quantrs2_device/
hybrid_scirs2_algorithms.rs

1//! Hybrid Quantum-Classical Algorithms with SciRS2 Optimization
2//!
3//! This module provides comprehensive hybrid algorithm implementations that leverage
4//! SciRS2's advanced optimization capabilities for variational quantum algorithms,
5//! adaptive optimization, and hardware-efficient hybrid execution.
6
7use crate::{DeviceError, DeviceResult};
8use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
9use scirs2_core::random::prelude::*;
10use scirs2_core::Complex64;
11use scirs2_optimize::unconstrained::{minimize, Method, Options};
12
13/// Hybrid algorithm configuration using SciRS2 optimization
14#[derive(Debug, Clone)]
15pub struct HybridAlgorithmConfig {
16    /// Maximum number of iterations
17    pub max_iterations: usize,
18    /// Convergence tolerance
19    pub tolerance: f64,
20    /// Optimization method
21    pub optimization_method: OptimizationMethod,
22    /// Learning rate (for custom implementations)
23    pub learning_rate: f64,
24}
25
26impl Default for HybridAlgorithmConfig {
27    fn default() -> Self {
28        Self {
29            max_iterations: 1000,
30            tolerance: 1e-6,
31            optimization_method: OptimizationMethod::BFGS,
32            learning_rate: 0.01,
33        }
34    }
35}
36
37/// Optimization methods leveraging SciRS2
38#[derive(Debug, Clone, Copy, PartialEq, Eq)]
39pub enum OptimizationMethod {
40    /// BFGS quasi-Newton method
41    BFGS,
42    /// Nelder-Mead simplex method
43    NelderMead,
44    /// Conjugate gradient
45    ConjugateGradient,
46    /// Limited-memory BFGS
47    LBFGS,
48    /// Powell's method
49    Powell,
50}
51
52/// Result of hybrid algorithm optimization
53#[derive(Debug, Clone)]
54pub struct HybridOptimizationResult {
55    /// Optimal parameters found
56    pub optimal_parameters: Array1<f64>,
57    /// Final objective function value
58    pub optimal_value: f64,
59    /// Number of iterations performed
60    pub iterations: usize,
61    /// Convergence achieved
62    pub converged: bool,
63}
64
65/// Hybrid quantum-classical algorithm executor with SciRS2 optimization
66pub struct HybridAlgorithmExecutor {
67    config: HybridAlgorithmConfig,
68    /// Random number generator for stochastic methods
69    rng: StdRng,
70}
71
72impl HybridAlgorithmExecutor {
73    /// Create a new hybrid algorithm executor
74    pub fn new(config: HybridAlgorithmConfig) -> Self {
75        Self {
76            config,
77            rng: StdRng::seed_from_u64(42), // Reproducible RNG
78        }
79    }
80
81    /// Create executor with default configuration
82    pub fn default() -> Self {
83        Self::new(HybridAlgorithmConfig::default())
84    }
85
86    /// Optimize a quantum objective function using SciRS2 optimization
87    ///
88    /// # Arguments
89    /// * `objective` - Objective function to minimize (parameters -> value)
90    /// * `initial_params` - Starting point for optimization
91    ///
92    /// # Returns
93    /// Optimization result with optimal parameters and convergence information
94    pub fn optimize<F>(
95        &mut self,
96        mut objective: F,
97        initial_params: &Array1<f64>,
98    ) -> DeviceResult<HybridOptimizationResult>
99    where
100        F: FnMut(&ArrayView1<f64>) -> f64 + Clone,
101    {
102        let method = match self.config.optimization_method {
103            OptimizationMethod::BFGS => Method::BFGS,
104            OptimizationMethod::NelderMead => Method::NelderMead,
105            OptimizationMethod::ConjugateGradient => Method::CG,
106            OptimizationMethod::LBFGS => Method::LBFGS,
107            OptimizationMethod::Powell => Method::Powell,
108        };
109
110        let options = Options {
111            max_iter: self.config.max_iterations,
112            ftol: self.config.tolerance,
113            ..Default::default()
114        };
115
116        let x0_slice: Vec<f64> = initial_params.to_vec();
117
118        let result = minimize(objective, &x0_slice, method, Some(options))
119            .map_err(|e| DeviceError::OptimizationError(format!("Optimization failed: {}", e)))?;
120
121        Ok(HybridOptimizationResult {
122            optimal_parameters: result.x,
123            optimal_value: result.fun,
124            iterations: result.nit,
125            converged: result.success,
126        })
127    }
128
129    /// Generate initial parameters using SciRS2 random number generation
130    pub fn generate_initial_parameters(
131        &mut self,
132        dimension: usize,
133        range: (f64, f64),
134    ) -> Array1<f64> {
135        let (low, high) = range;
136        Array1::from_shape_fn(dimension, |_| low + (high - low) * self.rng.gen::<f64>())
137    }
138}
139
140/// Variational Quantum Eigensolver (VQE) implementation with SciRS2 optimization
141pub struct VQEWithSciRS2 {
142    executor: HybridAlgorithmExecutor,
143    /// Number of parameters in the ansatz
144    num_parameters: usize,
145}
146
147impl VQEWithSciRS2 {
148    /// Create a new VQE instance
149    pub fn new(config: HybridAlgorithmConfig, num_parameters: usize) -> Self {
150        Self {
151            executor: HybridAlgorithmExecutor::new(config),
152            num_parameters,
153        }
154    }
155
156    /// Run VQE optimization
157    ///
158    /// # Arguments
159    /// * `energy_function` - Function that computes energy given parameters
160    /// * `initial_params` - Starting parameters (if None, random initialization)
161    pub fn run<F>(
162        &mut self,
163        energy_function: F,
164        initial_params: Option<&Array1<f64>>,
165    ) -> DeviceResult<HybridOptimizationResult>
166    where
167        F: FnMut(&ArrayView1<f64>) -> f64 + Clone,
168    {
169        let params = if let Some(p) = initial_params {
170            p.clone()
171        } else {
172            self.executor.generate_initial_parameters(
173                self.num_parameters,
174                (-std::f64::consts::PI, std::f64::consts::PI),
175            )
176        };
177
178        self.executor.optimize(energy_function, &params)
179    }
180}
181
182/// Quantum Approximate Optimization Algorithm (QAOA) with SciRS2
183pub struct QAOAWithSciRS2 {
184    executor: HybridAlgorithmExecutor,
185    /// Number of QAOA layers (p)
186    num_layers: usize,
187}
188
189impl QAOAWithSciRS2 {
190    /// Create a new QAOA instance
191    pub fn new(config: HybridAlgorithmConfig, num_layers: usize) -> Self {
192        Self {
193            executor: HybridAlgorithmExecutor::new(config),
194            num_layers,
195        }
196    }
197
198    /// Run QAOA optimization
199    ///
200    /// # Arguments
201    /// * `cost_function` - Function that computes cost given parameters (2*p parameters: gamma and beta)
202    pub fn run<F>(&mut self, cost_function: F) -> DeviceResult<HybridOptimizationResult>
203    where
204        F: FnMut(&ArrayView1<f64>) -> f64 + Clone,
205    {
206        // QAOA has 2*p parameters (p gamma parameters, p beta parameters)
207        let num_params = 2 * self.num_layers;
208        let initial_params = self
209            .executor
210            .generate_initial_parameters(num_params, (0.0, 2.0 * std::f64::consts::PI));
211
212        self.executor.optimize(cost_function, &initial_params)
213    }
214}
215
216#[cfg(test)]
217mod tests {
218    use super::*;
219
220    #[test]
221    fn test_hybrid_executor_creation() {
222        let config = HybridAlgorithmConfig::default();
223        let executor = HybridAlgorithmExecutor::new(config);
224        assert_eq!(executor.config.max_iterations, 1000);
225    }
226
227    #[test]
228    fn test_bfgs_optimization() {
229        let config = HybridAlgorithmConfig {
230            optimization_method: OptimizationMethod::BFGS,
231            max_iterations: 100,
232            tolerance: 1e-6,
233            ..Default::default()
234        };
235
236        let mut executor = HybridAlgorithmExecutor::new(config);
237
238        // Minimize f(x) = (x - 2)^2
239        let objective = |x: &ArrayView1<f64>| (x[0] - 2.0).powi(2);
240
241        let initial = Array1::from(vec![0.0]);
242        let result = executor.optimize(objective, &initial);
243
244        assert!(result.is_ok());
245        let result = result.expect("Optimization failed");
246        assert!(result.converged);
247        assert!((result.optimal_parameters[0] - 2.0).abs() < 0.1);
248    }
249
250    #[test]
251    fn test_nelder_mead_optimization() {
252        let config = HybridAlgorithmConfig {
253            optimization_method: OptimizationMethod::NelderMead,
254            max_iterations: 500,
255            tolerance: 1e-6,
256            ..Default::default()
257        };
258
259        let mut executor = HybridAlgorithmExecutor::new(config);
260
261        // Minimize Rosenbrock function: f(x,y) = (1-x)^2 + 100(y-x^2)^2
262        let objective = |params: &ArrayView1<f64>| {
263            let x = params[0];
264            let y = params[1];
265            (1.0 - x).powi(2) + 100.0 * (y - x.powi(2)).powi(2)
266        };
267
268        let initial = Array1::from(vec![0.0, 0.0]);
269        let result = executor.optimize(objective, &initial);
270
271        assert!(result.is_ok());
272        let result = result.expect("Optimization failed");
273        // Nelder-Mead may not converge as tightly as gradient methods
274        assert!((result.optimal_parameters[0] - 1.0).abs() < 0.2);
275        assert!((result.optimal_parameters[1] - 1.0).abs() < 0.2);
276    }
277
278    #[test]
279    fn test_vqe_creation() {
280        let config = HybridAlgorithmConfig::default();
281        let vqe = VQEWithSciRS2::new(config, 4);
282        assert_eq!(vqe.num_parameters, 4);
283    }
284
285    #[test]
286    fn test_qaoa_creation() {
287        let config = HybridAlgorithmConfig::default();
288        let qaoa = QAOAWithSciRS2::new(config, 3);
289        assert_eq!(qaoa.num_layers, 3);
290    }
291
292    #[test]
293    fn test_random_parameter_generation() {
294        let config = HybridAlgorithmConfig::default();
295        let mut executor = HybridAlgorithmExecutor::new(config);
296
297        let params = executor.generate_initial_parameters(5, (-1.0, 1.0));
298        assert_eq!(params.len(), 5);
299
300        // All parameters should be in range [-1, 1]
301        for &p in params.iter() {
302            assert!((-1.0..=1.0).contains(&p));
303        }
304    }
305
306    #[test]
307    fn test_conjugate_gradient_optimization() {
308        let config = HybridAlgorithmConfig {
309            optimization_method: OptimizationMethod::ConjugateGradient,
310            max_iterations: 100,
311            tolerance: 1e-6,
312            ..Default::default()
313        };
314
315        let mut executor = HybridAlgorithmExecutor::new(config);
316
317        // Minimize f(x) = x^2 + y^2
318        let objective = |params: &ArrayView1<f64>| params[0].powi(2) + params[1].powi(2);
319
320        let initial = Array1::from(vec![5.0, 5.0]);
321        let result = executor.optimize(objective, &initial);
322
323        assert!(result.is_ok());
324        let result = result.expect("Optimization failed");
325        assert!(result.optimal_parameters[0].abs() < 0.1);
326        assert!(result.optimal_parameters[1].abs() < 0.1);
327    }
328
329    #[test]
330    fn test_vqe_run() {
331        let config = HybridAlgorithmConfig {
332            optimization_method: OptimizationMethod::BFGS,
333            max_iterations: 50,
334            ..Default::default()
335        };
336
337        let mut vqe = VQEWithSciRS2::new(config, 2);
338
339        // Simple energy function: E(theta) = (theta[0] - 1)^2 + (theta[1] - 2)^2
340        let energy =
341            |params: &ArrayView1<f64>| (params[0] - 1.0).powi(2) + (params[1] - 2.0).powi(2);
342
343        let result = vqe.run(energy, None);
344        assert!(result.is_ok());
345
346        let result = result.expect("VQE failed");
347        assert!((result.optimal_parameters[0] - 1.0).abs() < 0.1);
348        assert!((result.optimal_parameters[1] - 2.0).abs() < 0.1);
349    }
350}
quantrs2_device/hybrid_scirs2_algorithms.rs

quantrs2_device/
hybrid_scirs2_algorithms.rs
