quantrs2-ml 0.1.3

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::error::{MLError, Result};
use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
use crate::quantum_transformer::{
    create_causal_mask, PositionEncodingType, QuantumAttentionType, QuantumTransformer,
    QuantumTransformerConfig,
};
use quantrs2_circuit::builder::{Circuit, Simulator};
use quantrs2_core::gate::{multi::*, single::*, GateOp};
use quantrs2_sim::statevector::StateVectorSimulator;
use scirs2_core::ndarray::{s, Array1, Array2, Array3, Array4, Axis};
use std::collections::HashMap;
use std::f64::consts::PI;

/// Quantum reasoning module
#[derive(Debug, Clone)]
pub struct QuantumReasoningModule {
    /// Reasoning configuration
    config: QuantumReasoningConfig,
    /// Logical reasoning circuits
    pub(crate) logical_circuits: Vec<Circuit<16>>,
    /// Causal reasoning networks
    causal_networks: Vec<QuantumNeuralNetwork>,
    /// Analogical reasoning system
    analogical_system: QuantumAnalogyEngine,
    /// Reasoning state memory
    reasoning_memory: Array2<f64>,
    /// Chain-of-thought quantum states
    cot_states: Vec<Array1<f64>>,
}
impl QuantumReasoningModule {
    /// Create new quantum reasoning module
    pub fn new(config: QuantumReasoningConfig) -> Result<Self> {
        let mut logical_circuits = Vec::new();
        let mut causal_networks = Vec::new();
        if config.logical_reasoning {
            for _ in 0..config.reasoning_steps {
                let mut circuit = Circuit::<16>::new();
                for i in 0..8 {
                    circuit.h(i);
                    circuit.ry(i, 0.0);
                }
                for i in 0..7 {
                    circuit.cnot(i, i + 1);
                }
                logical_circuits.push(circuit);
            }
        }
        if config.causal_reasoning {
            for _ in 0..config.reasoning_steps {
                let layers = vec![
                    QNNLayerType::EncodingLayer { num_features: 256 },
                    QNNLayerType::VariationalLayer { num_params: 128 },
                    QNNLayerType::EntanglementLayer {
                        connectivity: "circular".to_string(),
                    },
                    QNNLayerType::VariationalLayer { num_params: 256 },
                    QNNLayerType::MeasurementLayer {
                        measurement_basis: "computational".to_string(),
                    },
                ];
                let network = QuantumNeuralNetwork::new(layers, 12, 256, 256)?;
                causal_networks.push(network);
            }
        }
        let analogical_system = QuantumAnalogyEngine::new()?;
        Ok(Self {
            config,
            logical_circuits,
            causal_networks,
            analogical_system,
            reasoning_memory: Array2::zeros((100, 256)),
            cot_states: Vec::new(),
        })
    }
    /// Apply quantum reasoning to transformer output
    pub fn apply_reasoning(&mut self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
        let mut reasoned_output = hidden_states.clone();
        if self.config.logical_reasoning {
            reasoned_output = self.apply_logical_reasoning(&reasoned_output)?;
        }
        if self.config.causal_reasoning {
            reasoned_output = self.apply_causal_reasoning(&reasoned_output)?;
        }
        if self.config.analogical_reasoning {
            reasoned_output = self.apply_analogical_reasoning(&reasoned_output)?;
        }
        Ok(reasoned_output)
    }
    /// Apply logical reasoning using quantum circuits
    fn apply_logical_reasoning(&mut self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
        let mut output = hidden_states.clone();
        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
        for step in 0..self.config.reasoning_steps.min(self.logical_circuits.len()) {
            let simulator = StateVectorSimulator::new();
            let register = simulator.run(&self.logical_circuits[step])?;
            let quantum_state = register.probabilities();
            let reasoning_features = self.extract_logical_features(&quantum_state)?;
            for batch_idx in 0..batch_size {
                for seq_idx in 0..seq_len {
                    let mut hidden = output.slice_mut(s![batch_idx, seq_idx, ..]);
                    let reasoning_weight = 0.1;
                    for (i, &feature) in reasoning_features.iter().enumerate() {
                        if i < hidden.len() {
                            hidden[i] =
                                hidden[i] * (1.0 - reasoning_weight) + feature * reasoning_weight;
                        }
                    }
                }
            }
            let reasoning_state = Array1::from_vec(reasoning_features);
            self.cot_states.push(reasoning_state);
        }
        Ok(output)
    }
    /// Apply causal reasoning using quantum networks
    fn apply_causal_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
        if self.causal_networks.is_empty() {
            return Ok(hidden_states.clone());
        }
        let mut output = hidden_states.clone();
        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
        for batch_idx in 0..batch_size {
            for seq_idx in 1..seq_len {
                let current_hidden = hidden_states.slice(s![batch_idx, seq_idx, ..]).to_owned();
                let prev_hidden = hidden_states
                    .slice(s![batch_idx, seq_idx - 1, ..])
                    .to_owned();
                let causal_input = Array1::from_iter(
                    current_hidden
                        .iter()
                        .chain(prev_hidden.iter())
                        .take(256)
                        .cloned(),
                );
                let causal_output = self.causal_networks[0].forward(&causal_input)?;
                let causal_weight = 0.2;
                output.slice_mut(s![batch_idx, seq_idx, ..]).zip_mut_with(
                    &causal_output,
                    |orig, causal| {
                        *orig = *orig * (1.0 - causal_weight) + causal * causal_weight;
                    },
                );
            }
        }
        Ok(output)
    }
    /// Apply analogical reasoning
    fn apply_analogical_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
        self.analogical_system
            .apply_analogical_reasoning(hidden_states)
    }
    /// Enhance token selection with quantum reasoning
    pub fn enhance_token_selection(&self, logits: &Array1<f64>) -> Result<Array1<f64>> {
        let mut enhanced_logits = logits.clone();
        if !self.cot_states.is_empty() {
            let latest_reasoning = &self.cot_states[self.cot_states.len() - 1];
            let reasoning_weight = 0.1;
            for (i, &reasoning_val) in latest_reasoning.iter().enumerate() {
                if i < enhanced_logits.len() {
                    enhanced_logits[i] += reasoning_val * reasoning_weight;
                }
            }
        }
        Ok(enhanced_logits)
    }
    /// Measure quantum coherence in reasoning
    pub fn measure_coherence(&self) -> Result<f64> {
        if self.cot_states.is_empty() {
            return Ok(0.0);
        }
        let latest_state = &self.cot_states[self.cot_states.len() - 1];
        let coherence = 1.0
            - latest_state
                .mapv(|x| (x * PI).sin().abs())
                .mean()
                .unwrap_or(0.0);
        Ok(coherence)
    }
    /// Extract logical features from quantum state
    fn extract_logical_features(&self, quantum_state: &[f64]) -> Result<Vec<f64>> {
        let mut features = Vec::new();
        for (i, &amplitude) in quantum_state.iter().enumerate() {
            let logical_feature = amplitude * amplitude;
            features.push(logical_feature);
            if features.len() >= 256 {
                break;
            }
        }
        while features.len() < 256 {
            features.push(0.0);
        }
        Ok(features)
    }
    /// Get number of parameters
    pub fn num_parameters(&self) -> usize {
        let mut total = 0;
        for network in &self.causal_networks {
            total += network.parameters.len();
        }
        total += self.analogical_system.num_parameters();
        total += self.reasoning_memory.len();
        total
    }
}
/// Quantum associative memory
#[derive(Debug, Clone)]
pub struct QuantumAssociativeMemory {
    /// Memory patterns
    patterns: Array2<f64>,
    /// Quantum Hopfield weights
    hopfield_weights: Array2<f64>,
    /// Memory circuit parameters
    memory_circuit_params: Vec<f64>,
    /// Pattern amplitudes
    amplitudes: Array1<f64>,
    /// Retrieval threshold
    threshold: f64,
}
impl QuantumAssociativeMemory {
    /// Create new quantum associative memory
    pub fn new(capacity: usize, pattern_size: usize) -> Result<Self> {
        let patterns = Array2::zeros((capacity, pattern_size));
        let hopfield_weights = Array2::zeros((pattern_size, pattern_size));
        let num_qubits = 8;
        let mut memory_circuit_params = Vec::new();
        for _ in 0..num_qubits {
            memory_circuit_params.push(1.0);
        }
        for i in 0..num_qubits {
            for j in i + 1..num_qubits {
                memory_circuit_params.push(2.0);
            }
        }
        Ok(Self {
            patterns,
            hopfield_weights,
            memory_circuit_params,
            amplitudes: Array1::zeros(capacity),
            threshold: 0.5,
        })
    }
    /// Store pattern in memory
    pub fn store_pattern(&mut self, pattern: &Array1<f64>) -> Result<()> {
        let store_idx = self
            .amplitudes
            .iter()
            .enumerate()
            .min_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(i, _)| i)
            .unwrap_or(0);
        self.patterns
            .slice_mut(s![store_idx, ..pattern.len()])
            .assign(pattern);
        for i in 0..pattern.len() {
            for j in 0..pattern.len() {
                if i != j {
                    self.hopfield_weights[[i, j]] += pattern[i] * pattern[j] * 0.1;
                }
            }
        }
        self.amplitudes[store_idx] = 1.0;
        Ok(())
    }
    /// Retrieve pattern from memory
    pub fn retrieve(&self, query: &Array1<f64>) -> Result<Array1<f64>> {
        let mut best_match = query.clone();
        let mut best_similarity = 0.0;
        for i in 0..self.patterns.nrows() {
            if self.amplitudes[i] > 0.1 {
                let pattern = self.patterns.row(i).to_owned();
                let similarity = self.compute_similarity(query, &pattern)?;
                if similarity > best_similarity {
                    best_similarity = similarity;
                    best_match = pattern;
                }
            }
        }
        if best_similarity > self.threshold {
            let retrieved = self.apply_hopfield_dynamics(&best_match)?;
            Ok(retrieved)
        } else {
            Ok(query.clone())
        }
    }
    /// Compute similarity between patterns
    fn compute_similarity(&self, pattern1: &Array1<f64>, pattern2: &Array1<f64>) -> Result<f64> {
        let norm1 = pattern1.mapv(|x| x * x).sum().sqrt();
        let norm2 = pattern2.mapv(|x| x * x).sum().sqrt();
        if norm1 < 1e-10 || norm2 < 1e-10 {
            return Ok(0.0);
        }
        let dot_product = pattern1.dot(pattern2);
        Ok(dot_product / (norm1 * norm2))
    }
    /// Apply Hopfield dynamics for pattern completion
    fn apply_hopfield_dynamics(&self, initial: &Array1<f64>) -> Result<Array1<f64>> {
        let mut state = initial.clone();
        for _ in 0..5 {
            let new_state = self.hopfield_weights.dot(&state);
            state = new_state.mapv(|x| x.tanh());
        }
        Ok(state)
    }
}
/// Model scale variants
#[derive(Debug, Clone)]
pub enum ModelScale {
    /// Small model (< 1B parameters)
    Small {
        layers: usize,
        model_dim: usize,
        heads: usize,
    },
    /// Medium model (1B - 10B parameters)
    Medium {
        layers: usize,
        model_dim: usize,
        heads: usize,
    },
    /// Large model (10B - 100B parameters)
    Large {
        layers: usize,
        model_dim: usize,
        heads: usize,
    },
    /// XL model (> 100B parameters)
    ExtraLarge {
        layers: usize,
        model_dim: usize,
        heads: usize,
    },
}
/// Subword tokenizer
#[derive(Debug, Clone)]
pub struct SubwordTokenizer {
    /// BPE merges
    merges: Vec<(String, String)>,
    /// Token frequencies
    frequencies: HashMap<String, usize>,
    /// Quantum encoding of subwords
    quantum_encodings: HashMap<String, Array1<f64>>,
}
impl SubwordTokenizer {
    /// Create new subword tokenizer
    pub fn new() -> Self {
        Self {
            merges: Vec::new(),
            frequencies: HashMap::new(),
            quantum_encodings: HashMap::new(),
        }
    }
}
/// Quality metrics for generated text
#[derive(Debug, Clone)]
pub struct QualityMetrics {
    /// Perplexity score
    pub perplexity: f64,
    /// Coherence score
    pub coherence: f64,
    /// Factual accuracy
    pub factual_accuracy: f64,
    /// Logical consistency
    pub logical_consistency: f64,
    /// Quantum advantage measure
    pub quantum_advantage: f64,
}
/// Text generation parameters
#[derive(Debug, Clone)]
pub struct GenerationConfig {
    /// Maximum generation length
    pub max_length: usize,
    /// Temperature for sampling
    pub temperature: f64,
    /// Top-k sampling
    pub top_k: Option<usize>,
    /// Top-p (nucleus) sampling
    pub top_p: Option<f64>,
    /// Repetition penalty
    pub repetition_penalty: f64,
    /// Enable quantum reasoning during generation
    pub use_quantum_reasoning: bool,
    /// Memory-guided generation
    pub use_memory: bool,
    /// Chain-of-thought generation
    pub chain_of_thought: bool,
}
impl GenerationConfig {
    /// Default generation configuration
    pub fn default() -> Self {
        Self {
            max_length: 100,
            temperature: 1.0,
            top_k: Some(50),
            top_p: Some(0.9),
            repetition_penalty: 1.1,
            use_quantum_reasoning: true,
            use_memory: true,
            chain_of_thought: false,
        }
    }
    /// Creative generation configuration
    pub fn creative() -> Self {
        Self {
            max_length: 200,
            temperature: 1.2,
            top_k: Some(100),
            top_p: Some(0.95),
            repetition_penalty: 1.05,
            use_quantum_reasoning: true,
            use_memory: true,
            chain_of_thought: true,
        }
    }
    /// Precise generation configuration
    pub fn precise() -> Self {
        Self {
            max_length: 50,
            temperature: 0.7,
            top_k: Some(20),
            top_p: Some(0.8),
            repetition_penalty: 1.2,
            use_quantum_reasoning: true,
            use_memory: true,
            chain_of_thought: true,
        }
    }
}
/// Quantum analogy engine
#[derive(Debug, Clone)]
pub struct QuantumAnalogyEngine {
    /// Analogy mapping circuit parameters
    mapping_circuit_params: Vec<Vec<f64>>,
    /// Structural similarity measures
    similarity_measures: Array2<f64>,
    /// Analogy transformation matrices
    transformations: Vec<Array2<f64>>,
    /// Quantum interference patterns for analogies
    interference_patterns: Array3<f64>,
}
impl QuantumAnalogyEngine {
    /// Create new quantum analogy engine
    pub fn new() -> Result<Self> {
        let mut mapping_circuit_params = Vec::new();
        for _ in 0..5 {
            let mut params = Vec::new();
            for _ in 0..10 {
                params.push(1.0);
                params.push(0.0);
            }
            for _ in 0..5 {
                params.push(2.0);
            }
            mapping_circuit_params.push(params);
        }
        Ok(Self {
            mapping_circuit_params,
            similarity_measures: Array2::zeros((100, 100)),
            transformations: Vec::new(),
            interference_patterns: Array3::zeros((10, 10, 10)),
        })
    }
    /// Apply analogical reasoning
    pub fn apply_analogical_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
        let mut output = hidden_states.clone();
        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
        for batch_idx in 0..batch_size {
            for seq_idx in 0..seq_len {
                let hidden = hidden_states.slice(s![batch_idx, seq_idx, ..]);
                let analogy_weight = 0.05;
                let analogy_factor = (seq_idx as f64 * 0.1).sin() * analogy_weight;
                output
                    .slice_mut(s![batch_idx, seq_idx, ..])
                    .zip_mut_with(&hidden, |orig, h| {
                        *orig = *orig + h * analogy_factor;
                    });
            }
        }
        Ok(output)
    }
    /// Get number of parameters
    pub fn num_parameters(&self) -> usize {
        self.similarity_measures.len()
            + self.transformations.iter().map(|t| t.len()).sum::<usize>()
            + self.interference_patterns.len()
    }
}
/// Quantum memory system
#[derive(Debug, Clone)]
pub struct QuantumMemorySystem {
    /// Memory configuration
    config: QuantumMemoryConfig,
    /// Associative memory banks
    pub(crate) associative_banks: Vec<QuantumAssociativeMemory>,
    /// Episodic memory store
    episodic_memory: Vec<QuantumEpisode>,
    /// Memory retrieval circuit parameters
    retrieval_circuit_params: Vec<Vec<f64>>,
    /// Memory compression codebooks
    compression_codebooks: HashMap<String, Array2<f64>>,
}
impl QuantumMemorySystem {
    /// Create new quantum memory system
    pub fn new(config: QuantumMemoryConfig) -> Result<Self> {
        let mut associative_banks = Vec::new();
        let mut retrieval_circuit_params = Vec::new();
        if config.associative_memory {
            for _ in 0..5 {
                let memory_bank = QuantumAssociativeMemory::new(100, 128)?;
                associative_banks.push(memory_bank);
            }
        }
        for _ in 0..config.memory_size / 100 {
            let mut params = Vec::new();
            for _ in 0..8 {
                params.push(1.0);
                params.push(0.0);
            }
            for _ in 0..7 {
                params.push(2.0);
            }
            retrieval_circuit_params.push(params);
        }
        Ok(Self {
            config,
            associative_banks,
            episodic_memory: Vec::new(),
            retrieval_circuit_params,
            compression_codebooks: HashMap::new(),
        })
    }
    /// Enhance embeddings with memory retrieval
    pub fn enhance_embeddings(&self, embeddings: &Array3<f64>) -> Result<Array3<f64>> {
        let mut enhanced = embeddings.clone();
        if self.config.associative_memory && !self.associative_banks.is_empty() {
            for batch_idx in 0..embeddings.dim().0 {
                for seq_idx in 0..embeddings.dim().1 {
                    let query = embeddings.slice(s![batch_idx, seq_idx, ..]).to_owned();
                    let retrieved_memory = self.retrieve_associative_memory(&query)?;
                    let combination_weight = 0.1;
                    enhanced.slice_mut(s![batch_idx, seq_idx, ..]).zip_mut_with(
                        &retrieved_memory,
                        |orig, mem| {
                            *orig = *orig * (1.0 - combination_weight) + mem * combination_weight;
                        },
                    );
                }
            }
        }
        Ok(enhanced)
    }
    /// Retrieve from associative memory
    fn retrieve_associative_memory(&self, query: &Array1<f64>) -> Result<Array1<f64>> {
        if self.associative_banks.is_empty() {
            return Ok(query.clone());
        }
        self.associative_banks[0].retrieve(query)
    }
    /// Update memory with new information
    pub fn update_memory(
        &mut self,
        hidden_states: &Array3<f64>,
        input_ids: &Array2<usize>,
    ) -> Result<()> {
        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
        if self.config.episodic_memory {
            for batch_idx in 0..batch_size {
                let context = hidden_states.slice(s![batch_idx, 0, ..]).to_owned();
                let content = hidden_states.slice(s![batch_idx, .., ..]).to_owned();
                let episode = QuantumEpisode {
                    context,
                    content,
                    quantum_state: Array1::zeros(hidden_dim),
                    timestamp: self.episodic_memory.len() as f64,
                    coherence: 0.8,
                    importance: 1.0,
                };
                self.episodic_memory.push(episode);
                if self.episodic_memory.len() > self.config.memory_size {
                    self.episodic_memory.remove(0);
                }
            }
        }
        for bank in &mut self.associative_banks {
            let sample_hidden = hidden_states.slice(s![0, 0, ..]).to_owned();
            bank.store_pattern(&sample_hidden)?;
        }
        Ok(())
    }
    /// Get number of parameters
    pub fn num_parameters(&self) -> usize {
        let mut total = 0;
        for bank in &self.associative_banks {
            total += bank.hopfield_weights.len();
            total += bank.patterns.len();
        }
        for codebook in self.compression_codebooks.values() {
            total += codebook.len();
        }
        total
    }
}
/// Vocabulary management
#[derive(Debug, Clone)]
pub struct Vocabulary {
    /// Token to ID mapping
    token_to_id: HashMap<String, usize>,
    /// ID to token mapping
    id_to_token: HashMap<usize, String>,
    /// Special tokens
    pub(crate) special_tokens: HashMap<String, usize>,
    /// Subword tokenizer
    tokenizer: SubwordTokenizer,
    /// Quantum token embeddings
    pub(crate) quantum_embeddings: Array2<f64>,
}
impl Vocabulary {
    /// Create new vocabulary
    pub fn new(vocab_size: usize) -> Result<Self> {
        let mut token_to_id = HashMap::new();
        let mut id_to_token = HashMap::new();
        let mut special_tokens = HashMap::new();
        special_tokens.insert("<pad>".to_string(), 0);
        special_tokens.insert("<unk>".to_string(), 1);
        special_tokens.insert("<sos>".to_string(), 2);
        special_tokens.insert("<eos>".to_string(), 3);
        token_to_id.insert("<pad>".to_string(), 0);
        token_to_id.insert("<unk>".to_string(), 1);
        token_to_id.insert("<sos>".to_string(), 2);
        token_to_id.insert("<eos>".to_string(), 3);
        id_to_token.insert(0, "<pad>".to_string());
        id_to_token.insert(1, "<unk>".to_string());
        id_to_token.insert(2, "<sos>".to_string());
        id_to_token.insert(3, "<eos>".to_string());
        let quantum_embeddings = Array2::from_shape_fn((vocab_size, 768), |(i, j)| {
            0.02 * (i as f64 * 0.1 + j as f64 * 0.01).sin()
        });
        let tokenizer = SubwordTokenizer::new();
        Ok(Self {
            token_to_id,
            id_to_token,
            special_tokens,
            tokenizer,
            quantum_embeddings,
        })
    }
    /// Tokenize text
    pub fn tokenize(&self, text: &str) -> Result<Vec<usize>> {
        let tokens: Vec<usize> = text
            .split_whitespace()
            .map(|word| self.token_to_id.get(word).copied().unwrap_or(1))
            .collect();
        Ok(tokens)
    }
    /// Get token embedding
    pub fn get_embedding(&self, token_id: usize) -> Result<Array1<f64>> {
        if token_id < self.quantum_embeddings.nrows() {
            Ok(self.quantum_embeddings.row(token_id).to_owned())
        } else {
            Ok(self.quantum_embeddings.row(1).to_owned())
        }
    }
    /// Decode token
    pub fn decode_token(&self, token_id: usize) -> Result<String> {
        Ok(self
            .id_to_token
            .get(&token_id)
            .cloned()
            .unwrap_or_else(|| "<unk>".to_string()))
    }
    /// Check if token is end-of-sequence
    pub fn is_eos_token(&self, token_id: usize) -> bool {
        token_id == 3
    }
}
/// Quantum parameter update strategies
#[derive(Debug, Clone)]
pub enum QuantumParameterUpdate {
    /// Classical gradient descent on quantum parameters
    ClassicalOnQuantum,
    /// Quantum natural gradients
    QuantumNatural,
    /// Quantum BFGS optimization
    QuantumBFGS,
    /// Parameter shift rule
    ParameterShift,
    /// Quantum Adam optimizer
    QuantumAdam,
}
/// Training configuration for QLLMs
#[derive(Debug, Clone)]
pub struct QLLMTrainingConfig {
    /// Quantum-classical hybrid training
    pub hybrid_training: bool,
    /// Quantum parameter update strategy
    pub parameter_update: QuantumParameterUpdate,
    /// Gradient accumulation steps
    pub gradient_accumulation: usize,
    /// Quantum noise injection for regularization
    pub quantum_noise: bool,
    /// Quantum advantage optimization
    pub quantum_advantage_opt: bool,
}
impl QLLMTrainingConfig {
    /// Default training configuration
    pub fn default() -> Self {
        Self {
            hybrid_training: true,
            parameter_update: QuantumParameterUpdate::ClassicalOnQuantum,
            gradient_accumulation: 1,
            quantum_noise: false,
            quantum_advantage_opt: false,
        }
    }
    /// Advanced training configuration
    pub fn advanced() -> Self {
        Self {
            hybrid_training: true,
            parameter_update: QuantumParameterUpdate::QuantumAdam,
            gradient_accumulation: 8,
            quantum_noise: true,
            quantum_advantage_opt: true,
        }
    }
}
/// Quantum Large Language Model configuration
#[derive(Debug, Clone)]
pub struct QuantumLLMConfig {
    /// Base transformer configuration
    pub transformer_config: QuantumTransformerConfig,
    /// Vocabulary size
    pub vocab_size: usize,
    /// Maximum context length
    pub max_context_length: usize,
    /// Number of quantum memory layers
    pub quantum_memory_layers: usize,
    /// Quantum reasoning module configuration
    pub reasoning_config: QuantumReasoningConfig,
    /// Quantum memory configuration
    pub memory_config: QuantumMemoryConfig,
    /// Model scale
    pub model_scale: ModelScale,
    /// Training configuration
    pub training_config: QLLMTrainingConfig,
}
impl QuantumLLMConfig {
    /// Create small model configuration
    pub fn small(vocab_size: usize) -> Self {
        Self {
            transformer_config: QuantumTransformerConfig {
                model_dim: 768,
                num_heads: 12,
                ff_dim: 3072,
                num_layers: 12,
                max_seq_len: 2048,
                num_qubits: 10,
                dropout_rate: 0.1,
                attention_type: QuantumAttentionType::HybridQuantumClassical,
                position_encoding: PositionEncodingType::Rotary,
            },
            vocab_size,
            max_context_length: 2048,
            quantum_memory_layers: 4,
            reasoning_config: QuantumReasoningConfig::default(),
            memory_config: QuantumMemoryConfig::default(),
            model_scale: ModelScale::Small {
                layers: 12,
                model_dim: 768,
                heads: 12,
            },
            training_config: QLLMTrainingConfig::default(),
        }
    }
    /// Create medium model configuration
    pub fn medium(vocab_size: usize) -> Self {
        Self {
            transformer_config: QuantumTransformerConfig {
                model_dim: 1024,
                num_heads: 16,
                ff_dim: 4096,
                num_layers: 24,
                max_seq_len: 4096,
                num_qubits: 16,
                dropout_rate: 0.1,
                attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
                position_encoding: PositionEncodingType::LearnableQuantum,
            },
            vocab_size,
            max_context_length: 4096,
            quantum_memory_layers: 8,
            reasoning_config: QuantumReasoningConfig::enhanced(),
            memory_config: QuantumMemoryConfig::enhanced(),
            model_scale: ModelScale::Medium {
                layers: 24,
                model_dim: 1024,
                heads: 16,
            },
            training_config: QLLMTrainingConfig::default(),
        }
    }
    /// Create large model configuration
    pub fn large(vocab_size: usize) -> Self {
        Self {
            transformer_config: QuantumTransformerConfig {
                model_dim: 1536,
                num_heads: 24,
                ff_dim: 6144,
                num_layers: 48,
                max_seq_len: 8192,
                num_qubits: 12,
                dropout_rate: 0.1,
                attention_type: QuantumAttentionType::FullQuantum,
                position_encoding: PositionEncodingType::QuantumPhase,
            },
            vocab_size,
            max_context_length: 8192,
            quantum_memory_layers: 16,
            reasoning_config: QuantumReasoningConfig::advanced(),
            memory_config: QuantumMemoryConfig::advanced(),
            model_scale: ModelScale::Large {
                layers: 48,
                model_dim: 1536,
                heads: 24,
            },
            training_config: QLLMTrainingConfig::advanced(),
        }
    }
}
/// Quantum memory configuration
#[derive(Debug, Clone)]
pub struct QuantumMemoryConfig {
    /// Memory bank size
    pub memory_size: usize,
    /// Quantum associative memory
    pub associative_memory: bool,
    /// Episodic memory with quantum states
    pub episodic_memory: bool,
    /// Memory retrieval mechanism
    pub retrieval_mechanism: MemoryRetrievalType,
    /// Memory compression using quantum algorithms
    pub quantum_compression: bool,
    /// Memory coherence time
    pub coherence_time: f64,
}
impl QuantumMemoryConfig {
    /// Default memory configuration
    pub fn default() -> Self {
        Self {
            memory_size: 1000,
            associative_memory: true,
            episodic_memory: false,
            retrieval_mechanism: MemoryRetrievalType::QuantumAssociative,
            quantum_compression: false,
            coherence_time: 100.0,
        }
    }
    /// Enhanced memory configuration
    pub fn enhanced() -> Self {
        Self {
            memory_size: 5000,
            associative_memory: true,
            episodic_memory: true,
            retrieval_mechanism: MemoryRetrievalType::ContentAddressable,
            quantum_compression: true,
            coherence_time: 200.0,
        }
    }
    /// Advanced memory configuration
    pub fn advanced() -> Self {
        Self {
            memory_size: 20000,
            associative_memory: true,
            episodic_memory: true,
            retrieval_mechanism: MemoryRetrievalType::Holographic,
            quantum_compression: true,
            coherence_time: 500.0,
        }
    }
}
/// Memory retrieval mechanisms
#[derive(Debug, Clone)]
pub enum MemoryRetrievalType {
    /// Quantum associative retrieval
    QuantumAssociative,
    /// Content-addressable memory
    ContentAddressable,
    /// Holographic memory retrieval
    Holographic,
    /// Quantum Hopfield networks
    QuantumHopfield,
    /// Hierarchical memory access
    Hierarchical,
}
/// Generation statistics
#[derive(Debug, Clone)]
pub struct GenerationStatistics {
    /// Total tokens generated
    pub total_tokens: usize,
    /// Average generation speed (tokens/sec)
    pub avg_speed: f64,
    /// Quantum coherence during generation
    pub quantum_coherence: f64,
    /// Reasoning steps taken
    pub reasoning_steps: usize,
    /// Memory retrievals
    pub memory_retrievals: usize,
    /// Generation quality metrics
    pub quality_metrics: QualityMetrics,
}
impl GenerationStatistics {
    /// Create new generation statistics
    pub fn new() -> Self {
        Self {
            total_tokens: 0,
            avg_speed: 0.0,
            quantum_coherence: 0.0,
            reasoning_steps: 0,
            memory_retrievals: 0,
            quality_metrics: QualityMetrics {
                perplexity: 0.0,
                coherence: 0.0,
                factual_accuracy: 0.0,
                logical_consistency: 0.0,
                quantum_advantage: 0.0,
            },
        }
    }
}
/// Quantum reasoning configuration
#[derive(Debug, Clone)]
pub struct QuantumReasoningConfig {
    /// Enable quantum logical reasoning
    pub logical_reasoning: bool,
    /// Enable quantum causal reasoning
    pub causal_reasoning: bool,
    /// Enable quantum analogical reasoning
    pub analogical_reasoning: bool,
    /// Number of reasoning steps
    pub reasoning_steps: usize,
    /// Reasoning circuit depth
    pub circuit_depth: usize,
    /// Quantum entanglement strength for reasoning
    pub entanglement_strength: f64,
}
impl QuantumReasoningConfig {
    /// Default reasoning configuration
    pub fn default() -> Self {
        Self {
            logical_reasoning: true,
            causal_reasoning: false,
            analogical_reasoning: false,
            reasoning_steps: 3,
            circuit_depth: 5,
            entanglement_strength: 0.5,
        }
    }
    /// Enhanced reasoning configuration
    pub fn enhanced() -> Self {
        Self {
            logical_reasoning: true,
            causal_reasoning: true,
            analogical_reasoning: false,
            reasoning_steps: 5,
            circuit_depth: 8,
            entanglement_strength: 0.7,
        }
    }
    /// Advanced reasoning configuration
    pub fn advanced() -> Self {
        Self {
            logical_reasoning: true,
            causal_reasoning: true,
            analogical_reasoning: true,
            reasoning_steps: 8,
            circuit_depth: 12,
            entanglement_strength: 0.9,
        }
    }
}
/// Main Quantum Large Language Model
#[derive(Debug, Clone)]
pub struct QuantumLLM {
    /// Model configuration
    config: QuantumLLMConfig,
    /// Token embedding layer
    token_embedding: QuantumNeuralNetwork,
    /// Core transformer
    transformer: QuantumTransformer,
    /// Quantum memory system
    quantum_memory: QuantumMemorySystem,
    /// Quantum reasoning module
    quantum_reasoning: QuantumReasoningModule,
    /// Language modeling head
    lm_head: QuantumNeuralNetwork,
    /// Vocabulary mappings
    vocab: Vocabulary,
    /// Model statistics
    generation_stats: GenerationStatistics,
}
impl QuantumLLM {
    /// Create new quantum large language model
    pub fn new(config: QuantumLLMConfig) -> Result<Self> {
        let embed_layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: config.vocab_size,
            },
            QNNLayerType::VariationalLayer {
                num_params: config.transformer_config.model_dim,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];
        let token_embedding = QuantumNeuralNetwork::new(
            embed_layers,
            config.transformer_config.num_qubits,
            config.vocab_size,
            config.transformer_config.model_dim,
        )?;
        let transformer = QuantumTransformer::new(config.transformer_config.clone())?;
        let quantum_memory = QuantumMemorySystem::new(config.memory_config.clone())?;
        let quantum_reasoning = QuantumReasoningModule::new(config.reasoning_config.clone())?;
        let lm_layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: config.transformer_config.model_dim,
            },
            QNNLayerType::VariationalLayer {
                num_params: config.vocab_size,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];
        let lm_head = QuantumNeuralNetwork::new(
            lm_layers,
            config.transformer_config.num_qubits,
            config.transformer_config.model_dim,
            config.vocab_size,
        )?;
        let vocab = Vocabulary::new(config.vocab_size)?;
        let generation_stats = GenerationStatistics::new();
        Ok(Self {
            config,
            token_embedding,
            transformer,
            quantum_memory,
            quantum_reasoning,
            lm_head,
            vocab,
            generation_stats,
        })
    }
    /// Forward pass through the model
    pub fn forward(
        &mut self,
        input_ids: &Array2<usize>,
        attention_mask: Option<&Array3<bool>>,
        use_memory: bool,
        use_reasoning: bool,
    ) -> Result<Array3<f64>> {
        let (batch_size, seq_len) = input_ids.dim();
        if seq_len > self.config.max_context_length {
            return Err(MLError::ConfigurationError(format!(
                "Sequence length {} exceeds maximum context length {}",
                seq_len, self.config.max_context_length
            )));
        }
        let mut embeddings = Array3::zeros((
            batch_size,
            seq_len,
            self.config.transformer_config.model_dim,
        ));
        for batch_idx in 0..batch_size {
            for seq_idx in 0..seq_len {
                let token_id = input_ids[[batch_idx, seq_idx]];
                let token_embedding = self.vocab.get_embedding(token_id)?;
                let embedded = self.token_embedding.forward(&token_embedding)?;
                embeddings
                    .slice_mut(s![batch_idx, seq_idx, ..])
                    .assign(&embedded);
            }
        }
        if use_memory {
            embeddings = self.quantum_memory.enhance_embeddings(&embeddings)?;
        }
        let transformer_output = self.transformer.forward(&embeddings, attention_mask)?;
        let reasoned_output = if use_reasoning {
            self.quantum_reasoning
                .apply_reasoning(&transformer_output)?
        } else {
            transformer_output
        };
        let mut logits = Array3::zeros((batch_size, seq_len, self.config.vocab_size));
        for batch_idx in 0..batch_size {
            for seq_idx in 0..seq_len {
                let hidden_state = reasoned_output.slice(s![batch_idx, seq_idx, ..]).to_owned();
                let token_logits = self.lm_head.forward(&hidden_state)?;
                logits
                    .slice_mut(s![batch_idx, seq_idx, ..])
                    .assign(&token_logits);
            }
        }
        if use_memory {
            self.quantum_memory
                .update_memory(&reasoned_output, input_ids)?;
        }
        Ok(logits)
    }
    /// Generate text with quantum enhancement
    pub fn generate(&mut self, prompt: &str, config: GenerationConfig) -> Result<String> {
        let input_ids = self.vocab.tokenize(prompt)?;
        let mut current_ids = Array1::from_vec(input_ids);
        let mut generated_text = prompt.to_string();
        for step in 0..config.max_length {
            let batch_input = current_ids.clone().insert_axis(Axis(0));
            let input_2d = Array2::from_shape_vec((1, current_ids.len()), current_ids.to_vec())
                .map_err(|e| {
                    MLError::MLOperationError(format!("Failed to create input array: {}", e))
                })?;
            let seq_len = current_ids.len();
            let causal_mask = create_causal_mask(1, seq_len);
            let logits = self.forward(
                &input_2d,
                Some(&causal_mask),
                config.use_memory,
                config.use_quantum_reasoning,
            )?;
            let next_token_logits = logits.slice(s![0, seq_len - 1, ..]).to_owned();
            let final_logits = if config.use_quantum_reasoning && config.chain_of_thought {
                self.quantum_reasoning
                    .enhance_token_selection(&next_token_logits)?
            } else {
                next_token_logits
            };
            let next_token = self.sample_token(&final_logits, &config)?;
            if self.vocab.is_eos_token(next_token) {
                break;
            }
            let new_current = Array1::from_iter(
                current_ids
                    .iter()
                    .cloned()
                    .chain(std::iter::once(next_token)),
            );
            current_ids = new_current;
            let token_text = self.vocab.decode_token(next_token)?;
            generated_text.push_str(&token_text);
            self.generation_stats.total_tokens += 1;
            if step % 10 == 0 {
                let coherence = self.quantum_reasoning.measure_coherence()?;
                self.generation_stats.quantum_coherence = coherence;
            }
        }
        Ok(generated_text)
    }
    /// Sample next token from logits
    fn sample_token(&self, logits: &Array1<f64>, config: &GenerationConfig) -> Result<usize> {
        let mut scores = logits.clone();
        if config.temperature != 1.0 {
            scores = scores / config.temperature;
        }
        if config.repetition_penalty != 1.0 {
            scores = scores * config.repetition_penalty;
        }
        let max_score = scores.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
        let exp_scores = scores.mapv(|x| (x - max_score).exp());
        let sum_exp = exp_scores.sum();
        let mut probs = exp_scores / sum_exp;
        if let Some(k) = config.top_k {
            let mut indexed_probs: Vec<(usize, f64)> =
                probs.iter().enumerate().map(|(i, &p)| (i, p)).collect();
            indexed_probs
                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
            for (i, &(idx, _)) in indexed_probs.iter().enumerate() {
                if i >= k {
                    probs[idx] = 0.0;
                }
            }
            let sum_probs = probs.sum();
            if sum_probs > 0.0 {
                probs = probs / sum_probs;
            }
        }
        if let Some(p) = config.top_p {
            let mut indexed_probs: Vec<(usize, f64)> = probs
                .iter()
                .enumerate()
                .map(|(i, &prob)| (i, prob))
                .collect();
            indexed_probs
                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
            let mut cumulative = 0.0;
            for (idx, prob) in &indexed_probs {
                cumulative += prob;
                if cumulative > p {
                    for (i, &(remaining_idx, _)) in indexed_probs.iter().enumerate() {
                        if cumulative - prob > p {
                            probs[remaining_idx] = 0.0;
                        }
                    }
                    break;
                }
            }
            let sum_probs = probs.sum();
            if sum_probs > 0.0 {
                probs = probs / sum_probs;
            }
        }
        let mut cumulative = 0.0;
        let random_val = fastrand::f64();
        for (i, &prob) in probs.iter().enumerate() {
            cumulative += prob;
            if random_val <= cumulative {
                return Ok(i);
            }
        }
        Ok(probs
            .iter()
            .enumerate()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(i, _)| i)
            .unwrap_or(0))
    }
    /// Get model configuration
    pub fn config(&self) -> &QuantumLLMConfig {
        &self.config
    }
    /// Get generation statistics
    pub fn generation_stats(&self) -> &GenerationStatistics {
        &self.generation_stats
    }
    /// Calculate total model parameters
    pub fn num_parameters(&self) -> usize {
        let mut total = 0;
        total += self.token_embedding.parameters.len();
        total += self.transformer.num_parameters();
        total += self.lm_head.parameters.len();
        total += self.quantum_memory.num_parameters();
        total += self.quantum_reasoning.num_parameters();
        total += self.vocab.quantum_embeddings.len();
        total
    }
    /// Evaluate model perplexity on a dataset
    pub fn evaluate_perplexity(&mut self, texts: &[String]) -> Result<f64> {
        let mut total_log_likelihood = 0.0;
        let mut total_tokens = 0;
        for text in texts {
            let tokens = self.vocab.tokenize(text)?;
            if tokens.len() < 2 {
                continue;
            }
            let tokens_len = tokens.len();
            let input_ids = Array2::from_shape_vec((1, tokens_len), tokens.clone())?;
            let logits = self.forward(&input_ids, None, false, false)?;
            for i in 0..tokens_len - 1 {
                let target_token = tokens[i + 1];
                let token_logits = logits.slice(s![0, i, ..]);
                let max_logit = token_logits
                    .iter()
                    .cloned()
                    .fold(f64::NEG_INFINITY, f64::max);
                let exp_logits = token_logits.mapv(|x| (x - max_logit).exp());
                let sum_exp = exp_logits.sum();
                let prob = exp_logits[target_token] / sum_exp;
                if prob > 1e-10 {
                    total_log_likelihood += prob.ln();
                    total_tokens += 1;
                }
            }
        }
        if total_tokens == 0 {
            return Ok(f64::INFINITY);
        }
        let avg_log_likelihood = total_log_likelihood / total_tokens as f64;
        let perplexity = (-avg_log_likelihood).exp();
        Ok(perplexity)
    }
}
/// Quantum episodic memory
#[derive(Debug, Clone)]
pub struct QuantumEpisode {
    /// Episode context
    context: Array1<f64>,
    /// Episode content
    content: Array2<f64>,
    /// Quantum state representation
    quantum_state: Array1<f64>,
    /// Episode timestamp
    timestamp: f64,
    /// Coherence measure
    coherence: f64,
    /// Episode importance
    importance: f64,
}