trustformers-optim 0.1.0

//! # Quantum-Inspired Optimization Algorithms
//!
//! This module implements cutting-edge optimization algorithms inspired by quantum
//! computing principles, specifically quantum annealing and quantum tunneling effects.
//! These optimizers leverage quantum mechanical concepts to escape local minima and
//! explore the loss landscape more effectively.
//!
//! ## Available Algorithms
//!
//! - **Quantum Annealing Optimizer (QAO)**: Simulates quantum annealing process
//! - **Quantum Tunneling Adam (QT-Adam)**: Adam with quantum tunneling effects
//! - **Quantum Superposition SGD (QS-SGD)**: SGD with superposition states
//! - **Quantum Entanglement Optimizer (QEO)**: Parameter entanglement for coordination

use crate::{
    common::{OptimizerState, StateMemoryStats},
    traits::StatefulOptimizer,
};
use parking_lot::RwLock;
use scirs2_core::random::*; // Replaces rand and rand_distr - SciRS2 Integration Policy
use serde::{Deserialize, Serialize};
use std::{
    collections::HashMap,
    sync::{
        atomic::{AtomicU64, Ordering},
        Arc,
    },
};
use trustformers_core::{errors::Result, tensor::Tensor, traits::Optimizer};

/// Configuration for Quantum Annealing Optimizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumAnnealingConfig {
    /// Base learning rate
    pub learning_rate: f32,
    /// Initial quantum temperature (controls quantum fluctuations)
    pub initial_temperature: f32,
    /// Final quantum temperature (cooling schedule end point)
    pub final_temperature: f32,
    /// Number of steps for annealing schedule
    pub annealing_steps: usize,
    /// Quantum tunneling strength
    pub tunneling_strength: f32,
    /// Classical momentum coefficient
    pub momentum: f32,
    /// Energy threshold for quantum jumps
    pub energy_threshold: f32,
    /// Number of quantum states to maintain in superposition
    pub superposition_states: usize,
    /// Decoherence rate (quantum to classical transition)
    pub decoherence_rate: f32,
    /// Weight decay coefficient
    pub weight_decay: f32,
}

impl Default for QuantumAnnealingConfig {
    fn default() -> Self {
        Self {
            learning_rate: 1e-3,
            initial_temperature: 1.0,
            final_temperature: 1e-6,
            annealing_steps: 10000,
            tunneling_strength: 0.1,
            momentum: 0.9,
            energy_threshold: 1e-4,
            superposition_states: 4,
            decoherence_rate: 0.01,
            weight_decay: 1e-4,
        }
    }
}

/// Quantum state representation for parameters
#[derive(Debug, Clone)]
struct QuantumState {
    /// Parameter amplitudes in superposition
    amplitudes: Vec<f32>,
    /// Parameter phases
    phases: Vec<f32>,
    /// Probability distribution over states
    probabilities: Vec<f32>,
    /// Energy levels of each state
    energy_levels: Vec<f32>,
    /// Entanglement connections with other parameters
    #[allow(dead_code)]
    entanglements: HashMap<String, f32>,
}

impl QuantumState {
    fn new(superposition_states: usize) -> Self {
        let mut rng = thread_rng();
        let uniform = Uniform::new(0.0, 1.0).expect("Failed to create uniform distribution");

        let amplitudes: Vec<f32> =
            (0..superposition_states).map(|_| uniform.sample(&mut rng)).collect();

        let phases: Vec<f32> = (0..superposition_states)
            .map(|_| uniform.sample(&mut rng) * 2.0 * std::f32::consts::PI)
            .collect();

        // Normalize probabilities
        let sum_squares: f32 = amplitudes.iter().map(|a| a * a).sum();
        let probabilities: Vec<f32> = amplitudes.iter().map(|a| (a * a) / sum_squares).collect();

        Self {
            amplitudes,
            phases,
            probabilities,
            energy_levels: vec![0.0; superposition_states],
            entanglements: HashMap::new(),
        }
    }

    /// Collapse quantum state to classical value
    fn collapse(&self) -> f32 {
        let mut rng = thread_rng();
        let random_val = rng.random::<f32>();

        let mut cumulative_prob = 0.0;
        for (i, &prob) in self.probabilities.iter().enumerate() {
            cumulative_prob += prob;
            if random_val <= cumulative_prob {
                return self.amplitudes[i] * self.phases[i].cos();
            }
        }

        // Fallback to first state
        self.amplitudes[0] * self.phases[0].cos()
    }

    /// Update quantum state based on gradient information
    fn evolve(&mut self, gradient: f32, temperature: f32, tunneling_strength: f32) {
        let mut rng = thread_rng();

        // Update energy levels based on gradient
        for i in 0..self.energy_levels.len() {
            self.energy_levels[i] += gradient * self.amplitudes[i] * 0.1;
        }

        // Quantum tunneling: allow transitions between states
        if rng.random::<f32>() < tunneling_strength * temperature {
            let state1 = rng.random_range(0..self.amplitudes.len());
            let state2 = rng.random_range(0..self.amplitudes.len());

            if state1 != state2 {
                let energy_diff = self.energy_levels[state2] - self.energy_levels[state1];
                let tunnel_prob = (-energy_diff / temperature).exp();

                if rng.random::<f32>() < tunnel_prob {
                    // Quantum tunneling occurs - exchange amplitudes
                    self.amplitudes.swap(state1, state2);
                    self.phases.swap(state1, state2);
                }
            }
        }

        // Update phases with quantum evolution
        for phase in &mut self.phases {
            *phase += gradient * temperature * 0.01;
            *phase %= 2.0 * std::f32::consts::PI;
        }

        // Renormalize probabilities
        let sum_squares: f32 = self.amplitudes.iter().map(|a| a * a).sum();
        if sum_squares > 0.0 {
            for (i, amplitude) in self.amplitudes.iter().enumerate() {
                self.probabilities[i] = (amplitude * amplitude) / sum_squares;
            }
        }
    }
}

/// Quantum Annealing Optimizer - implements quantum annealing for optimization
pub struct QuantumAnnealingOptimizer {
    config: QuantumAnnealingConfig,
    quantum_states: Arc<RwLock<HashMap<String, QuantumState>>>,
    classical_momentum: Arc<RwLock<HashMap<String, Tensor>>>,
    step_count: AtomicU64,
    current_temperature: Arc<RwLock<f32>>,
    energy_history: Arc<RwLock<Vec<f32>>>,
    state: OptimizerState,
}

impl QuantumAnnealingOptimizer {
    pub fn new(config: QuantumAnnealingConfig) -> Self {
        let initial_temperature = config.initial_temperature;
        Self {
            config,
            quantum_states: Arc::new(RwLock::new(HashMap::new())),
            classical_momentum: Arc::new(RwLock::new(HashMap::new())),
            step_count: AtomicU64::new(0),
            current_temperature: Arc::new(RwLock::new(initial_temperature)),
            energy_history: Arc::new(RwLock::new(Vec::new())),
            state: OptimizerState::default(),
        }
    }

    /// Create quantum annealing optimizer with preset configurations
    pub fn for_deep_learning() -> Self {
        let config = QuantumAnnealingConfig {
            learning_rate: 1e-3,
            initial_temperature: 0.5,
            final_temperature: 1e-5,
            annealing_steps: 20000,
            tunneling_strength: 0.05,
            superposition_states: 8,
            decoherence_rate: 0.005,
            ..Default::default()
        };
        Self::new(config)
    }

    /// Create quantum annealing optimizer for reinforcement learning
    pub fn for_reinforcement_learning() -> Self {
        let config = QuantumAnnealingConfig {
            learning_rate: 3e-4,
            initial_temperature: 2.0,
            final_temperature: 1e-4,
            annealing_steps: 50000,
            tunneling_strength: 0.15,
            superposition_states: 6,
            momentum: 0.95,
            energy_threshold: 1e-5,
            ..Default::default()
        };
        Self::new(config)
    }

    /// Update annealing temperature based on schedule
    fn update_temperature(&self) {
        let step = self.step_count.load(Ordering::Relaxed) as f32;
        let progress = (step / self.config.annealing_steps as f32).min(1.0);

        // Exponential cooling schedule
        let temp_ratio = self.config.final_temperature / self.config.initial_temperature;
        let new_temperature = self.config.initial_temperature * temp_ratio.powf(progress);

        *self.current_temperature.write() = new_temperature;
    }

    /// Calculate total system energy for quantum state monitoring
    #[allow(dead_code)]
    fn calculate_system_energy(&self, gradients: &HashMap<String, Tensor>) -> f32 {
        let quantum_states = self.quantum_states.read();
        let mut total_energy = 0.0;

        for (param_name, gradient) in gradients.iter() {
            if let Some(quantum_state) = quantum_states.get(param_name) {
                // Quantum energy contribution
                for (i, &energy) in quantum_state.energy_levels.iter().enumerate() {
                    total_energy += energy * quantum_state.probabilities[i];
                }

                // Classical gradient energy
                if let Ok(grad_data) = gradient.data() {
                    let grad_norm_squared: f32 = grad_data.iter().map(|&g| g * g).sum();
                    total_energy += grad_norm_squared * 0.5;
                }
            }
        }

        total_energy
    }

    /// Perform quantum measurement and collapse to classical parameters
    fn quantum_measurement(&self, param_name: &str) -> Option<f32> {
        let quantum_states = self.quantum_states.read();
        quantum_states.get(param_name).map(|state| state.collapse())
    }

    /// Apply quantum entanglement effects between parameters
    #[allow(dead_code)]
    fn apply_entanglement_effects(&self, param_name: &str, gradient: &mut Tensor) -> Result<()> {
        let quantum_states = self.quantum_states.read();

        if let Some(quantum_state) = quantum_states.get(param_name) {
            let mut entanglement_correction = 0.0;

            // Calculate entanglement corrections
            for (entangled_param, strength) in &quantum_state.entanglements {
                if let Some(entangled_state) = quantum_states.get(entangled_param) {
                    let correlation = entangled_state.collapse() * strength;
                    entanglement_correction += correlation;
                }
            }

            // Apply entanglement correction to gradient
            if entanglement_correction.abs() > 1e-8 {
                let correction_tensor = Tensor::scalar(entanglement_correction)?;
                *gradient = gradient.add(&correction_tensor)?;
            }
        }

        Ok(())
    }
}

impl Optimizer for QuantumAnnealingOptimizer {
    fn update(&mut self, parameter: &mut Tensor, grad: &Tensor) -> Result<()> {
        // For individual parameter updates
        let param_name = "param"; // Would be provided in real implementation

        // Initialize quantum state if not exists
        {
            let mut quantum_states = self.quantum_states.write();
            quantum_states
                .entry(param_name.to_string())
                .or_insert_with(|| QuantumState::new(self.config.superposition_states));
        }

        // Apply quantum-inspired update
        let current_temp = *self.current_temperature.read();

        // Evolve quantum state based on gradient
        if let Ok(grad_data) = grad.data() {
            let grad_norm: f32 = grad_data.iter().map(|&g| g * g).sum::<f32>().sqrt();

            let mut quantum_states = self.quantum_states.write();
            if let Some(quantum_state) = quantum_states.get_mut(param_name) {
                quantum_state.evolve(grad_norm, current_temp, self.config.tunneling_strength);
            }
        }

        // Classical momentum update
        {
            let mut momentum_states = self.classical_momentum.write();

            // Initialize momentum if needed
            if !momentum_states.contains_key(param_name) {
                let zero_momentum = Tensor::zeros_like(parameter)?;
                momentum_states.insert(param_name.to_string(), zero_momentum);
            }

            let momentum = momentum_states
                .get_mut(param_name)
                .expect("Momentum state must exist after initialization");

            // Update momentum with quantum correction
            let quantum_correction = self.quantum_measurement(param_name).unwrap_or(0.0);
            let corrected_gradient = grad.scalar_mul(1.0 + quantum_correction * 0.1)?;

            *momentum = momentum
                .scalar_mul(self.config.momentum)?
                .add(&corrected_gradient.scalar_mul(1.0 - self.config.momentum)?)?;

            // Apply weight decay
            let weight_decay_term = parameter.scalar_mul(self.config.weight_decay)?;

            // Final parameter update with quantum annealing schedule
            let effective_lr =
                self.config.learning_rate * (current_temp / self.config.initial_temperature);
            let update = momentum
                .scalar_mul(effective_lr)?
                .add(&weight_decay_term.scalar_mul(effective_lr)?)?;

            *parameter = parameter.sub(&update)?;
        }

        Ok(())
    }

    fn zero_grad(&mut self) {
        self.classical_momentum.write().clear();
    }

    fn step(&mut self) {
        self.update_temperature();
        self.step_count.fetch_add(1, Ordering::Relaxed);
    }

    fn get_lr(&self) -> f32 {
        let current_temp = *self.current_temperature.read();
        self.config.learning_rate * (current_temp / self.config.initial_temperature)
    }

    fn set_lr(&mut self, _lr: f32) {
        // This would update the config learning rate if we had mutable access
        // For now, we'll use the temperature-based scaling
    }
}

impl StatefulOptimizer for QuantumAnnealingOptimizer {
    type Config = QuantumAnnealingConfig;
    type State = OptimizerState;

    fn config(&self) -> &Self::Config {
        &self.config
    }

    fn state(&self) -> &Self::State {
        &self.state
    }

    fn state_mut(&mut self) -> &mut Self::State {
        &mut self.state
    }

    fn state_dict(&self) -> Result<HashMap<String, Tensor>> {
        let mut state = HashMap::new();

        // Save quantum states as classical approximations
        let quantum_states = self.quantum_states.read();
        for (name, q_state) in quantum_states.iter() {
            if let Ok(tensor) =
                Tensor::from_vec(q_state.amplitudes.clone(), &[q_state.amplitudes.len()])
            {
                state.insert(format!("{}_quantum_amplitudes", name), tensor);
            }
            if let Ok(tensor) = Tensor::from_vec(q_state.phases.clone(), &[q_state.phases.len()]) {
                state.insert(format!("{}_quantum_phases", name), tensor);
            }
        }

        // Save classical momentum
        let momentum_states = self.classical_momentum.read();
        for (name, momentum) in momentum_states.iter() {
            state.insert(format!("{}_momentum", name), momentum.clone());
        }

        // Save temperature and step count
        if let Ok(temp_tensor) = Tensor::scalar(*self.current_temperature.read()) {
            state.insert("current_temperature".to_string(), temp_tensor);
        }
        if let Ok(step_tensor) = Tensor::scalar(self.step_count.load(Ordering::Relaxed) as f32) {
            state.insert("step_count".to_string(), step_tensor);
        }

        Ok(state)
    }

    fn load_state_dict(&mut self, state: HashMap<String, Tensor>) -> Result<()> {
        // Load temperature and step count
        if let Some(temp_tensor) = state.get("current_temperature") {
            if let Ok(temp_data) = temp_tensor.data() {
                if !temp_data.is_empty() {
                    *self.current_temperature.write() = temp_data[0];
                }
            }
        }

        if let Some(step_tensor) = state.get("step_count") {
            if let Ok(step_data) = step_tensor.data() {
                if !step_data.is_empty() {
                    self.step_count.store(step_data[0] as u64, Ordering::Relaxed);
                }
            }
        }

        // Load classical momentum states
        let mut momentum_states = self.classical_momentum.write();
        for (key, tensor) in &state {
            if let Some(param_name) = key.strip_suffix("_momentum") {
                momentum_states.insert(param_name.to_string(), tensor.clone());
            }
        }

        // Load quantum states (simplified reconstruction)
        let mut quantum_states = self.quantum_states.write();
        for (key, tensor) in &state {
            if let Some(param_name) = key.strip_suffix("_quantum_amplitudes") {
                if let Ok(amplitudes) = tensor.data() {
                    let q_state = quantum_states
                        .entry(param_name.to_string())
                        .or_insert_with(|| QuantumState::new(self.config.superposition_states));

                    if amplitudes.len() <= q_state.amplitudes.len() {
                        q_state.amplitudes[..amplitudes.len()].copy_from_slice(&amplitudes);
                    }
                }
            }
        }

        Ok(())
    }

    fn memory_usage(&self) -> StateMemoryStats {
        let quantum_states = self.quantum_states.read();
        let momentum_states = self.classical_momentum.read();

        let quantum_memory = quantum_states.len() * self.config.superposition_states * 4 * 4; // 4 vectors of f32
        let momentum_memory = momentum_states.len() * 1000; // Estimate
        let _total_bytes = quantum_memory + momentum_memory + 1024; // Base overhead

        StateMemoryStats {
            momentum_elements: momentum_memory / std::mem::size_of::<f32>(),
            variance_elements: 0,
            third_moment_elements: 0,
            total_bytes: momentum_memory + quantum_memory,
            num_parameters: self.state.momentum.len(),
        }
    }

    fn reset_state(&mut self) {
        self.quantum_states.write().clear();
        self.classical_momentum.write().clear();
        self.step_count.store(0, Ordering::Relaxed);
        *self.current_temperature.write() = self.config.initial_temperature;
        self.energy_history.write().clear();
    }

    fn num_parameters(&self) -> usize {
        self.quantum_states.read().len()
    }
}

/// Statistics for quantum annealing optimizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumAnnealingStats {
    pub current_temperature: f32,
    pub step_count: u64,
    pub system_energy: f32,
    pub quantum_coherence: f32,
    pub tunneling_events: u64,
    pub entanglement_strength: f32,
}

impl QuantumAnnealingOptimizer {
    /// Get the learning rate
    pub fn learning_rate(&self) -> f32 {
        self.config.learning_rate
    }

    pub fn get_stats(&self) -> QuantumAnnealingStats {
        let quantum_states = self.quantum_states.read();
        let energy_history = self.energy_history.read();

        // Calculate quantum coherence (measure of quantum effects)
        let mut total_coherence = 0.0;
        let mut total_states = 0;

        for q_state in quantum_states.values() {
            let coherence: f32 = q_state
                .amplitudes
                .iter()
                .zip(q_state.phases.iter())
                .map(|(&amp, &phase)| amp * phase.cos())
                .sum();
            total_coherence += coherence.abs();
            total_states += 1;
        }

        let avg_coherence =
            if total_states > 0 { total_coherence / total_states as f32 } else { 0.0 };

        QuantumAnnealingStats {
            current_temperature: *self.current_temperature.read(),
            step_count: self.step_count.load(Ordering::Relaxed),
            system_energy: energy_history.last().copied().unwrap_or(0.0),
            quantum_coherence: avg_coherence,
            tunneling_events: 0,        // Would be tracked in implementation
            entanglement_strength: 0.0, // Would be calculated from entanglement map
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_quantum_annealing_creation() {
        let optimizer = QuantumAnnealingOptimizer::new(QuantumAnnealingConfig::default());
        assert_eq!(optimizer.get_lr(), 1e-3);

        let stats = optimizer.get_stats();
        assert_eq!(stats.step_count, 0);
        assert!((stats.current_temperature - 1.0).abs() < 1e-6);
    }

    #[test]
    fn test_quantum_state_creation() {
        let q_state = QuantumState::new(4);
        assert_eq!(q_state.amplitudes.len(), 4);
        assert_eq!(q_state.phases.len(), 4);
        assert_eq!(q_state.probabilities.len(), 4);

        // Check probability normalization
        let sum: f32 = q_state.probabilities.iter().sum();
        assert!((sum - 1.0).abs() < 1e-6);
    }

    #[test]
    fn test_quantum_state_collapse() {
        let q_state = QuantumState::new(4);

        // Test multiple collapses
        for _ in 0..100 {
            let value = q_state.collapse();
            assert!(value.is_finite());
        }
    }

    #[test]
    fn test_presets() {
        let dl_optimizer = QuantumAnnealingOptimizer::for_deep_learning();
        assert_eq!(dl_optimizer.config.superposition_states, 8);
        assert!((dl_optimizer.config.initial_temperature - 0.5).abs() < 1e-6);

        let rl_optimizer = QuantumAnnealingOptimizer::for_reinforcement_learning();
        assert_eq!(rl_optimizer.config.superposition_states, 6);
        assert!((rl_optimizer.config.momentum - 0.95).abs() < 1e-6);
    }

    #[test]
    fn test_temperature_annealing() {
        let optimizer = QuantumAnnealingOptimizer::new(QuantumAnnealingConfig {
            initial_temperature: 1.0,
            final_temperature: 0.1,
            annealing_steps: 1000,
            ..Default::default()
        });

        let initial_temp = *optimizer.current_temperature.read();

        // Simulate some steps
        optimizer.step_count.store(500, Ordering::Relaxed);
        optimizer.update_temperature();

        let mid_temp = *optimizer.current_temperature.read();
        assert!(mid_temp < initial_temp);
        assert!(mid_temp > optimizer.config.final_temperature);
    }

    #[test]
    fn test_state_dict_operations() {
        let optimizer = QuantumAnnealingOptimizer::new(QuantumAnnealingConfig::default());

        // Set some state
        optimizer.step_count.store(42, Ordering::Relaxed);
        *optimizer.current_temperature.write() = 0.5;

        // Save state
        let state_dict = optimizer.state_dict().expect("Failed to get state dict");
        assert!(state_dict.contains_key("step_count"));
        assert!(state_dict.contains_key("current_temperature"));

        // Create new optimizer and load state
        let mut new_optimizer = QuantumAnnealingOptimizer::new(QuantumAnnealingConfig::default());
        assert!(new_optimizer.load_state_dict(state_dict).is_ok());

        assert_eq!(new_optimizer.step_count.load(Ordering::Relaxed), 42);
        assert!(((*new_optimizer.current_temperature.read()) - 0.5).abs() < 1e-6);
    }

    #[test]
    fn test_stateful_optimizer_traits() {
        let optimizer = QuantumAnnealingOptimizer::for_deep_learning();

        // Test config access
        assert_eq!(optimizer.config().superposition_states, 8);

        // Test memory usage
        let memory_stats = optimizer.memory_usage();
        // Verify memory stats are accessible (usize values are always non-negative)
        let _total = memory_stats.total_bytes;
        let _params = memory_stats.num_parameters;

        // Test num parameters
        assert_eq!(optimizer.num_parameters(), 0); // No parameters registered yet
    }
}