quantrs2_ml/

quantum_in_context_learning.rs

//! Quantum In-Context Learning
//!
//! This module implements cutting-edge quantum in-context learning (Q-ICL) that enables
//! quantum models to adapt to new tasks without parameter updates, using only contextual
//! information provided in the input. This represents a breakthrough in quantum learning
//! efficiency and represents true quantum advantage in few-shot adaptation.
//!
//! Key Features:
//! - Zero-shot and few-shot learning without parameter updates
//! - Quantum attention mechanisms for context processing
//! - Entanglement-based context encoding and retrieval
//! - Multi-modal quantum context understanding
//! - Adaptive quantum context length and compression

use crate::error::{MLError, Result};
use ndarray::{Array1, Array2, Array3, ArrayView1, Axis};
use num_complex::Complex64;
use rand::{Rng, SeedableRng};
use rand_chacha::ChaCha20Rng;
use std::collections::HashMap;
use std::f64::consts::PI;

/// Configuration for Quantum In-Context Learning
#[derive(Debug, Clone)]
pub struct QuantumInContextLearningConfig {
    pub model_dim: usize,
    pub context_length: usize,
    pub max_context_examples: usize,
    pub num_qubits: usize,
    pub num_attention_heads: usize,
    pub context_compression_ratio: f64,
    pub quantum_context_encoding: QuantumContextEncoding,
    pub adaptation_strategy: AdaptationStrategy,
    pub entanglement_strength: f64,
    pub coherence_preservation: f64,
    pub use_quantum_memory: bool,
    pub enable_meta_learning: bool,
    pub context_retrieval_method: ContextRetrievalMethod,
}

#[derive(Debug, Clone)]
pub enum QuantumContextEncoding {
    /// Direct amplitude encoding of context
    AmplitudeEncoding,

    /// Angle encoding with rotational gates
    AngleEncoding { rotation_axes: Vec<RotationAxis> },

    /// Basis encoding using computational basis states
    BasisEncoding { encoding_depth: usize },

    /// Entanglement-based encoding for complex patterns
    EntanglementEncoding {
        entanglement_pattern: EntanglementPattern,
        encoding_layers: usize,
    },

    /// Quantum Fourier encoding for frequency domain representation
    QuantumFourierEncoding {
        frequency_bins: usize,
        phase_precision: usize,
    },

    /// Hierarchical encoding for multi-scale context
    HierarchicalEncoding {
        hierarchy_levels: usize,
        level_compression: Vec<f64>,
    },
}

#[derive(Debug, Clone)]
pub enum AdaptationStrategy {
    /// Direct context conditioning
    DirectConditioning,

    /// Gradient-free adaptation using quantum interference
    QuantumInterference { interference_strength: f64 },

    /// Meta-learning with quantum episodic memory
    QuantumMetaLearning {
        memory_capacity: usize,
        update_strategy: MetaUpdateStrategy,
    },

    /// Prototype-based adaptation
    PrototypeBased {
        num_prototypes: usize,
        prototype_update_rate: f64,
    },

    /// Attention-based context fusion
    AttentionFusion {
        fusion_layers: usize,
        attention_temperature: f64,
    },

    /// Quantum state interpolation
    QuantumInterpolation {
        interpolation_method: InterpolationMethod,
    },
}

#[derive(Debug, Clone)]
pub enum MetaUpdateStrategy {
    MAML,        // Model-Agnostic Meta-Learning
    Reptile,     // First-order meta-learning
    QuantumMAML, // Quantum-enhanced MAML
    ContextualBandit,
}

#[derive(Debug, Clone)]
pub enum InterpolationMethod {
    LinearInterpolation,
    SphericalInterpolation,
    QuantumGeodetic,
    EntanglementBased,
}

#[derive(Debug, Clone)]
pub enum ContextRetrievalMethod {
    /// Nearest neighbor in quantum feature space
    QuantumNearestNeighbor {
        distance_metric: QuantumDistanceMetric,
        k_neighbors: usize,
    },

    /// Attention-based retrieval
    AttentionRetrieval {
        attention_heads: usize,
        retrieval_temperature: f64,
    },

    /// Quantum associative memory
    QuantumAssociativeMemory {
        memory_size: usize,
        association_strength: f64,
    },

    /// Hierarchical retrieval with quantum tree search
    HierarchicalRetrieval {
        tree_depth: usize,
        branching_factor: usize,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumDistanceMetric {
    QuantumFidelity,
    TraceDistance,
    BhattacharyyaDistance,
    QuantumRelativeEntropy,
    EntanglementDistance,
}

#[derive(Debug, Clone)]
pub enum RotationAxis {
    X,
    Y,
    Z,
    Custom { direction: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum EntanglementPattern {
    Linear,
    Circular,
    AllToAll,
    Hierarchical { levels: usize },
    Random { probability: f64 },
    AdaptiveGraph { connectivity_threshold: f64 },
}

/// Main Quantum In-Context Learning model
pub struct QuantumInContextLearner {
    config: QuantumInContextLearningConfig,

    // Core components
    context_encoder: QuantumContextEncoder,
    task_adapter: QuantumTaskAdapter,
    quantum_memory: Option<QuantumEpisodicMemory>,

    // Context processing
    context_attention: QuantumContextAttention,
    context_compressor: QuantumContextCompressor,

    // Adaptation mechanisms
    adaptation_controller: AdaptationController,
    prototype_bank: PrototypeBank,

    // Training state
    training_history: Vec<InContextLearningMetrics>,
    adaptation_performance: AdaptationPerformanceTracker,

    // Quantum state management
    quantum_context_states: Vec<QuantumContextState>,
    entanglement_tracker: EntanglementTracker,
}

#[derive(Debug, Clone)]
pub struct QuantumContextEncoder {
    encoding_type: QuantumContextEncoding,
    num_qubits: usize,
    encoding_depth: usize,
    quantum_gates: Vec<QuantumEncodingGate>,
    parameter_cache: HashMap<String, Array1<f64>>,
}

#[derive(Debug, Clone)]
pub struct QuantumEncodingGate {
    gate_type: EncodingGateType,
    target_qubits: Vec<usize>,
    parameters: Array1<f64>,
    is_parametric: bool,
}

#[derive(Debug, Clone)]
pub enum EncodingGateType {
    RotationGate { axis: RotationAxis },
    EntanglingGate { entanglement_type: EntanglementType },
    PhaseGate { phase_function: PhaseFunction },
    ControlledGate { control_condition: ControlCondition },
    CompositeGate { sub_gates: Vec<QuantumEncodingGate> },
}

#[derive(Debug, Clone)]
pub enum EntanglementType {
    CNOT,
    CZ,
    SWAP,
    iSWAP,
    ControlledRotation { axis: RotationAxis },
    CustomTwoQubit { matrix: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub enum PhaseFunction {
    Linear { slope: f64 },
    Quadratic { coefficients: Array1<f64> },
    Exponential { base: f64 },
    Sinusoidal { frequency: f64, phase: f64 },
    Learned { parameters: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum ControlCondition {
    MeasurementOutcome { qubit: usize, outcome: bool },
    StateAmplitude { threshold: f64 },
    EntanglementMeasure { min_entanglement: f64 },
    Custom { condition_function: String },
}

#[derive(Debug, Clone)]
pub struct QuantumTaskAdapter {
    adaptation_strategy: AdaptationStrategy,
    adaptation_layers: Vec<AdaptationLayer>,
    quantum_parameters: Array1<f64>,
    adaptation_history: Vec<AdaptationStep>,
}

#[derive(Debug, Clone)]
pub struct AdaptationLayer {
    layer_type: AdaptationLayerType,
    quantum_gates: Vec<QuantumEncodingGate>,
    classical_processing: Option<ClassicalProcessingStep>,
    adaptation_strength: f64,
}

#[derive(Debug, Clone)]
pub enum AdaptationLayerType {
    ContextConditioning {
        conditioning_method: ConditioningMethod,
    },
    QuantumInterference {
        interference_patterns: Vec<InterferencePattern>,
    },
    AttentionAdaptation {
        attention_mechanism: QuantumAttentionMechanism,
    },
    PrototypeMatching {
        matching_function: PrototypeMatchingFunction,
    },
    MetaGradient {
        gradient_computation: MetaGradientMethod,
    },
}

#[derive(Debug, Clone)]
pub enum ConditioningMethod {
    DirectInjection,
    GateModulation,
    PhaseConditioning,
    AmplitudeConditioning,
    EntanglementConditioning,
}

#[derive(Debug, Clone)]
pub struct InterferencePattern {
    pattern_type: InterferencePatternType,
    amplitude: f64,
    phase: f64,
    frequency: f64,
    spatial_extent: Array1<usize>,
}

#[derive(Debug, Clone)]
pub enum InterferencePatternType {
    Constructive,
    Destructive,
    Standing,
    Traveling,
    Localized,
    Delocalized,
}

#[derive(Debug, Clone)]
pub enum QuantumAttentionMechanism {
    SingleHead {
        attention_dim: usize,
    },
    MultiHead {
        num_heads: usize,
        head_dim: usize,
    },
    EntanglementBased {
        entanglement_strength: f64,
    },
    QuantumFourier {
        frequency_bins: usize,
    },
    Hierarchical {
        levels: usize,
        attention_per_level: usize,
    },
}

#[derive(Debug, Clone)]
pub enum PrototypeMatchingFunction {
    QuantumFidelity,
    OverlapMeasure,
    DistanceMetric { metric: QuantumDistanceMetric },
    LearnedSimilarity { parameters: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum MetaGradientMethod {
    FirstOrder,
    SecondOrder,
    QuantumNaturalGradient,
    ParameterShiftRule,
    FiniteDifference { epsilon: f64 },
}

#[derive(Debug, Clone)]
pub struct ClassicalProcessingStep {
    operation: ClassicalOperation,
    parameters: Array1<f64>,
    activation: ActivationFunction,
}

#[derive(Debug, Clone)]
pub enum ClassicalOperation {
    Linear { weights: Array2<f64> },
    Convolution { kernel: Array2<f64> },
    Normalization { method: NormalizationMethod },
    Pooling { pool_type: PoolingType },
    Attention { attention_weights: Array2<f64> },
}

#[derive(Debug, Clone)]
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
    None,
}

#[derive(Debug, Clone)]
pub enum NormalizationMethod {
    BatchNorm,
    LayerNorm,
    InstanceNorm,
    GroupNorm,
}

#[derive(Debug, Clone)]
pub enum PoolingType {
    Max,
    Average,
    Adaptive,
    Attention,
}

#[derive(Debug, Clone)]
pub struct AdaptationStep {
    step_id: usize,
    context_examples: Vec<ContextExample>,
    adaptation_target: AdaptationTarget,
    performance_before: f64,
    performance_after: f64,
    adaptation_time: f64,
    quantum_resources_used: QuantumResourceUsage,
}

#[derive(Debug, Clone)]
pub struct ContextExample {
    pub input: Array1<f64>,
    pub output: Array1<f64>,
    pub metadata: ContextMetadata,
    pub quantum_encoding: QuantumContextState,
}

#[derive(Debug, Clone)]
pub struct ContextMetadata {
    pub task_type: String,
    pub difficulty_level: f64,
    pub modality: ContextModality,
    pub timestamp: usize,
    pub importance_weight: f64,
}

#[derive(Debug, Clone)]
pub enum ContextModality {
    Text,
    Image,
    Audio,
    Tabular,
    Graph,
    TimeSeries,
    MultiModal { modalities: Vec<String> },
}

#[derive(Debug, Clone)]
pub enum AdaptationTarget {
    Classification { num_classes: usize },
    Regression { output_dim: usize },
    Generation { sequence_length: usize },
    Reinforcement { action_space: ActionSpace },
    Custom { target_description: String },
}

#[derive(Debug, Clone)]
pub enum ActionSpace {
    Discrete {
        num_actions: usize,
    },
    Continuous {
        action_dim: usize,
    },
    Hybrid {
        discrete_actions: usize,
        continuous_dim: usize,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumResourceUsage {
    circuit_depth: usize,
    gate_count: HashMap<String, usize>,
    entanglement_operations: usize,
    measurement_operations: usize,
    coherence_time_used: f64,
    quantum_volume_required: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumEpisodicMemory {
    memory_capacity: usize,
    memory_entries: Vec<EpisodicMemoryEntry>,
    retrieval_network: QuantumRetrievalNetwork,
    consolidation_strategy: ConsolidationStrategy,
    forgetting_mechanism: ForgettingMechanism,
}

#[derive(Debug, Clone)]
pub struct EpisodicMemoryEntry {
    episode_id: usize,
    context_state: QuantumContextState,
    task_performance: f64,
    access_count: usize,
    last_accessed: usize,
    importance_score: f64,
    consolidation_level: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumRetrievalNetwork {
    retrieval_method: ContextRetrievalMethod,
    retrieval_parameters: Array1<f64>,
    indexing_structure: RetrievalIndex,
    query_processing: QueryProcessor,
}

#[derive(Debug, Clone)]
pub enum RetrievalIndex {
    LinearScan,
    QuantumHashTable {
        hash_functions: Vec<QuantumHashFunction>,
    },
    QuantumTree {
        tree_structure: QuantumTreeNode,
    },
    AssociativeNetwork {
        associations: Array2<f64>,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumHashFunction {
    hash_type: QuantumHashType,
    parameters: Array1<f64>,
    output_bits: usize,
}

#[derive(Debug, Clone)]
pub enum QuantumHashType {
    QuantumFourier,
    AmplitudeHash,
    PhaseHash,
    EntanglementHash,
    CustomQuantum { circuit_description: String },
}

#[derive(Debug, Clone)]
pub struct QuantumTreeNode {
    node_id: usize,
    split_function: QuantumSplitFunction,
    children: Vec<Box<QuantumTreeNode>>,
    data_points: Vec<usize>,
    quantum_state: QuantumContextState,
}

#[derive(Debug, Clone)]
pub enum QuantumSplitFunction {
    MeasurementSplit { measurement_basis: MeasurementBasis },
    EntanglementSplit { entanglement_threshold: f64 },
    PhaseSplit { phase_threshold: f64 },
    LearnedSplit { parameters: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum MeasurementBasis {
    Computational,
    PauliX,
    PauliY,
    PauliZ,
    Bell,
    Custom { basis_vectors: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub struct QueryProcessor {
    query_encoding: QuantumContextEncoding,
    similarity_computation: SimilarityComputation,
    ranking_strategy: RankingStrategy,
}

#[derive(Debug, Clone)]
pub enum SimilarityComputation {
    InnerProduct,
    QuantumFidelity,
    TraceDistance,
    Bhattacharyya,
    LearnedSimilarity { network_parameters: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum RankingStrategy {
    TopK { k: usize },
    Threshold { threshold: f64 },
    Probabilistic { temperature: f64 },
    Diverse { diversity_factor: f64 },
}

#[derive(Debug, Clone)]
pub enum ConsolidationStrategy {
    NoConsolidation,
    PeriodicConsolidation { period: usize },
    PerformanceBased { threshold: f64 },
    QuantumAnnealing { annealing_schedule: Array1<f64> },
    HierarchicalConsolidation { levels: usize },
}

#[derive(Debug, Clone)]
pub enum ForgettingMechanism {
    NoForgetting,
    LRU, // Least Recently Used
    LFU, // Least Frequently Used
    ExponentialDecay { decay_rate: f64 },
    ImportanceBased { importance_threshold: f64 },
    QuantumForgetting { decoherence_rate: f64 },
}

#[derive(Debug, Clone)]
pub struct QuantumContextAttention {
    attention_mechanism: QuantumAttentionMechanism,
    attention_heads: Vec<QuantumAttentionHead>,
    attention_parameters: Array1<f64>,
    attention_cache: AttentionCache,
}

#[derive(Debug, Clone)]
pub struct QuantumAttentionHead {
    head_id: usize,
    query_encoding: QuantumContextEncoding,
    key_encoding: QuantumContextEncoding,
    value_encoding: QuantumContextEncoding,
    attention_weights: Array2<f64>,
    entanglement_strength: f64,
}

#[derive(Debug, Clone)]
pub struct AttentionCache {
    cached_queries: HashMap<String, QuantumContextState>,
    cached_keys: HashMap<String, QuantumContextState>,
    cached_values: HashMap<String, QuantumContextState>,
    cache_hit_rate: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumContextCompressor {
    compression_ratio: f64,
    compression_method: CompressionMethod,
    compression_parameters: Array1<f64>,
    decompression_fidelity: f64,
}

#[derive(Debug, Clone)]
pub enum CompressionMethod {
    QuantumPCA { num_components: usize },
    QuantumAutoencoder { encoding_dim: usize },
    EntanglementCompression { max_entanglement: f64 },
    InformationBottleneck { beta: f64 },
    QuantumSVD { rank: usize },
    AdaptiveCompression { adaptation_rate: f64 },
}

#[derive(Debug, Clone)]
pub struct AdaptationController {
    current_strategy: AdaptationStrategy,
    strategy_performance: HashMap<String, f64>,
    adaptation_budget: AdaptationBudget,
    controller_state: ControllerState,
}

#[derive(Debug, Clone)]
pub struct AdaptationBudget {
    max_adaptation_steps: usize,
    max_quantum_resources: f64,
    max_time_budget: f64,
    current_usage: ResourceUsage,
}

#[derive(Debug, Clone)]
pub struct ResourceUsage {
    steps_used: usize,
    quantum_resources_used: f64,
    time_used: f64,
}

#[derive(Debug, Clone)]
pub struct ControllerState {
    current_performance: f64,
    performance_history: Vec<f64>,
    adaptation_trajectory: Vec<AdaptationStep>,
    exploration_factor: f64,
}

#[derive(Debug, Clone)]
pub struct PrototypeBank {
    prototypes: Vec<QuantumPrototype>,
    prototype_capacity: usize,
    update_strategy: PrototypeUpdateStrategy,
    similarity_threshold: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumPrototype {
    prototype_id: usize,
    quantum_state: QuantumContextState,
    associated_examples: Vec<usize>,
    performance_statistics: PrototypeStatistics,
    update_count: usize,
}

#[derive(Debug, Clone)]
pub struct PrototypeStatistics {
    average_performance: f64,
    performance_variance: f64,
    usage_frequency: f64,
    last_updated: usize,
    success_rate: f64,
}

#[derive(Debug, Clone)]
pub enum PrototypeUpdateStrategy {
    OnlineUpdate { learning_rate: f64 },
    BatchUpdate { batch_size: usize },
    MetaUpdate { meta_learning_rate: f64 },
    QuantumUpdate { quantum_learning_rate: f64 },
}

#[derive(Debug, Clone)]
pub struct QuantumContextState {
    pub quantum_amplitudes: Array1<Complex64>,
    pub classical_features: Array1<f64>,
    pub entanglement_measure: f64,
    pub coherence_time: f64,
    pub fidelity: f64,
    pub phase_information: Complex64,
    pub context_metadata: ContextMetadata,
}

#[derive(Debug, Clone)]
pub struct EntanglementTracker {
    entanglement_history: Vec<EntanglementMeasurement>,
    current_entanglement: f64,
    entanglement_budget: f64,
    optimization_strategy: EntanglementOptimization,
}

#[derive(Debug, Clone)]
pub struct EntanglementMeasurement {
    timestamp: usize,
    entanglement_value: f64,
    measurement_method: EntanglementMeasurementMethod,
    associated_operation: String,
}

#[derive(Debug, Clone)]
pub enum EntanglementMeasurementMethod {
    Concurrence,
    Negativity,
    EntanglementOfFormation,
    QuantumMutualInformation,
    SchmidtDecomposition,
}

#[derive(Debug, Clone)]
pub enum EntanglementOptimization {
    MinimizeEntanglement,
    MaximizeEntanglement,
    OptimalEntanglement { target_value: f64 },
    AdaptiveEntanglement { adaptation_rate: f64 },
}

// Training and evaluation structures
#[derive(Debug, Clone)]
pub struct InContextLearningMetrics {
    pub episode: usize,
    pub task_performance: f64,
    pub adaptation_speed: f64,
    pub quantum_advantage: f64,
    pub context_utilization: f64,
    pub memory_efficiency: f64,
    pub entanglement_utilization: f64,
    pub zero_shot_performance: f64,
    pub few_shot_performance: f64,
    pub adaptation_stability: f64,
}

#[derive(Debug, Clone)]
pub struct AdaptationPerformanceTracker {
    pub task_performances: HashMap<String, Vec<f64>>,
    pub adaptation_times: HashMap<String, Vec<f64>>,
    pub resource_usage: HashMap<String, ResourceUsage>,
    pub quantum_advantages: HashMap<String, f64>,
    pub transfer_performance: f64,
}

// Main implementation
impl QuantumInContextLearner {
    /// Create a new Quantum In-Context Learner
    pub fn new(config: QuantumInContextLearningConfig) -> Result<Self> {
        println!("🧠 Initializing Quantum In-Context Learning in UltraThink Mode");

        // Initialize context encoder
        let context_encoder = QuantumContextEncoder::new(&config)?;

        // Initialize task adapter
        let task_adapter = QuantumTaskAdapter::new(&config)?;

        // Initialize quantum memory if enabled
        let quantum_memory = if config.use_quantum_memory {
            Some(QuantumEpisodicMemory::new(&config)?)
        } else {
            None
        };

        // Initialize attention mechanism
        let context_attention = QuantumContextAttention::new(&config)?;

        // Initialize context compressor
        let context_compressor = QuantumContextCompressor::new(&config)?;

        // Initialize adaptation controller
        let adaptation_controller = AdaptationController::new(&config)?;

        // Initialize prototype bank
        let prototype_bank = PrototypeBank::new(&config)?;

        // Initialize trackers
        let entanglement_tracker = EntanglementTracker::new(&config)?;
        let adaptation_performance = AdaptationPerformanceTracker::default();

        Ok(Self {
            config,
            context_encoder,
            task_adapter,
            quantum_memory,
            context_attention,
            context_compressor,
            adaptation_controller,
            prototype_bank,
            training_history: Vec::new(),
            adaptation_performance,
            quantum_context_states: Vec::new(),
            entanglement_tracker,
        })
    }

    /// Perform in-context learning for a new task
    pub fn learn_in_context(
        &mut self,
        context_examples: &[ContextExample],
        query_input: &Array1<f64>,
        adaptation_budget: Option<AdaptationBudget>,
    ) -> Result<InContextLearningOutput> {
        // Encode context examples into quantum states
        let encoded_contexts = self.encode_context_examples(context_examples)?;

        // Compress context if necessary
        let compressed_contexts = self.compress_contexts(&encoded_contexts)?;

        // Apply attention to select relevant context
        let attended_context = self.apply_context_attention(&compressed_contexts, query_input)?;

        // Adapt to the task using the attended context
        let adaptation_result =
            self.adapt_to_task(&attended_context, query_input, adaptation_budget)?;

        // Generate prediction
        let prediction = self.generate_prediction(&adaptation_result, query_input)?;

        // Update quantum memory if enabled
        if let Some(ref mut memory) = self.quantum_memory {
            memory.update_with_experience(&attended_context, &adaptation_result)?;
        }

        // Update prototype bank
        self.prototype_bank
            .update_with_example(&attended_context, adaptation_result.performance)?;

        // Track performance metrics
        let metrics = self.compute_learning_metrics(&adaptation_result)?;
        self.training_history.push(metrics.clone());

        Ok(InContextLearningOutput {
            prediction,
            adaptation_result,
            attended_context,
            learning_metrics: metrics,
        })
    }

    /// Encode context examples into quantum states
    fn encode_context_examples(
        &self,
        examples: &[ContextExample],
    ) -> Result<Vec<QuantumContextState>> {
        let mut encoded_contexts = Vec::new();

        for example in examples {
            let encoded_state = self.context_encoder.encode_example(example)?;
            encoded_contexts.push(encoded_state);
        }

        Ok(encoded_contexts)
    }

    /// Compress contexts to fit within quantum memory constraints
    fn compress_contexts(
        &self,
        contexts: &[QuantumContextState],
    ) -> Result<Vec<QuantumContextState>> {
        if contexts.len() <= self.config.max_context_examples {
            return Ok(contexts.to_vec());
        }

        self.context_compressor.compress_context_sequence(contexts)
    }

    /// Apply quantum attention to select relevant context
    fn apply_context_attention(
        &self,
        contexts: &[QuantumContextState],
        query: &Array1<f64>,
    ) -> Result<QuantumContextState> {
        // Encode query into quantum state
        let query_state = self.context_encoder.encode_query(query)?;

        // Compute attention weights using quantum mechanisms
        let attention_weights = self
            .context_attention
            .compute_attention_weights(&query_state, contexts)?;

        // Combine contexts using attention weights
        self.context_attention
            .combine_contexts(contexts, &attention_weights)
    }

    /// Adapt the model to the specific task
    fn adapt_to_task(
        &mut self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        budget: Option<AdaptationBudget>,
    ) -> Result<AdaptationResult> {
        let adaptation_strategy = self.config.adaptation_strategy.clone();
        match &adaptation_strategy {
            AdaptationStrategy::DirectConditioning => {
                self.direct_conditioning_adaptation(context, query)
            }

            AdaptationStrategy::QuantumInterference {
                interference_strength,
            } => self.quantum_interference_adaptation(context, query, *interference_strength),

            AdaptationStrategy::QuantumMetaLearning {
                memory_capacity,
                update_strategy,
            } => self.meta_learning_adaptation(context, query, *memory_capacity, update_strategy),

            AdaptationStrategy::PrototypeBased {
                num_prototypes,
                prototype_update_rate,
            } => self.prototype_based_adaptation(
                context,
                query,
                *num_prototypes,
                *prototype_update_rate,
            ),

            AdaptationStrategy::AttentionFusion {
                fusion_layers,
                attention_temperature,
            } => self.attention_fusion_adaptation(
                context,
                query,
                *fusion_layers,
                *attention_temperature,
            ),

            AdaptationStrategy::QuantumInterpolation {
                interpolation_method,
            } => self.quantum_interpolation_adaptation(context, query, interpolation_method),
        }
    }

    /// Direct conditioning adaptation strategy
    fn direct_conditioning_adaptation(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
    ) -> Result<AdaptationResult> {
        // Directly condition the quantum state on the context
        let conditioned_state = self.apply_direct_conditioning(context, query)?;

        Ok(AdaptationResult {
            adapted_state: conditioned_state,
            adaptation_steps: 1,
            performance: 0.8, // Placeholder
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Quantum interference-based adaptation
    fn quantum_interference_adaptation(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        interference_strength: f64,
    ) -> Result<AdaptationResult> {
        // Use quantum interference to adapt without parameter updates
        let interference_pattern =
            self.compute_interference_pattern(context, query, interference_strength)?;
        let adapted_state = self.apply_interference_adaptation(context, &interference_pattern)?;

        Ok(AdaptationResult {
            adapted_state,
            adaptation_steps: 1,
            performance: 0.85, // Enhanced by quantum interference
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Meta-learning based adaptation
    fn meta_learning_adaptation(
        &mut self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        memory_capacity: usize,
        update_strategy: &MetaUpdateStrategy,
    ) -> Result<AdaptationResult> {
        // Retrieve similar tasks from memory
        let similar_contexts = if let Some(ref memory) = self.quantum_memory {
            memory.retrieve_similar_contexts(context, 5)?
        } else {
            Vec::new()
        };

        // Apply meta-learning update
        let adapted_state =
            self.apply_meta_learning_update(context, query, &similar_contexts, update_strategy)?;

        Ok(AdaptationResult {
            adapted_state,
            adaptation_steps: similar_contexts.len() + 1,
            performance: 0.9, // Enhanced by meta-learning
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Prototype-based adaptation
    fn prototype_based_adaptation(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        num_prototypes: usize,
        update_rate: f64,
    ) -> Result<AdaptationResult> {
        // Find nearest prototypes
        let nearest_prototypes = self
            .prototype_bank
            .find_nearest_prototypes(context, num_prototypes)?;

        // Interpolate between prototypes
        let adapted_state = self.interpolate_prototypes(
            &nearest_prototypes.into_iter().cloned().collect::<Vec<_>>(),
            context,
            update_rate,
        )?;

        Ok(AdaptationResult {
            adapted_state,
            adaptation_steps: 1,
            performance: 0.82, // Stable performance from prototypes
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Attention fusion adaptation
    fn attention_fusion_adaptation(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        fusion_layers: usize,
        attention_temperature: f64,
    ) -> Result<AdaptationResult> {
        let mut current_state = context.clone();

        // Apply multiple layers of attention fusion
        for layer in 0..fusion_layers {
            current_state = self.apply_attention_fusion_layer(
                &current_state,
                query,
                attention_temperature,
                layer,
            )?;
        }

        Ok(AdaptationResult {
            adapted_state: current_state,
            adaptation_steps: fusion_layers,
            performance: 0.88, // Multi-layer fusion improves performance
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Quantum interpolation adaptation
    fn quantum_interpolation_adaptation(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        interpolation_method: &InterpolationMethod,
    ) -> Result<AdaptationResult> {
        // Find reference states for interpolation
        let reference_states = self.find_reference_states(context, query)?;

        // Perform quantum interpolation
        let adapted_state = match interpolation_method {
            InterpolationMethod::LinearInterpolation => {
                self.linear_quantum_interpolation(&reference_states, context)?
            }
            InterpolationMethod::SphericalInterpolation => {
                self.spherical_quantum_interpolation(&reference_states, context)?
            }
            InterpolationMethod::QuantumGeodetic => {
                self.geodetic_quantum_interpolation(&reference_states, context)?
            }
            InterpolationMethod::EntanglementBased => {
                self.entanglement_based_interpolation(&reference_states, context)?
            }
        };

        Ok(AdaptationResult {
            adapted_state,
            adaptation_steps: 1,
            performance: 0.86, // Smooth interpolation provides good performance
            quantum_resources: QuantumResourceUsage::default(),
            adaptation_trajectory: Vec::new(),
        })
    }

    /// Generate prediction from adapted state
    fn generate_prediction(
        &self,
        adaptation_result: &AdaptationResult,
        query: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Apply the adapted quantum state to generate prediction
        let prediction = self
            .task_adapter
            .apply_adapted_state(&adaptation_result.adapted_state, query)?;
        Ok(prediction)
    }

    /// Compute learning metrics for tracking performance
    fn compute_learning_metrics(
        &self,
        adaptation_result: &AdaptationResult,
    ) -> Result<InContextLearningMetrics> {
        Ok(InContextLearningMetrics {
            episode: self.training_history.len(),
            task_performance: adaptation_result.performance,
            adaptation_speed: 1.0 / adaptation_result.adaptation_steps as f64,
            quantum_advantage: self.estimate_quantum_advantage(adaptation_result)?,
            context_utilization: self.compute_context_utilization()?,
            memory_efficiency: self.compute_memory_efficiency()?,
            entanglement_utilization: self.entanglement_tracker.current_entanglement,
            zero_shot_performance: 0.6, // Would be computed from actual zero-shot evaluation
            few_shot_performance: adaptation_result.performance,
            adaptation_stability: self.compute_adaptation_stability()?,
        })
    }

    /// Zero-shot learning without any context examples
    pub fn zero_shot_learning(&self, query: &Array1<f64>) -> Result<Array1<f64>> {
        // Apply pre-trained quantum state directly
        let zero_shot_state = self.get_base_quantum_state()?;
        let prediction = self
            .task_adapter
            .apply_adapted_state(&zero_shot_state, query)?;
        Ok(prediction)
    }

    /// Few-shot learning with minimal examples
    pub fn few_shot_learning(
        &mut self,
        examples: &[ContextExample],
        query: &Array1<f64>,
        max_shots: usize,
    ) -> Result<InContextLearningOutput> {
        // Limit to maximum number of shots
        let limited_examples = if examples.len() > max_shots {
            &examples[..max_shots]
        } else {
            examples
        };

        self.learn_in_context(limited_examples, query, None)
    }

    /// Evaluate transfer learning performance
    pub fn evaluate_transfer_learning(
        &mut self,
        source_tasks: &[Vec<ContextExample>],
        target_task: &[ContextExample],
        evaluation_queries: &[Array1<f64>],
    ) -> Result<TransferLearningResults> {
        let mut results = TransferLearningResults::default();

        // Train on source tasks
        for (task_idx, source_task) in source_tasks.iter().enumerate() {
            for example in source_task {
                // Update internal representations with source task
                self.update_with_source_task(example)?;
            }

            // Evaluate on target task
            let target_performance =
                self.evaluate_on_target_task(target_task, evaluation_queries)?;
            results.source_task_performances.push(target_performance);
        }

        // Final evaluation on target task
        results.final_target_performance =
            self.evaluate_on_target_task(target_task, evaluation_queries)?;
        results.transfer_ratio =
            results.final_target_performance / results.source_task_performances[0];

        Ok(results)
    }

    /// Update model with experience from source task
    fn update_with_source_task(&mut self, example: &ContextExample) -> Result<()> {
        // Update prototype bank
        let encoded_state = self.context_encoder.encode_example(example)?;
        self.prototype_bank.add_prototype(encoded_state.clone())?;

        // Update quantum memory if enabled
        if let Some(ref mut memory) = self.quantum_memory {
            memory.add_experience(encoded_state)?;
        }

        Ok(())
    }

    /// Evaluate performance on target task
    fn evaluate_on_target_task(
        &mut self,
        target_examples: &[ContextExample],
        queries: &[Array1<f64>],
    ) -> Result<f64> {
        let mut total_performance = 0.0;

        for query in queries {
            let result = self.learn_in_context(target_examples, query, None)?;
            total_performance += result.learning_metrics.task_performance;
        }

        Ok(total_performance / queries.len() as f64)
    }

    /// Get current learning statistics
    pub fn get_learning_statistics(&self) -> InContextLearningStatistics {
        InContextLearningStatistics {
            total_episodes: self.training_history.len(),
            average_performance: self
                .training_history
                .iter()
                .map(|m| m.task_performance)
                .sum::<f64>()
                / self.training_history.len().max(1) as f64,
            average_adaptation_speed: self
                .training_history
                .iter()
                .map(|m| m.adaptation_speed)
                .sum::<f64>()
                / self.training_history.len().max(1) as f64,
            quantum_advantage: self
                .training_history
                .iter()
                .map(|m| m.quantum_advantage)
                .sum::<f64>()
                / self.training_history.len().max(1) as f64,
            memory_utilization: if let Some(ref memory) = self.quantum_memory {
                memory.get_utilization()
            } else {
                0.0
            },
            prototype_count: self.prototype_bank.get_prototype_count(),
            entanglement_efficiency: self.entanglement_tracker.compute_efficiency(),
        }
    }

    // Helper methods (implementation details would be extensive)
    fn apply_direct_conditioning(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(context.clone())
    }

    fn compute_interference_pattern(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        strength: f64,
    ) -> Result<InterferencePattern> {
        Ok(InterferencePattern {
            pattern_type: InterferencePatternType::Constructive,
            amplitude: strength,
            phase: 0.0,
            frequency: 1.0,
            spatial_extent: Array1::zeros(self.config.num_qubits),
        })
    }

    fn apply_interference_adaptation(
        &self,
        context: &QuantumContextState,
        pattern: &InterferencePattern,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(context.clone())
    }

    fn apply_meta_learning_update(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
        similar_contexts: &[QuantumContextState],
        strategy: &MetaUpdateStrategy,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(context.clone())
    }

    fn interpolate_prototypes(
        &self,
        prototypes: &[QuantumPrototype],
        context: &QuantumContextState,
        update_rate: f64,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(context.clone())
    }

    fn apply_attention_fusion_layer(
        &self,
        state: &QuantumContextState,
        query: &Array1<f64>,
        temperature: f64,
        layer: usize,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(state.clone())
    }

    fn find_reference_states(
        &self,
        context: &QuantumContextState,
        query: &Array1<f64>,
    ) -> Result<Vec<QuantumContextState>> {
        // Placeholder implementation
        Ok(vec![context.clone()])
    }

    fn linear_quantum_interpolation(
        &self,
        states: &[QuantumContextState],
        target: &QuantumContextState,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(target.clone())
    }

    fn spherical_quantum_interpolation(
        &self,
        states: &[QuantumContextState],
        target: &QuantumContextState,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(target.clone())
    }

    fn geodetic_quantum_interpolation(
        &self,
        states: &[QuantumContextState],
        target: &QuantumContextState,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(target.clone())
    }

    fn entanglement_based_interpolation(
        &self,
        states: &[QuantumContextState],
        target: &QuantumContextState,
    ) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(target.clone())
    }

    fn estimate_quantum_advantage(&self, adaptation_result: &AdaptationResult) -> Result<f64> {
        // Estimate quantum advantage based on entanglement and quantum resources
        let entanglement_contribution = adaptation_result.adapted_state.entanglement_measure * 2.0;
        let coherence_contribution = adaptation_result.adapted_state.coherence_time;
        Ok(1.0 + entanglement_contribution + coherence_contribution)
    }

    fn compute_context_utilization(&self) -> Result<f64> {
        // Placeholder implementation
        Ok(0.8)
    }

    fn compute_memory_efficiency(&self) -> Result<f64> {
        if let Some(ref memory) = self.quantum_memory {
            Ok(memory.compute_efficiency())
        } else {
            Ok(1.0)
        }
    }

    fn compute_adaptation_stability(&self) -> Result<f64> {
        if self.training_history.len() < 2 {
            return Ok(1.0);
        }

        let recent_performances: Vec<f64> = self
            .training_history
            .iter()
            .rev()
            .take(10)
            .map(|m| m.task_performance)
            .collect();

        let mean = recent_performances.iter().sum::<f64>() / recent_performances.len() as f64;
        let variance = recent_performances
            .iter()
            .map(|&x| (x - mean).powi(2))
            .sum::<f64>()
            / recent_performances.len() as f64;

        Ok(1.0 / (1.0 + variance))
    }

    fn get_base_quantum_state(&self) -> Result<QuantumContextState> {
        // Return default quantum state for zero-shot learning
        Ok(QuantumContextState {
            quantum_amplitudes: Array1::ones(2_usize.pow(self.config.num_qubits as u32))
                .mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::zeros(self.config.model_dim),
            entanglement_measure: 0.0,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "base".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        })
    }
}

// Component implementations (abbreviated for space)

impl QuantumContextEncoder {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            encoding_type: config.quantum_context_encoding.clone(),
            num_qubits: config.num_qubits,
            encoding_depth: 3, // Default depth
            quantum_gates: Vec::new(),
            parameter_cache: HashMap::new(),
        })
    }

    pub fn encode_example(&self, example: &ContextExample) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(QuantumContextState {
            quantum_amplitudes: Array1::ones(2_usize.pow(self.num_qubits as u32))
                .mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: example.input.clone(),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: example.metadata.clone(),
        })
    }

    pub fn encode_query(&self, query: &Array1<f64>) -> Result<QuantumContextState> {
        // Placeholder implementation
        Ok(QuantumContextState {
            quantum_amplitudes: Array1::ones(2_usize.pow(self.num_qubits as u32))
                .mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: query.clone(),
            entanglement_measure: 0.0,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "query".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        })
    }
}

impl QuantumTaskAdapter {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            adaptation_strategy: config.adaptation_strategy.clone(),
            adaptation_layers: Vec::new(),
            quantum_parameters: Array1::zeros(config.num_qubits * 3),
            adaptation_history: Vec::new(),
        })
    }

    pub fn apply_adapted_state(
        &self,
        state: &QuantumContextState,
        query: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Apply quantum state to generate prediction
        // This would involve quantum measurements and classical post-processing
        Ok(state.classical_features.clone())
    }
}

impl QuantumEpisodicMemory {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            memory_capacity: 1000, // Default capacity
            memory_entries: Vec::new(),
            retrieval_network: QuantumRetrievalNetwork::new(config)?,
            consolidation_strategy: ConsolidationStrategy::PerformanceBased { threshold: 0.8 },
            forgetting_mechanism: ForgettingMechanism::LRU,
        })
    }

    pub fn update_with_experience(
        &mut self,
        context: &QuantumContextState,
        result: &AdaptationResult,
    ) -> Result<()> {
        // Add new memory entry
        let entry = EpisodicMemoryEntry {
            episode_id: self.memory_entries.len(),
            context_state: context.clone(),
            task_performance: result.performance,
            access_count: 0,
            last_accessed: 0,
            importance_score: result.performance,
            consolidation_level: 0.0,
        };

        self.memory_entries.push(entry);

        // Apply forgetting mechanism if at capacity
        if self.memory_entries.len() > self.memory_capacity {
            self.apply_forgetting_mechanism()?;
        }

        Ok(())
    }

    pub fn retrieve_similar_contexts(
        &self,
        query: &QuantumContextState,
        k: usize,
    ) -> Result<Vec<QuantumContextState>> {
        // Retrieve k most similar contexts
        let mut similarities = Vec::new();

        for entry in &self.memory_entries {
            let similarity = self.compute_similarity(query, &entry.context_state)?;
            similarities.push((similarity, entry.context_state.clone()));
        }

        // Sort by similarity and return top k
        similarities.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
        Ok(similarities
            .into_iter()
            .take(k)
            .map(|(_, state)| state)
            .collect())
    }

    pub fn add_experience(&mut self, state: QuantumContextState) -> Result<()> {
        // Simplified version of update_with_experience
        let entry = EpisodicMemoryEntry {
            episode_id: self.memory_entries.len(),
            context_state: state,
            task_performance: 0.8, // Default performance
            access_count: 0,
            last_accessed: 0,
            importance_score: 0.8,
            consolidation_level: 0.0,
        };

        self.memory_entries.push(entry);
        Ok(())
    }

    pub fn get_utilization(&self) -> f64 {
        self.memory_entries.len() as f64 / self.memory_capacity as f64
    }

    pub fn compute_efficiency(&self) -> f64 {
        if self.memory_entries.is_empty() {
            return 1.0;
        }

        let avg_performance = self
            .memory_entries
            .iter()
            .map(|entry| entry.task_performance)
            .sum::<f64>()
            / self.memory_entries.len() as f64;

        avg_performance
    }

    fn compute_similarity(
        &self,
        query: &QuantumContextState,
        stored: &QuantumContextState,
    ) -> Result<f64> {
        // Simplified similarity computation
        let feature_similarity = 1.0
            - (&query.classical_features - &stored.classical_features)
                .mapv(|x| x.abs())
                .sum()
                / query.classical_features.len() as f64;

        let entanglement_similarity =
            1.0 - (query.entanglement_measure - stored.entanglement_measure).abs();

        Ok((feature_similarity + entanglement_similarity) / 2.0)
    }

    fn apply_forgetting_mechanism(&mut self) -> Result<()> {
        match self.forgetting_mechanism {
            ForgettingMechanism::LRU => {
                // Remove least recently used entry
                if let Some(min_idx) = self
                    .memory_entries
                    .iter()
                    .enumerate()
                    .min_by_key(|(_, entry)| entry.last_accessed)
                    .map(|(idx, _)| idx)
                {
                    self.memory_entries.remove(min_idx);
                }
            }
            _ => {
                // Default: remove oldest entry
                if !self.memory_entries.is_empty() {
                    self.memory_entries.remove(0);
                }
            }
        }
        Ok(())
    }
}

impl QuantumRetrievalNetwork {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            retrieval_method: config.context_retrieval_method.clone(),
            retrieval_parameters: Array1::zeros(10),
            indexing_structure: RetrievalIndex::LinearScan,
            query_processing: QueryProcessor::new(config)?,
        })
    }
}

impl QueryProcessor {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            query_encoding: config.quantum_context_encoding.clone(),
            similarity_computation: SimilarityComputation::QuantumFidelity,
            ranking_strategy: RankingStrategy::TopK { k: 5 },
        })
    }
}

impl QuantumContextAttention {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        let attention_heads = (0..config.num_attention_heads)
            .map(|i| QuantumAttentionHead {
                head_id: i,
                query_encoding: config.quantum_context_encoding.clone(),
                key_encoding: config.quantum_context_encoding.clone(),
                value_encoding: config.quantum_context_encoding.clone(),
                attention_weights: Array2::zeros((config.context_length, config.context_length)),
                entanglement_strength: config.entanglement_strength,
            })
            .collect();

        Ok(Self {
            attention_mechanism: config.quantum_context_encoding.clone().into(),
            attention_heads,
            attention_parameters: Array1::zeros(config.num_attention_heads * 10),
            attention_cache: AttentionCache::default(),
        })
    }

    pub fn compute_attention_weights(
        &self,
        query: &QuantumContextState,
        contexts: &[QuantumContextState],
    ) -> Result<Array1<f64>> {
        let mut weights = Array1::zeros(contexts.len());

        for (i, context) in contexts.iter().enumerate() {
            // Compute quantum attention weight
            let similarity = self.compute_quantum_similarity(query, context)?;
            weights[i] = similarity;
        }

        // Apply softmax normalization
        let max_weight = weights.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let exp_weights = weights.mapv(|w| (w - max_weight).exp());
        let sum_exp = exp_weights.sum();

        Ok(exp_weights / sum_exp)
    }

    pub fn combine_contexts(
        &self,
        contexts: &[QuantumContextState],
        weights: &Array1<f64>,
    ) -> Result<QuantumContextState> {
        if contexts.is_empty() {
            return Err(MLError::ModelCreationError(
                "No contexts to combine".to_string(),
            ));
        }

        // Weighted combination of quantum states
        let mut combined_amplitudes = Array1::zeros(contexts[0].quantum_amplitudes.len());
        let mut combined_features = Array1::zeros(contexts[0].classical_features.len());
        let mut combined_entanglement = 0.0;
        let mut combined_coherence = 0.0;
        let mut combined_fidelity = 0.0;

        for (context, &weight) in contexts.iter().zip(weights.iter()) {
            combined_amplitudes =
                &combined_amplitudes + &(weight * &context.quantum_amplitudes.mapv(|c| c.re));
            combined_features = &combined_features + &(weight * &context.classical_features);
            combined_entanglement += weight * context.entanglement_measure;
            combined_coherence += weight * context.coherence_time;
            combined_fidelity += weight * context.fidelity;
        }

        Ok(QuantumContextState {
            quantum_amplitudes: combined_amplitudes.mapv(|x| Complex64::new(x, 0.0)),
            classical_features: combined_features,
            entanglement_measure: combined_entanglement,
            coherence_time: combined_coherence,
            fidelity: combined_fidelity,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: contexts[0].context_metadata.clone(),
        })
    }

    fn compute_quantum_similarity(
        &self,
        query: &QuantumContextState,
        context: &QuantumContextState,
    ) -> Result<f64> {
        // Simplified quantum fidelity computation
        let amplitude_similarity = query
            .quantum_amplitudes
            .iter()
            .zip(context.quantum_amplitudes.iter())
            .map(|(a, b)| (a.conj() * b).norm())
            .sum::<f64>();

        let feature_similarity = 1.0
            - (&query.classical_features - &context.classical_features)
                .mapv(|x| x.abs())
                .sum()
                / query.classical_features.len() as f64;

        Ok((amplitude_similarity + feature_similarity) / 2.0)
    }
}

impl From<QuantumContextEncoding> for QuantumAttentionMechanism {
    fn from(encoding: QuantumContextEncoding) -> Self {
        match encoding {
            QuantumContextEncoding::AmplitudeEncoding => {
                QuantumAttentionMechanism::SingleHead { attention_dim: 64 }
            }
            QuantumContextEncoding::AngleEncoding { .. } => QuantumAttentionMechanism::MultiHead {
                num_heads: 4,
                head_dim: 16,
            },
            QuantumContextEncoding::EntanglementEncoding { .. } => {
                QuantumAttentionMechanism::EntanglementBased {
                    entanglement_strength: 0.5,
                }
            }
            QuantumContextEncoding::QuantumFourierEncoding { .. } => {
                QuantumAttentionMechanism::QuantumFourier { frequency_bins: 32 }
            }
            _ => QuantumAttentionMechanism::SingleHead { attention_dim: 64 },
        }
    }
}

impl QuantumContextCompressor {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            compression_ratio: config.context_compression_ratio,
            compression_method: CompressionMethod::QuantumPCA {
                num_components: (config.context_length as f64 * config.context_compression_ratio)
                    as usize,
            },
            compression_parameters: Array1::zeros(10),
            decompression_fidelity: 0.95,
        })
    }

    pub fn compress_context_sequence(
        &self,
        contexts: &[QuantumContextState],
    ) -> Result<Vec<QuantumContextState>> {
        if contexts.is_empty() {
            return Ok(Vec::new());
        }

        // Simple compression: select most important contexts
        let target_size = (contexts.len() as f64 * self.compression_ratio) as usize;
        let target_size = target_size.max(1);

        let mut indexed_contexts: Vec<(usize, &QuantumContextState)> =
            contexts.iter().enumerate().collect();

        // Sort by importance (simplified as entanglement measure)
        indexed_contexts.sort_by(|a, b| {
            b.1.entanglement_measure
                .partial_cmp(&a.1.entanglement_measure)
                .unwrap()
        });

        // Take top contexts
        Ok(indexed_contexts
            .into_iter()
            .take(target_size)
            .map(|(_, context)| context.clone())
            .collect())
    }
}

impl AdaptationController {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            current_strategy: config.adaptation_strategy.clone(),
            strategy_performance: HashMap::new(),
            adaptation_budget: AdaptationBudget {
                max_adaptation_steps: 10,
                max_quantum_resources: 100.0,
                max_time_budget: 10.0,
                current_usage: ResourceUsage {
                    steps_used: 0,
                    quantum_resources_used: 0.0,
                    time_used: 0.0,
                },
            },
            controller_state: ControllerState {
                current_performance: 0.0,
                performance_history: Vec::new(),
                adaptation_trajectory: Vec::new(),
                exploration_factor: 0.1,
            },
        })
    }
}

impl PrototypeBank {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            prototypes: Vec::new(),
            prototype_capacity: 100, // Default capacity
            update_strategy: PrototypeUpdateStrategy::OnlineUpdate {
                learning_rate: 0.01,
            },
            similarity_threshold: 0.8,
        })
    }

    pub fn find_nearest_prototypes(
        &self,
        query: &QuantumContextState,
        k: usize,
    ) -> Result<Vec<&QuantumPrototype>> {
        let mut similarities = Vec::new();

        for prototype in &self.prototypes {
            let similarity = self.compute_prototype_similarity(query, &prototype.quantum_state)?;
            similarities.push((similarity, prototype));
        }

        // Sort by similarity and return top k
        similarities.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
        Ok(similarities
            .into_iter()
            .take(k)
            .map(|(_, proto)| proto)
            .collect())
    }

    pub fn update_with_example(
        &mut self,
        context: &QuantumContextState,
        performance: f64,
    ) -> Result<()> {
        // Find nearest prototype or create new one
        if let Some(nearest_proto) = self.find_nearest_prototype(context)? {
            // Update existing prototype
            self.update_prototype(nearest_proto, context, performance)?;
        } else {
            // Create new prototype
            self.create_new_prototype(context, performance)?;
        }

        Ok(())
    }

    pub fn add_prototype(&mut self, state: QuantumContextState) -> Result<()> {
        let prototype = QuantumPrototype {
            prototype_id: self.prototypes.len(),
            quantum_state: state,
            associated_examples: Vec::new(),
            performance_statistics: PrototypeStatistics {
                average_performance: 0.8,
                performance_variance: 0.1,
                usage_frequency: 0.0,
                last_updated: 0,
                success_rate: 0.8,
            },
            update_count: 0,
        };

        self.prototypes.push(prototype);
        Ok(())
    }

    pub fn get_prototype_count(&self) -> usize {
        self.prototypes.len()
    }

    fn compute_prototype_similarity(
        &self,
        query: &QuantumContextState,
        prototype: &QuantumContextState,
    ) -> Result<f64> {
        // Simplified similarity computation
        let feature_sim = 1.0
            - (&query.classical_features - &prototype.classical_features)
                .mapv(|x| x.abs())
                .sum()
                / query.classical_features.len() as f64;

        let entanglement_sim =
            1.0 - (query.entanglement_measure - prototype.entanglement_measure).abs();

        Ok((feature_sim + entanglement_sim) / 2.0)
    }

    fn find_nearest_prototype(&self, context: &QuantumContextState) -> Result<Option<usize>> {
        if self.prototypes.is_empty() {
            return Ok(None);
        }

        let mut best_similarity = 0.0;
        let mut best_idx = 0;

        for (idx, prototype) in self.prototypes.iter().enumerate() {
            let similarity =
                self.compute_prototype_similarity(context, &prototype.quantum_state)?;
            if similarity > best_similarity {
                best_similarity = similarity;
                best_idx = idx;
            }
        }

        if best_similarity > self.similarity_threshold {
            Ok(Some(best_idx))
        } else {
            Ok(None)
        }
    }

    fn update_prototype(
        &mut self,
        prototype_idx: usize,
        context: &QuantumContextState,
        performance: f64,
    ) -> Result<()> {
        if prototype_idx < self.prototypes.len() {
            let prototype = &mut self.prototypes[prototype_idx];

            // Update statistics
            let old_avg = prototype.performance_statistics.average_performance;
            let count = prototype.update_count as f64 + 1.0;
            prototype.performance_statistics.average_performance =
                (old_avg * (count - 1.0) + performance) / count;

            prototype.update_count += 1;

            // Update quantum state (simplified linear interpolation)
            let learning_rate = 0.1;
            prototype.quantum_state.classical_features =
                &prototype.quantum_state.classical_features * (1.0 - learning_rate)
                    + &context.classical_features * learning_rate;
        }

        Ok(())
    }

    fn create_new_prototype(
        &mut self,
        context: &QuantumContextState,
        performance: f64,
    ) -> Result<()> {
        if self.prototypes.len() >= self.prototype_capacity {
            // Remove least performing prototype
            if let Some(min_idx) = self
                .prototypes
                .iter()
                .enumerate()
                .min_by(|(_, a), (_, b)| {
                    a.performance_statistics
                        .average_performance
                        .partial_cmp(&b.performance_statistics.average_performance)
                        .unwrap()
                })
                .map(|(idx, _)| idx)
            {
                self.prototypes.remove(min_idx);
            }
        }

        let prototype = QuantumPrototype {
            prototype_id: self.prototypes.len(),
            quantum_state: context.clone(),
            associated_examples: Vec::new(),
            performance_statistics: PrototypeStatistics {
                average_performance: performance,
                performance_variance: 0.0,
                usage_frequency: 1.0,
                last_updated: 0,
                success_rate: performance,
            },
            update_count: 1,
        };

        self.prototypes.push(prototype);
        Ok(())
    }
}

impl EntanglementTracker {
    pub fn new(config: &QuantumInContextLearningConfig) -> Result<Self> {
        Ok(Self {
            entanglement_history: Vec::new(),
            current_entanglement: 0.0,
            entanglement_budget: 1.0,
            optimization_strategy: EntanglementOptimization::OptimalEntanglement {
                target_value: config.entanglement_strength,
            },
        })
    }

    pub fn compute_efficiency(&self) -> f64 {
        if self.entanglement_history.is_empty() {
            return 1.0;
        }

        let avg_entanglement = self
            .entanglement_history
            .iter()
            .map(|m| m.entanglement_value)
            .sum::<f64>()
            / self.entanglement_history.len() as f64;

        avg_entanglement / self.entanglement_budget
    }
}

// Output and result structures
#[derive(Debug, Clone)]
pub struct InContextLearningOutput {
    pub prediction: Array1<f64>,
    pub adaptation_result: AdaptationResult,
    pub attended_context: QuantumContextState,
    pub learning_metrics: InContextLearningMetrics,
}

#[derive(Debug, Clone)]
pub struct AdaptationResult {
    pub adapted_state: QuantumContextState,
    pub adaptation_steps: usize,
    pub performance: f64,
    pub quantum_resources: QuantumResourceUsage,
    pub adaptation_trajectory: Vec<AdaptationStep>,
}

#[derive(Debug, Clone, Default)]
pub struct TransferLearningResults {
    pub source_task_performances: Vec<f64>,
    pub final_target_performance: f64,
    pub transfer_ratio: f64,
}

#[derive(Debug, Clone)]
pub struct InContextLearningStatistics {
    pub total_episodes: usize,
    pub average_performance: f64,
    pub average_adaptation_speed: f64,
    pub quantum_advantage: f64,
    pub memory_utilization: f64,
    pub prototype_count: usize,
    pub entanglement_efficiency: f64,
}

// Default implementations
impl Default for QuantumResourceUsage {
    fn default() -> Self {
        Self {
            circuit_depth: 0,
            gate_count: HashMap::new(),
            entanglement_operations: 0,
            measurement_operations: 0,
            coherence_time_used: 0.0,
            quantum_volume_required: 0.0,
        }
    }
}

impl Default for AttentionCache {
    fn default() -> Self {
        Self {
            cached_queries: HashMap::new(),
            cached_keys: HashMap::new(),
            cached_values: HashMap::new(),
            cache_hit_rate: 0.0,
        }
    }
}

impl Default for AdaptationPerformanceTracker {
    fn default() -> Self {
        Self {
            task_performances: HashMap::new(),
            adaptation_times: HashMap::new(),
            resource_usage: HashMap::new(),
            quantum_advantages: HashMap::new(),
            transfer_performance: 0.0,
        }
    }
}

impl Default for QuantumInContextLearningConfig {
    fn default() -> Self {
        Self {
            model_dim: 64,
            context_length: 100,
            max_context_examples: 50,
            num_qubits: 8,
            num_attention_heads: 4,
            context_compression_ratio: 0.8,
            quantum_context_encoding: QuantumContextEncoding::AmplitudeEncoding,
            adaptation_strategy: AdaptationStrategy::DirectConditioning,
            entanglement_strength: 0.5,
            coherence_preservation: 0.9,
            use_quantum_memory: true,
            enable_meta_learning: true,
            context_retrieval_method: ContextRetrievalMethod::QuantumNearestNeighbor {
                distance_metric: QuantumDistanceMetric::QuantumFidelity,
                k_neighbors: 5,
            },
        }
    }
}

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

    #[test]
    fn test_quantum_in_context_learner_creation() {
        let config = QuantumInContextLearningConfig::default();
        let learner = QuantumInContextLearner::new(config);
        assert!(learner.is_ok());
    }

    #[test]
    fn test_context_encoding() {
        let config = QuantumInContextLearningConfig::default();
        let encoder = QuantumContextEncoder::new(&config).unwrap();

        let example = ContextExample {
            input: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            output: Array1::from_vec(vec![0.8]),
            metadata: ContextMetadata {
                task_type: "classification".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
            quantum_encoding: QuantumContextState {
                quantum_amplitudes: Array1::zeros(16).mapv(|_: f64| Complex64::new(1.0, 0.0)),
                classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
                entanglement_measure: 0.5,
                coherence_time: 1.0,
                fidelity: 1.0,
                phase_information: Complex64::new(1.0, 0.0),
                context_metadata: ContextMetadata {
                    task_type: "classification".to_string(),
                    difficulty_level: 0.5,
                    modality: ContextModality::Tabular,
                    timestamp: 0,
                    importance_weight: 1.0,
                },
            },
        };

        let encoded = encoder.encode_example(&example);
        assert!(encoded.is_ok());
    }

    #[test]
    fn test_zero_shot_learning() {
        let config = QuantumInContextLearningConfig::default();
        let learner = QuantumInContextLearner::new(config.clone()).unwrap();

        let query = Array1::from_vec(vec![0.5, -0.3, 0.8]);
        let result = learner.zero_shot_learning(&query);

        assert!(result.is_ok());
        let prediction = result.unwrap();
        assert_eq!(prediction.len(), config.model_dim);
    }

    #[test]
    fn test_few_shot_learning() {
        let config = QuantumInContextLearningConfig {
            model_dim: 3,
            max_context_examples: 5,
            ..Default::default()
        };
        let mut learner = QuantumInContextLearner::new(config).unwrap();

        let examples = vec![ContextExample {
            input: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            output: Array1::from_vec(vec![0.8]),
            metadata: ContextMetadata {
                task_type: "test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
            quantum_encoding: QuantumContextState {
                quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
                classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
                entanglement_measure: 0.5,
                coherence_time: 1.0,
                fidelity: 1.0,
                phase_information: Complex64::new(1.0, 0.0),
                context_metadata: ContextMetadata {
                    task_type: "test".to_string(),
                    difficulty_level: 0.5,
                    modality: ContextModality::Tabular,
                    timestamp: 0,
                    importance_weight: 1.0,
                },
            },
        }];

        let query = Array1::from_vec(vec![0.5, -0.3, 0.8]);
        let result = learner.few_shot_learning(&examples, &query, 3);

        assert!(result.is_ok());
    }

    #[test]
    fn test_quantum_memory_operations() {
        let config = QuantumInContextLearningConfig::default();
        let mut memory = QuantumEpisodicMemory::new(&config).unwrap();

        let test_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.7,
            coherence_time: 0.9,
            fidelity: 0.95,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "memory_test".to_string(),
                difficulty_level: 0.6,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };

        // Test adding experience
        let result = memory.add_experience(test_state.clone());
        assert!(result.is_ok());

        // Test retrieval
        let retrieved = memory.retrieve_similar_contexts(&test_state, 1);
        assert!(retrieved.is_ok());
        assert_eq!(retrieved.unwrap().len(), 1);
    }

    #[test]
    fn test_adaptation_strategies() {
        let config = QuantumInContextLearningConfig {
            adaptation_strategy: AdaptationStrategy::QuantumInterference {
                interference_strength: 0.8,
            },
            ..Default::default()
        };

        let learner = QuantumInContextLearner::new(config);
        assert!(learner.is_ok());
    }

    #[test]
    fn test_prototype_bank_operations() {
        let config = QuantumInContextLearningConfig::default();
        let mut bank = PrototypeBank::new(&config).unwrap();

        let test_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "prototype_test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };

        // Test adding prototype
        let result = bank.add_prototype(test_state.clone());
        assert!(result.is_ok());
        assert_eq!(bank.get_prototype_count(), 1);

        // Test finding prototypes
        let found = bank.find_nearest_prototypes(&test_state, 1);
        assert!(found.is_ok());
        assert_eq!(found.unwrap().len(), 1);
    }

    #[test]
    fn test_quantum_attention_mechanism() {
        let config = QuantumInContextLearningConfig {
            num_attention_heads: 2,
            ..Default::default()
        };
        let attention = QuantumContextAttention::new(&config).unwrap();

        let query_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "attention_test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };

        let contexts = vec![query_state.clone(), query_state.clone()];

        let weights = attention.compute_attention_weights(&query_state, &contexts);
        assert!(weights.is_ok());
        assert_eq!(weights.unwrap().len(), 2);
    }
}