oxirs_stream/quantum_processing/
classical_processor.rs

1//! Classical processor for hybrid quantum-classical operations
2
3use anyhow::Result;
4use std::collections::HashMap;
5
6use super::QuantumProcessingResult;
7use crate::error::StreamResult;
8use crate::event::StreamEvent;
9
10/// Classical processor for hybrid operations
11pub struct ClassicalProcessor {
12    optimization_algorithms: Vec<ClassicalOptimizer>,
13    ml_models: HashMap<String, ClassicalMLModel>,
14    preprocessing_pipelines: Vec<PreprocessingPipeline>,
15    postprocessing_pipelines: Vec<PostprocessingPipeline>,
16}
17
18impl Default for ClassicalProcessor {
19    fn default() -> Self {
20        Self::new()
21    }
22}
23
24impl ClassicalProcessor {
25    pub fn new() -> Self {
26        Self {
27            optimization_algorithms: vec![ClassicalOptimizer::Adam, ClassicalOptimizer::BFGS],
28            ml_models: HashMap::new(),
29            preprocessing_pipelines: Vec::new(),
30            postprocessing_pipelines: Vec::new(),
31        }
32    }
33
34    pub async fn preprocess_event(&self, event: &StreamEvent) -> Result<StreamEvent> {
35        // Classical preprocessing logic
36        Ok(event.clone())
37    }
38
39    pub async fn postprocess_result(
40        &self,
41        _quantum_result: QuantumProcessingResult,
42        processed_event: StreamEvent,
43    ) -> StreamResult<StreamEvent> {
44        // Classical postprocessing logic
45        // For now, return the processed event as-is
46        Ok(processed_event)
47    }
48}
49
50/// Classical optimization algorithms
51#[derive(Debug, Clone)]
52pub enum ClassicalOptimizer {
53    GradientDescent,
54    Adam,
55    BFGS,
56    NelderMead,
57    SimulatedAnnealing,
58    GeneticAlgorithm,
59    ParticleSwarm,
60    BayesianOptimization,
61}
62
63/// Classical ML models
64#[derive(Debug, Clone)]
65pub struct ClassicalMLModel {
66    pub model_type: ClassicalMLType,
67    pub parameters: Vec<f64>,
68    pub training_accuracy: f64,
69    pub inference_time_ms: f64,
70}
71
72/// Classical ML model types
73#[derive(Debug, Clone)]
74pub enum ClassicalMLType {
75    LinearRegression,
76    LogisticRegression,
77    RandomForest,
78    SVM,
79    NeuralNetwork,
80    DecisionTree,
81    KMeans,
82    PCA,
83}
84
85/// Preprocessing pipeline
86#[derive(Debug, Clone)]
87pub struct PreprocessingPipeline {
88    pub steps: Vec<PreprocessingStep>,
89}
90
91/// Preprocessing steps
92#[derive(Debug, Clone)]
93pub enum PreprocessingStep {
94    Normalization,
95    Standardization,
96    FeatureSelection,
97    DimensionalityReduction,
98    NoiseFiltering,
99}
100
101/// Postprocessing pipeline
102#[derive(Debug, Clone)]
103pub struct PostprocessingPipeline {
104    pub steps: Vec<PostprocessingStep>,
105}
106
107/// Postprocessing steps
108#[derive(Debug, Clone)]
109pub enum PostprocessingStep {
110    ResultValidation,
111    ErrorCorrection,
112    StatisticalAnalysis,
113    Visualization,
114}
oxirs_stream/quantum_processing/classical_processor.rs

oxirs_stream/quantum_processing/
classical_processor.rs
