quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71

//! Learning strategies for quantum continual learning

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2};
use std::collections::HashMap;

use super::config::{ContinualLearningStrategy as StrategyType, QuantumContinualLearningConfig, ContinualTask};

/// Continual learning strategy trait
pub trait ContinualLearningStrategy: std::fmt::Debug {
    fn learn_task(
        &mut self,
        task: &ContinualTask,
        data: &Array2<f64>,
        labels: &Array1<i32>,
        model_parameters: &mut HashMap<String, Array1<f64>>,
    ) -> Result<()>;

    fn consolidate_knowledge(&mut self, model_parameters: &mut HashMap<String, Array1<f64>>) -> Result<()>;
}

/// Elastic Weight Consolidation implementation
#[derive(Debug)]
pub struct EWCStrategy {
    lambda: f64,
    fisher_information: HashMap<String, Array1<f64>>,
    optimal_parameters: HashMap<String, Array1<f64>>,
}

impl EWCStrategy {
    pub fn new(lambda: f64) -> Self {
        Self {
            lambda,
            fisher_information: HashMap::new(),
            optimal_parameters: HashMap::new(),
        }
    }
}

impl ContinualLearningStrategy for EWCStrategy {
    fn learn_task(
        &mut self,
        task: &ContinualTask,
        data: &Array2<f64>,
        labels: &Array1<i32>,
        model_parameters: &mut HashMap<String, Array1<f64>>,
    ) -> Result<()> {
        // Placeholder EWC implementation
        let param_key = format!("task_{}", task.task_id);
        let new_params = Array1::zeros(data.ncols());
        model_parameters.insert(param_key.clone(), new_params.clone());
        self.optimal_parameters.insert(param_key, new_params);
        Ok(())
    }

    fn consolidate_knowledge(&mut self, _model_parameters: &mut HashMap<String, Array1<f64>>) -> Result<()> {
        // Placeholder consolidation
        Ok(())
    }
}

/// Create learning strategy based on type
pub fn create_learning_strategy(
    strategy_type: StrategyType,
    config: &QuantumContinualLearningConfig,
) -> Result<Box<dyn ContinualLearningStrategy>> {
    match strategy_type {
        StrategyType::EWC => Ok(Box::new(EWCStrategy::new(config.regularization_strength))),
        _ => Ok(Box::new(EWCStrategy::new(config.regularization_strength))), // Placeholder for others
    }
}