConfig,
35}; // Selective re-export to avoid VQAResult conflicts
36pub use executor::{VQAExecutor, VQAExecutorConfig, VQAResult}; // VQAResult from executor only
37pub use hardware::*;
38pub use noise::*;
39pub use objectives::*;
40pub use optimization::*;
41pub use statistical::*;
42
43// Convenient type aliases
44pub type DeviceResult<T> = crate::DeviceResult<T>;
45pub type DeviceError = crate::DeviceError;
46
47// Feature-gated SciRS2 imports (re-exported for use in submodules)
48#[cfg(feature = "scirs2")]
49pub use scirs2_graph;
50#[cfg(feature = "scirs2")]
51pub use scirs2_linalg;
52#[cfg(feature = "scirs2")]
53pub use scirs2_optimize;
54#[cfg(feature = "scirs2")]
55pub use scirs2_stats;
56
57// Fallback implementations when SciRS2 is not available
58#[cfg(not(feature = "scirs2"))]
59pub mod fallback_scirs2 {
60    use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
61
62    pub fn mean(_data: &ArrayView1<f64>) -> Result<f64, String> {
63        Ok(0.0)
64    }
65    pub fn std(_data: &ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
66        Ok(1.0)
67    }
68    pub fn var(_data: &ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
69        Ok(1.0)
70    }
71    pub fn pearsonr(
72        _x: &ArrayView1<f64>,
73        _y: &ArrayView1<f64>,
74        _alt: &str,
75    ) -> Result<(f64, f64), String> {
76        Ok((0.0, 0.5))
77    }
78    pub fn trace(_matrix: &ArrayView2<f64>) -> Result<f64, String> {
79        Ok(1.0)
80    }
81    pub fn inv(_matrix: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
82        Ok(Array2::eye(2))
83    }
84
85    pub struct OptimizeResult {
86        pub x: Array1<f64>,
87        pub fun: f64,
88        pub success: bool,
89        pub nit: usize,
90        pub nfev: usize,
91        pub message: String,
92    }
93
94    pub fn minimize(
95        _func: fn(&Array1<f64>) -> f64,
96        _x0: &Array1<f64>,
97        _method: &str,
98    ) -> Result<OptimizeResult, String> {
99        Ok(OptimizeResult {
100            x: Array1::zeros(2),
101            fun: 0.0,
102            success: true,
103            nit: 0,
104            nfev: 0,
105            message: "Fallback optimization".to_string(),
106        })
107    }
108
109    pub fn differential_evolution(
110        _func: fn(&Array1<f64>) -> f64,
111        _bounds: &[(f64, f64)],
112    ) -> Result<OptimizeResult, String> {
113        Ok(OptimizeResult {
114            x: Array1::zeros(2),
115            fun: 0.0,
116            success: true,
117            nit: 0,
118            nfev: 0,
119            message: "Fallback optimization".to_string(),
120        })
121    }
122}
123
124#[cfg(not(feature = "scirs2"))]
125pub use fallback_scirs2::*;
126
127/// Main VQA execution entry point — synchronous gradient-descent loop.
128///
129/// Uses the parameter-shift gradient via `objective_function.compute_gradient`
130/// with a fixed step size of 0.01. Runs for `config.optimization_config.max_iterations`
131/// steps and reports convergence when the objective change falls below `config.optimization_config.convergence_tolerance`.
132pub fn execute_vqa(
133    config: VQAConfig,
134    ansatz: &circuits::ParametricCircuit,
135    objective_function: &dyn ObjectiveFunction,
136) -> DeviceResult<VQAResult> {
137    use scirs2_core::ndarray::Array1;
138    use std::time::Instant;
139
140    let max_iter = config.optimization_config.max_iterations;
141    let tol = config.optimization_config.convergence_tolerance;
142    let step = 0.01_f64;
143
144    let mut params = Array1::from_vec(ansatz.parameters.clone());
145    let mut best_value = f64::INFINITY;
146    let mut best_params = params.clone();
147    let mut history = Vec::with_capacity(max_iter);
148    let mut converged = false;
149    let start = Instant::now();
150
151    for _ in 0..max_iter {
152        let result = objective_function.evaluate(&params)?;
153        history.push(result.value);
154        if result.value < best_value {
155            best_value = result.value;
156            best_params = params.clone();
157        }
158        if history.len() > 1 {
159            let prev = history[history.len() - 2];
160            if (prev - result.value).abs() < tol {
161                converged = true;
162                break;
163            }
164        }
165        // Gradient descent step.
166        let grad = objective_function.compute_gradient(&params)?;
167        params = params - grad * step;
168    }
169
170    let statistics = statistical::analyze_convergence(&history);
171    Ok(VQAResult {
172        optimal_parameters: best_params.to_vec(),
173        best_value,
174        iterations: history.len(),
175        execution_time: start.elapsed(),
176        converged,
177        statistics,
178        history,
179    })
180}
181
182/// Create VQA configuration for VQE algorithm
183pub fn vqe_config() -> VQAConfig {
184    VQAConfig::new(VQAAlgorithmType::VQE)
185}
186
187/// Create VQA configuration for QAOA algorithm
188pub fn qaoa_config(num_layers: usize) -> VQAConfig {
189    let mut config = VQAConfig::new(VQAAlgorithmType::QAOA);
190    config.optimization_config.max_iterations = num_layers * 100;
191    config
192}
quantrs2_device/vqa_support/mod.rs

quantrs2_device/vqa_support/
mod.rs
