scirs2_io/pipeline/advanced_optimization/

quantum.rs

//! Quantum-inspired optimization algorithms for pipeline optimization
//!
//! This module implements quantum computing-inspired optimization techniques
//! including quantum annealing, quantum state management, and quantum tunneling.

use crate::error::{IoError, Result};
use scirs2_core::random::Rng;
use std::collections::HashMap;

use super::config::QuantumOptimizationConfig;

/// Quantum state representation for optimization problems
#[derive(Debug)]
pub struct QuantumState {
    qubits: Vec<Qubit>,
    entanglement_matrix: Vec<Vec<f64>>,
    superposition_weights: Vec<f64>,
}

impl Default for QuantumState {
    fn default() -> Self {
        Self::new(10)
    }
}

impl QuantumState {
    pub fn new(num_qubits: usize) -> Self {
        Self {
            qubits: (0..num_qubits).map(|_| Qubit::new()).collect(),
            entanglement_matrix: vec![vec![0.0; num_qubits]; num_qubits],
            superposition_weights: vec![1.0 / (num_qubits as f64).sqrt(); num_qubits],
        }
    }

    pub fn initialize_superposition(&mut self, dimensions: usize) -> Result<()> {
        // Initialize quantum superposition state
        let mut rng = scirs2_core::random::thread_rng();
        for (i, qubit) in self.qubits.iter_mut().enumerate().take(dimensions) {
            qubit.set_superposition_state(
                self.superposition_weights[i],
                rng.gen::<f64>() * 2.0 * std::f64::consts::PI,
            );
        }

        // Create entanglement between optimization variables
        self.create_entanglement_network(dimensions)?;

        Ok(())
    }

    fn create_entanglement_network(&mut self, dimensions: usize) -> Result<()> {
        let mut rng = scirs2_core::random::thread_rng();
        for i in 0..dimensions {
            for j in (i + 1)..dimensions {
                let entanglement_strength = (rng.gen::<f64>() * 0.5).exp();
                self.entanglement_matrix[i][j] = entanglement_strength;
                self.entanglement_matrix[j][i] = entanglement_strength;
            }
        }
        Ok(())
    }

    pub fn collapse_to_classical(&self) -> Vec<f64> {
        self.qubits.iter().map(|qubit| qubit.measure()).collect()
    }

    pub fn apply_quantum_gate(&mut self, gate: QuantumGate, qubit_indices: &[usize]) -> Result<()> {
        match gate {
            QuantumGate::Hadamard => {
                for &idx in qubit_indices {
                    if idx < self.qubits.len() {
                        self.qubits[idx].apply_hadamard();
                    }
                }
            }
            QuantumGate::PauliX => {
                for &idx in qubit_indices {
                    if idx < self.qubits.len() {
                        self.qubits[idx].apply_pauli_x();
                    }
                }
            }
            QuantumGate::Rotation { angle } => {
                for &idx in qubit_indices {
                    if idx < self.qubits.len() {
                        self.qubits[idx].apply_rotation(angle);
                    }
                }
            }
        }
        Ok(())
    }

    pub fn get_entanglement_strength(&self, qubit1: usize, qubit2: usize) -> f64 {
        if qubit1 < self.entanglement_matrix.len()
            && qubit2 < self.entanglement_matrix[qubit1].len()
        {
            self.entanglement_matrix[qubit1][qubit2]
        } else {
            0.0
        }
    }
}

/// Individual qubit with quantum properties
#[derive(Debug)]
pub struct Qubit {
    amplitude_alpha: f64,
    amplitude_beta: f64,
    phase: f64,
}

impl Qubit {
    pub fn new() -> Self {
        Self {
            amplitude_alpha: 1.0 / std::f64::consts::SQRT_2,
            amplitude_beta: 1.0 / std::f64::consts::SQRT_2,
            phase: 0.0,
        }
    }

    pub fn set_superposition_state(&mut self, weight: f64, phase: f64) {
        self.amplitude_alpha = weight.sqrt();
        self.amplitude_beta = (1.0 - weight).sqrt();
        self.phase = phase;
    }

    pub fn measure(&self) -> f64 {
        let mut rng = scirs2_core::random::thread_rng();
        if rng.gen::<f64>() < self.amplitude_alpha.powi(2) {
            0.0
        } else {
            1.0
        }
    }

    pub fn apply_hadamard(&mut self) {
        let new_alpha = (self.amplitude_alpha + self.amplitude_beta) / std::f64::consts::SQRT_2;
        let new_beta = (self.amplitude_alpha - self.amplitude_beta) / std::f64::consts::SQRT_2;
        self.amplitude_alpha = new_alpha;
        self.amplitude_beta = new_beta;
    }

    pub fn apply_pauli_x(&mut self) {
        std::mem::swap(&mut self.amplitude_alpha, &mut self.amplitude_beta);
    }

    pub fn apply_rotation(&mut self, angle: f64) {
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        let new_alpha = cos_half * self.amplitude_alpha - sin_half * self.amplitude_beta;
        let new_beta = sin_half * self.amplitude_alpha + cos_half * self.amplitude_beta;

        self.amplitude_alpha = new_alpha;
        self.amplitude_beta = new_beta;
    }

    pub fn get_probability_zero(&self) -> f64 {
        self.amplitude_alpha.powi(2)
    }

    pub fn get_probability_one(&self) -> f64 {
        self.amplitude_beta.powi(2)
    }
}

impl Default for Qubit {
    fn default() -> Self {
        Self::new()
    }
}

/// Quantum gates for state manipulation
#[derive(Debug, Clone)]
pub enum QuantumGate {
    Hadamard,
    PauliX,
    Rotation { angle: f64 },
}

/// Quantum annealing simulator for global optimization
#[derive(Debug)]
pub struct QuantumAnnealer {
    temperature_schedule: Vec<f64>,
    tunneling_probability: f64,
    annealing_steps: usize,
}

impl QuantumAnnealer {
    pub fn new() -> Self {
        Self {
            temperature_schedule: Self::generate_temperature_schedule(1000),
            tunneling_probability: 0.1,
            annealing_steps: 1000,
        }
    }

    pub fn from_config(config: &QuantumOptimizationConfig) -> Self {
        Self {
            temperature_schedule: config.temperature_schedule.clone(),
            tunneling_probability: config.tunneling_probability,
            annealing_steps: config.annealing_steps,
        }
    }

    pub fn anneal(
        &self,
        objective_function: &dyn Fn(&[f64]) -> f64,
        quantum_state: &QuantumState,
        constraints: &[QuantumConstraint],
    ) -> Result<QuantumAnnealingResult> {
        let mut current_state = self.sample_quantum_state(quantum_state)?;
        let mut current_energy = objective_function(&current_state);
        let mut best_state = current_state.clone();
        let mut best_energy = current_energy;

        for &temperature in self.temperature_schedule.iter() {
            // Generate candidate state with quantum tunneling
            let candidate_state = self.quantum_tunnel(&current_state, temperature)?;

            // Check constraints
            if self.satisfies_constraints(&candidate_state, constraints) {
                let candidate_energy = objective_function(&candidate_state);
                let energy_delta = candidate_energy - current_energy;

                // Quantum annealing acceptance criterion
                if energy_delta < 0.0 || self.quantum_acceptance(energy_delta, temperature) {
                    current_state = candidate_state;
                    current_energy = candidate_energy;

                    if current_energy < best_energy {
                        best_state = current_state.clone();
                        best_energy = current_energy;
                    }
                }
            }
        }

        Ok(QuantumAnnealingResult {
            parameters: best_state,
            energy: best_energy,
            convergence_step: self.annealing_steps,
        })
    }

    fn generate_temperature_schedule(steps: usize) -> Vec<f64> {
        (0..steps)
            .map(|i| {
                let t = i as f64 / steps as f64;
                10.0 * (-5.0 * t).exp()
            })
            .collect()
    }

    fn sample_quantum_state(&self, quantum_state: &QuantumState) -> Result<Vec<f64>> {
        Ok(quantum_state
            .qubits
            .iter()
            .map(|qubit| qubit.measure())
            .collect())
    }

    fn quantum_tunnel(&self, state: &[f64], temperature: f64) -> Result<Vec<f64>> {
        let mut rng = scirs2_core::random::thread_rng();
        let mut new_state = state.to_vec();
        for value in &mut new_state {
            if rng.gen::<f64>() < self.tunneling_probability {
                let tunnel_distance = temperature * rng.gen::<f64>();
                *value += tunnel_distance * (rng.gen::<f64>() - 0.5) * 2.0;
                *value = value.clamp(0.0, 1.0);
            }
        }
        Ok(new_state)
    }

    fn quantum_acceptance(&self, energy_delta: f64, temperature: f64) -> bool {
        if temperature <= 0.0 {
            false
        } else {
            let mut rng = scirs2_core::random::thread_rng();
            rng.gen::<f64>() < (-energy_delta / temperature).exp()
        }
    }

    fn satisfies_constraints(&self, state: &[f64], constraints: &[QuantumConstraint]) -> bool {
        constraints.iter().all(|constraint| constraint.check(state))
    }
}

impl Default for QuantumAnnealer {
    fn default() -> Self {
        Self::new()
    }
}

/// Quantum constraint for optimization problems
#[derive(Debug)]
pub struct QuantumConstraint {
    constraint_type: QuantumConstraintType,
    parameters: Vec<f64>,
}

impl QuantumConstraint {
    pub fn new(constraint_type: QuantumConstraintType, parameters: Vec<f64>) -> Self {
        Self {
            constraint_type,
            parameters,
        }
    }

    pub fn check(&self, state: &[f64]) -> bool {
        match self.constraint_type {
            QuantumConstraintType::Range => {
                if self.parameters.len() >= 3 {
                    let index = self.parameters[0] as usize;
                    let min_val = self.parameters[1];
                    let max_val = self.parameters[2];
                    if index < state.len() {
                        return state[index] >= min_val && state[index] <= max_val;
                    }
                }
                false
            }
            QuantumConstraintType::Sum => {
                if self.parameters.len() >= 2 {
                    let target_sum = self.parameters[0];
                    let tolerance = self.parameters[1];
                    let actual_sum: f64 = state.iter().sum();
                    return (actual_sum - target_sum).abs() <= tolerance;
                }
                false
            }
            QuantumConstraintType::Linear => {
                if self.parameters.len() >= state.len() + 2 {
                    let target = self.parameters[0];
                    let tolerance = self.parameters[1];
                    let coefficients = &self.parameters[2..];
                    let linear_combination: f64 = state
                        .iter()
                        .zip(coefficients.iter())
                        .map(|(x, c)| x * c)
                        .sum();
                    return (linear_combination - target).abs() <= tolerance;
                }
                false
            }
        }
    }
}

/// Types of quantum constraints
#[derive(Debug, Clone)]
pub enum QuantumConstraintType {
    Range,  // Variable must be within [min, max]
    Sum,    // Sum of variables must equal target ± tolerance
    Linear, // Linear combination must equal target ± tolerance
}

/// Result of quantum annealing optimization
#[derive(Debug)]
pub struct QuantumAnnealingResult {
    pub parameters: Vec<f64>,
    pub energy: f64,
    pub convergence_step: usize,
}

/// Quantum optimization engine for pipeline parameters
#[derive(Debug)]
pub struct QuantumOptimizer {
    quantum_state: QuantumState,
    annealer: QuantumAnnealer,
    config: QuantumOptimizationConfig,
}

impl QuantumOptimizer {
    pub fn new(config: QuantumOptimizationConfig) -> Self {
        Self {
            quantum_state: QuantumState::new(config.num_qubits),
            annealer: QuantumAnnealer::from_config(&config),
            config,
        }
    }

    pub fn optimize_pipeline_parameters(
        &mut self,
        objective_function: &dyn Fn(&[f64]) -> f64,
        constraints: &[QuantumConstraint],
        dimensions: usize,
    ) -> Result<QuantumOptimizationResult> {
        // Initialize quantum superposition for the optimization space
        self.quantum_state.initialize_superposition(dimensions)?;

        // Apply quantum gates for exploration
        self.apply_exploration_gates(dimensions)?;

        // Perform quantum annealing
        let annealing_result =
            self.annealer
                .anneal(objective_function, &self.quantum_state, constraints)?;

        // Analyze quantum entanglement patterns
        let entanglement_analysis = self.analyze_entanglement(dimensions);

        Ok(QuantumOptimizationResult {
            optimal_parameters: annealing_result.parameters,
            objective_value: annealing_result.energy,
            convergence_info: QuantumConvergenceInfo {
                converged: true,
                final_temperature: self
                    .config
                    .temperature_schedule
                    .last()
                    .copied()
                    .unwrap_or(0.0),
                annealing_steps: annealing_result.convergence_step,
            },
            entanglement_analysis,
            quantum_state_info: self.get_quantum_state_info(),
        })
    }

    fn apply_exploration_gates(&mut self, dimensions: usize) -> Result<()> {
        // Apply Hadamard gates for superposition
        let hadamard_indices: Vec<usize> = (0..dimensions).collect();
        self.quantum_state
            .apply_quantum_gate(QuantumGate::Hadamard, &hadamard_indices)?;

        // Apply rotation gates for fine-tuning
        let mut rng = scirs2_core::random::thread_rng();
        for i in 0..dimensions {
            let rotation_angle = rng.gen::<f64>() * std::f64::consts::PI;
            self.quantum_state.apply_quantum_gate(
                QuantumGate::Rotation {
                    angle: rotation_angle,
                },
                &[i],
            )?;
        }

        Ok(())
    }

    fn analyze_entanglement(&self, dimensions: usize) -> EntanglementAnalysis {
        let mut total_entanglement = 0.0;
        let mut max_entanglement: f64 = 0.0;
        let mut entangled_pairs = Vec::new();

        for i in 0..dimensions {
            for j in (i + 1)..dimensions {
                let entanglement = self.quantum_state.get_entanglement_strength(i, j);
                total_entanglement += entanglement;
                max_entanglement = max_entanglement.max(entanglement);

                if entanglement > 0.5 {
                    entangled_pairs.push((i, j, entanglement));
                }
            }
        }

        let connectivity =
            entangled_pairs.len() as f64 / (dimensions * (dimensions - 1) / 2) as f64;

        EntanglementAnalysis {
            average_entanglement: total_entanglement / (dimensions * (dimensions - 1) / 2) as f64,
            max_entanglement,
            entangled_pairs,
            connectivity,
        }
    }

    fn get_quantum_state_info(&self) -> QuantumStateInfo {
        let superposition_entropy = self.calculate_superposition_entropy();
        let coherence_measure = self.calculate_coherence();

        QuantumStateInfo {
            num_qubits: self.quantum_state.qubits.len(),
            superposition_entropy,
            coherence_measure,
            measurement_probabilities: self
                .quantum_state
                .qubits
                .iter()
                .map(|q| (q.get_probability_zero(), q.get_probability_one()))
                .collect(),
        }
    }

    fn calculate_superposition_entropy(&self) -> f64 {
        self.quantum_state
            .qubits
            .iter()
            .map(|qubit| {
                let p0 = qubit.get_probability_zero();
                let p1 = qubit.get_probability_one();
                -(p0 * p0.ln() + p1 * p1.ln())
            })
            .sum::<f64>()
            / self.quantum_state.qubits.len() as f64
    }

    fn calculate_coherence(&self) -> f64 {
        // Simplified coherence measure
        self.quantum_state
            .qubits
            .iter()
            .map(|qubit| {
                let p_diff = (qubit.get_probability_zero() - qubit.get_probability_one()).abs();
                1.0 - p_diff
            })
            .sum::<f64>()
            / self.quantum_state.qubits.len() as f64
    }
}

impl Default for QuantumOptimizer {
    fn default() -> Self {
        Self::new(QuantumOptimizationConfig::default())
    }
}

/// Complete result of quantum optimization
#[derive(Debug)]
pub struct QuantumOptimizationResult {
    pub optimal_parameters: Vec<f64>,
    pub objective_value: f64,
    pub convergence_info: QuantumConvergenceInfo,
    pub entanglement_analysis: EntanglementAnalysis,
    pub quantum_state_info: QuantumStateInfo,
}

/// Convergence information for quantum optimization
#[derive(Debug)]
pub struct QuantumConvergenceInfo {
    pub converged: bool,
    pub final_temperature: f64,
    pub annealing_steps: usize,
}

/// Analysis of quantum entanglement in the optimization process
#[derive(Debug)]
pub struct EntanglementAnalysis {
    pub average_entanglement: f64,
    pub max_entanglement: f64,
    pub entangled_pairs: Vec<(usize, usize, f64)>,
    pub connectivity: f64,
}

/// Information about the quantum state
#[derive(Debug)]
pub struct QuantumStateInfo {
    pub num_qubits: usize,
    pub superposition_entropy: f64,
    pub coherence_measure: f64,
    pub measurement_probabilities: Vec<(f64, f64)>,
}