quantrs2-anneal 0.1.3

Quantum annealing support for the QuantRS2 framework
Documentation 
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//! # MLPerformanceModel - Trait Implementations
//!
//! This module contains trait implementations for `MLPerformanceModel`.
//!
//! ## Implemented Traits
//!
//! - `PerformanceModel`
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::ising::{IsingError, IsingModel};
use std::collections::{HashMap, HashSet, VecDeque};

use super::functions::{HardwareCompilationResult, PerformanceModel};
use super::types::{
    ControlPrecision, CouplingRange, EmbeddingInfo, HardwareCharacteristics, MLPerformanceModel,
    PerformanceData, PerformanceMetrics, PerformancePrediction, QubitNoise, SensitivityAnalysis,
    TemperatureProfile,
};

impl PerformanceModel for MLPerformanceModel {
    fn predict_performance(
        &self,
        problem: &IsingModel,
        embedding: &EmbeddingInfo,
        hardware: &HardwareCharacteristics,
    ) -> HardwareCompilationResult<PerformancePrediction> {
        let features = self.extract_features(problem, embedding, hardware);
        let mut success_prob = 0.8;
        let mut solution_quality = 0.9;
        let mut time_to_solution = 1000.0;
        if features.len() >= 3 {
            success_prob *= 0.1f64.mul_add(-features[2].max(0.0).min(2.0), 1.0);
            solution_quality *= features[3].max(0.5).min(1.0);
            time_to_solution *= features[0] / 100.0;
        }
        let confidence_intervals = HashMap::from([
            (
                "success_probability".to_string(),
                (success_prob * 0.9, success_prob * 1.1),
            ),
            (
                "solution_quality".to_string(),
                (solution_quality * 0.95, solution_quality * 1.05),
            ),
            (
                "time_to_solution".to_string(),
                (time_to_solution * 0.8, time_to_solution * 1.2),
            ),
        ]);
        let sensitivity_analysis = SensitivityAnalysis {
            parameter_sensitivities: HashMap::from([
                ("chain_length".to_string(), 0.3),
                ("embedding_efficiency".to_string(), 0.4),
                ("problem_size".to_string(), 0.2),
            ]),
            noise_sensitivity: 0.1,
            temperature_sensitivity: 0.15,
            robustness_measures: HashMap::from([("overall_robustness".to_string(), 0.7)]),
        };
        Ok(PerformancePrediction {
            success_probability: success_prob,
            solution_quality,
            time_to_solution,
            confidence_intervals,
            sensitivity_analysis,
        })
    }
    fn update_model(
        &mut self,
        problem: &IsingModel,
        embedding: &EmbeddingInfo,
        actual_performance: &PerformanceData,
    ) -> HardwareCompilationResult<()> {
        self.training_data.push((
            self.extract_features(
                problem,
                embedding,
                &HardwareCharacteristics {
                    num_qubits: problem.num_qubits,
                    connectivity: vec![vec![false; problem.num_qubits]; problem.num_qubits],
                    qubit_noise: vec![
                        QubitNoise {
                            t1: 100.0,
                            t2: 50.0,
                            gate_fidelity: 0.99,
                            bias_noise: 0.01,
                            readout_fidelity: 0.95,
                        };
                        problem.num_qubits
                    ],
                    coupling_ranges: vec![
                        vec![
                            CouplingRange {
                                min_strength: -1.0,
                                max_strength: 1.0,
                                fidelity: 0.98,
                                crosstalk: 0.02,
                            };
                            problem.num_qubits
                        ];
                        problem.num_qubits
                    ],
                    annealing_time_range: (1.0, 1000.0),
                    temperature_characteristics: TemperatureProfile {
                        initial_temp: 1.0,
                        final_temp: 0.01,
                        temp_precision: 0.001,
                        cooling_rate_limits: (0.1, 10.0),
                    },
                    control_precision: ControlPrecision {
                        bias_precision: 16,
                        coupling_precision: 16,
                        timing_precision: 1e-9,
                    },
                    constraints: Vec::new(),
                    performance_metrics: PerformanceMetrics {
                        success_probability: 0.8,
                        solution_quality: 0.9,
                        time_to_solution: vec![1000.0],
                        energy_resolution: 0.001,
                        reproducibility: 0.95,
                    },
                },
            ),
            actual_performance.clone(),
        ));
        self.confidence = (self.training_data.len() as f64 / 100.0).min(0.95).max(0.1);
        Ok(())
    }
    fn get_confidence(&self) -> f64 {
        self.confidence
    }
}