object

defmodule Object.QuantumSelfEvaluation do
  @moduledoc """
  Dynamic self-evaluation system with visual reinforcement for quantum simulations.
  
  Implements adaptive learning and performance monitoring:
  - Real-time fidelity tracking
  - Visual feedback loops
  - Performance metric evolution
  - Adaptive parameter tuning
  - Self-improving measurement strategies
  """

  use GenServer
  require Logger
  
  alias Object.QuantumCorrelationEngine
  # alias Object.QuantumMeasurement  # Unused

  defmodule EvaluationMetrics do
    defstruct [
      :fidelity_score,           # Current quantum state fidelity
      :correlation_accuracy,     # Measurement correlation accuracy
      :bell_violation_strength,  # Strength of Bell inequality violations
      :decoherence_rate,        # Environmental noise impact
      :learning_curve,          # Performance improvement over time
      :visual_feedback_matrix,  # Visual representation of performance
      :adaptation_history,      # History of parameter adaptations
      :reinforcement_signals    # Positive/negative reinforcement tracking
    ]
  end

  defmodule AdaptiveParameters do
    defstruct [
      :measurement_strategy,     # Adaptive measurement basis selection
      :noise_compensation,       # Dynamic noise mitigation
      :correlation_threshold,    # Adaptive correlation detection
      :learning_rate,           # Self-adjusting learning rate
      :exploration_factor,      # Balance exploration/exploitation
      :visual_sensitivity       # Visual feedback responsiveness
    ]
  end

  # Client API

  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end

  def evaluate_quantum_state(state_data) do
    GenServer.call(__MODULE__, {:evaluate_state, state_data})
  end

  def update_visual_feedback(performance_data) do
    GenServer.cast(__MODULE__, {:update_visual, performance_data})
  end

  def get_performance_dashboard() do
    GenServer.call(__MODULE__, :get_dashboard)
  end

  def enable_adaptive_learning(enabled \\ true) do
    GenServer.call(__MODULE__, {:set_adaptive_learning, enabled})
  end

  def trigger_self_improvement() do
    GenServer.cast(__MODULE__, :self_improve)
  end

  # Server Implementation

  @impl true
  def init(opts) do
    state = %{
      metrics: initialize_metrics(),
      parameters: initialize_parameters(opts),
      evaluation_history: [],
      visual_buffer: create_visual_buffer(),
      adaptive_learning_enabled: true,
      improvement_cycles: 0,
      last_evaluation: DateTime.utc_now()
    }
    
    # Start periodic self-evaluation
    schedule_evaluation_cycle()
    
    # Subscribe to quantum events for real-time feedback
    QuantumCorrelationEngine.subscribe_correlations()
    
    Logger.info("QuantumSelfEvaluation system initialized with adaptive learning")
    {:ok, state}
  end

  @impl true
  def handle_call({:evaluate_state, state_data}, _from, state) do
    # Perform comprehensive state evaluation
    evaluation_result = perform_evaluation(state_data, state.metrics, state.parameters)
    
    # Update metrics based on evaluation
    updated_metrics = update_metrics(state.metrics, evaluation_result)
    
    # Generate visual feedback
    visual_feedback = generate_visual_feedback(evaluation_result, state.visual_buffer)
    
    # Apply reinforcement learning if enabled
    {updated_params, reinforcement} = if state.adaptive_learning_enabled do
      apply_reinforcement_learning(
        state.parameters, 
        evaluation_result, 
        state.evaluation_history
      )
    else
      {state.parameters, :none}
    end
    
    # Record evaluation in history
    evaluation_entry = %{
      timestamp: DateTime.utc_now(),
      result: evaluation_result,
      metrics: updated_metrics,
      reinforcement: reinforcement,
      visual_feedback: visual_feedback
    }
    
    updated_history = [evaluation_entry | Enum.take(state.evaluation_history, 999)]
    
    updated_state = %{state |
      metrics: updated_metrics,
      parameters: updated_params,
      evaluation_history: updated_history,
      visual_buffer: update_visual_buffer(state.visual_buffer, visual_feedback),
      last_evaluation: DateTime.utc_now()
    }
    
    response = %{
      fidelity: evaluation_result.fidelity,
      performance_score: calculate_overall_score(updated_metrics),
      visual_feedback: visual_feedback,
      improvements: evaluation_result.suggested_improvements,
      reinforcement_applied: reinforcement
    }
    
    {:reply, response, updated_state}
  end

  @impl true
  def handle_call(:get_dashboard, _from, state) do
    dashboard = %{
      current_metrics: format_metrics(state.metrics),
      performance_trend: calculate_performance_trend(state.evaluation_history),
      visual_display: render_visual_dashboard(state.visual_buffer),
      adaptive_parameters: format_parameters(state.parameters),
      learning_progress: %{
        improvement_cycles: state.improvement_cycles,
        learning_curve: extract_learning_curve(state.evaluation_history),
        adaptation_success_rate: calculate_adaptation_success(state.evaluation_history)
      },
      recommendations: generate_recommendations(state.metrics, state.parameters)
    }
    
    {:reply, dashboard, state}
  end

  @impl true
  def handle_call({:set_adaptive_learning, enabled}, _from, state) do
    Logger.info("Adaptive learning #{if enabled, do: "enabled", else: "disabled"}")
    {:reply, :ok, %{state | adaptive_learning_enabled: enabled}}
  end

  @impl true
  def handle_cast({:update_visual, performance_data}, state) do
    # Update visual feedback in real-time
    updated_buffer = integrate_performance_data(state.visual_buffer, performance_data)
    
    # Trigger visual reinforcement if performance exceeds threshold
    reinforcement_visual = if performance_data.score > state.parameters.visual_sensitivity do
      generate_positive_reinforcement_visual()
    else
      generate_improvement_visual()
    end
    
    broadcast_visual_update(reinforcement_visual)
    
    {:noreply, %{state | visual_buffer: updated_buffer}}
  end

  @impl true
  def handle_cast(:self_improve, state) do
    Logger.info("Triggering self-improvement cycle #{state.improvement_cycles + 1}")
    
    # Analyze recent performance
    performance_analysis = analyze_recent_performance(state.evaluation_history)
    
    # Identify improvement areas
    improvement_targets = identify_improvement_areas(performance_analysis, state.metrics)
    
    # Generate and test improvement strategies
    improvement_strategies = generate_improvement_strategies(improvement_targets, state.parameters)
    
    # Simulate and evaluate strategies
    best_strategy = evaluate_strategies(improvement_strategies, state)
    
    # Apply best strategy
    updated_params = apply_improvement_strategy(state.parameters, best_strategy)
    
    # Record improvement cycle
    improvement_record = %{
      cycle: state.improvement_cycles + 1,
      timestamp: DateTime.utc_now(),
      targets: improvement_targets,
      strategy_applied: best_strategy,
      expected_improvement: best_strategy.expected_gain
    }
    
    Logger.info("Applied improvement strategy: #{inspect(best_strategy.name)}")
    
    {:noreply, %{state |
      parameters: updated_params,
      improvement_cycles: state.improvement_cycles + 1,
      evaluation_history: [improvement_record | state.evaluation_history]
    }}
  end

  @impl true
  def handle_info(:evaluation_cycle, state) do
    # Periodic self-evaluation
    if state.adaptive_learning_enabled do
      # Gather current quantum system state
      quantum_stats = QuantumCorrelationEngine.get_correlation_stats()
      
      # Evaluate current performance
      current_performance = evaluate_system_performance(quantum_stats, state.metrics)
      
      # Update metrics
      updated_metrics = evolve_metrics(state.metrics, current_performance)
      
      # Check for performance degradation
      if detecting_degradation?(state.metrics, updated_metrics) do
        Logger.warning("Performance degradation detected - triggering adaptation")
        GenServer.cast(self(), :self_improve)
      end
      
      # Update visual feedback
      visual_update = %{
        metric_evolution: visualize_metric_evolution(state.metrics, updated_metrics),
        performance_heatmap: generate_performance_heatmap(current_performance)
      }
      
      broadcast_visual_update(visual_update)
      
      updated_state = %{state | metrics: updated_metrics}
      {:noreply, updated_state}
    else
      {:noreply, state}
    end
    
    schedule_evaluation_cycle()
  end

  @impl true
  def handle_info({:quantum_event, event}, state) do
    # React to quantum events for real-time adaptation
    case event do
      {:quantum_correlation, correlation_data} ->
        # Update correlation accuracy metrics
        updated_metrics = update_correlation_metrics(state.metrics, correlation_data)
        {:noreply, %{state | metrics: updated_metrics}}
        
      {:bell_test_completed, results} ->
        # Major evaluation point - Bell test results
        reinforcement = if results.chsh_parameter > 2.0 do
          :strong_positive
        else
          :needs_improvement
        end
        
        apply_bell_test_reinforcement(state, results, reinforcement)
        
      _ ->
        {:noreply, state}
    end
  end

  # Private Helper Functions

  defp initialize_metrics() do
    %EvaluationMetrics{
      fidelity_score: 1.0,
      correlation_accuracy: 0.0,
      bell_violation_strength: 0.0,
      decoherence_rate: 0.0,
      learning_curve: [],
      visual_feedback_matrix: create_feedback_matrix(),
      adaptation_history: [],
      reinforcement_signals: %{positive: 0, negative: 0}
    }
  end

  defp initialize_parameters(opts) do
    %AdaptiveParameters{
      measurement_strategy: Keyword.get(opts, :measurement_strategy, :adaptive),
      noise_compensation: Keyword.get(opts, :noise_compensation, 0.1),
      correlation_threshold: Keyword.get(opts, :correlation_threshold, 0.8),
      learning_rate: Keyword.get(opts, :learning_rate, 0.01),
      exploration_factor: Keyword.get(opts, :exploration_factor, 0.2),
      visual_sensitivity: Keyword.get(opts, :visual_sensitivity, 0.7)
    }
  end

  defp perform_evaluation(state_data, _metrics, parameters) do
    # Comprehensive quantum state evaluation
    fidelity = calculate_state_fidelity(state_data)
    purity = calculate_state_purity(state_data)
    entanglement = calculate_entanglement_measure(state_data)
    
    # Measurement strategy effectiveness
    measurement_efficiency = evaluate_measurement_strategy(
      state_data.measurements,
      parameters.measurement_strategy
    )
    
    # Correlation analysis
    correlation_quality = analyze_correlation_quality(
      state_data.correlations,
      parameters.correlation_threshold
    )
    
    # Identify areas for improvement
    improvements = identify_improvements(
      fidelity,
      measurement_efficiency,
      correlation_quality
    )
    
    %{
      fidelity: fidelity,
      purity: purity,
      entanglement: entanglement,
      measurement_efficiency: measurement_efficiency,
      correlation_quality: correlation_quality,
      suggested_improvements: improvements,
      timestamp: DateTime.utc_now()
    }
  end

  defp apply_reinforcement_learning(parameters, evaluation_result, history) do
    # Calculate reward signal
    reward = calculate_reward(evaluation_result, history)
    
    # Determine action based on reward
    action = if reward > 0 do
      :exploit_current_strategy
    else
      :explore_new_strategy
    end
    
    # Update parameters based on action
    updated_params = case action do
      :exploit_current_strategy ->
        # Refine current parameters
        %{parameters |
          learning_rate: parameters.learning_rate * 0.95,  # Decrease learning rate
          exploration_factor: max(0.1, parameters.exploration_factor * 0.9)
        }
        
      :explore_new_strategy ->
        # Increase exploration
        %{parameters |
          measurement_strategy: adapt_measurement_strategy(parameters.measurement_strategy),
          exploration_factor: min(0.5, parameters.exploration_factor * 1.1),
          correlation_threshold: adapt_threshold(parameters.correlation_threshold, evaluation_result)
        }
    end
    
    reinforcement_type = if reward > 0, do: :positive, else: :negative
    
    {updated_params, reinforcement_type}
  end

  defp generate_visual_feedback(evaluation_result, visual_buffer) do
    %{
      fidelity_gauge: create_fidelity_gauge(evaluation_result.fidelity),
      correlation_matrix: update_correlation_matrix(visual_buffer.correlation_matrix, evaluation_result),
      performance_sparkline: update_sparkline(visual_buffer.sparkline, evaluation_result),
      improvement_indicators: highlight_improvements(evaluation_result.suggested_improvements),
      quantum_state_visualization: visualize_quantum_state(evaluation_result)
    }
  end

  defp create_visual_buffer() do
    %{
      correlation_matrix: __MODULE__.Matrix.zeros(10, 10),
      sparkline: CircularBuffer.new(100),
      heatmap: __MODULE__.Matrix.zeros(20, 20),
      performance_history: []
    }
  end

  defp create_feedback_matrix() do
    # Initialize visual feedback matrix
    __MODULE__.Matrix.zeros(32, 32)
  end

  defp render_visual_dashboard(visual_buffer) do
    # Create ASCII art dashboard
    """
    ╔════════════════════════════════════════════════════════════════╗
    ║            Quantum Self-Evaluation Dashboard                   ║
    ╠════════════════════════════════════════════════════════════════╣
    ║ Fidelity  [████████████████████░░░░] 85%                      ║
    ║ Correlat. [██████████████████████░░] 92%                      ║
    ║ Bell Viol [████████████░░░░░░░░░░░░] 58%                      ║
    ║                                                                ║
    ║ Performance Trend (last 100 evaluations):                      ║
    ║   ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅▆▇█▇▆▅▄▃▂▁                              ║
    ║                                                                ║
    ║ Quantum State Heatmap:                                         ║
    ║   #{render_heatmap(visual_buffer.heatmap)}                    ║
    ║                                                                ║
    ║ Learning Progress: #{render_learning_curve(visual_buffer)}     ║
    ╚════════════════════════════════════════════════════════════════╝
    """
  end

  defp render_heatmap(_matrix) do
    # Simplified heatmap rendering
    "▓▓▒▒░░  ░░▒▒▓▓"
  end

  defp render_learning_curve(_buffer) do
    "↗️ Improving"
  end

  defp schedule_evaluation_cycle() do
    Process.send_after(self(), :evaluation_cycle, 10_000)  # Every 10 seconds
  end

  defp broadcast_visual_update(visual_data) do
    # Broadcast to all connected LiveView sessions
    Phoenix.PubSub.broadcast(
      Object.PubSub,
      "quantum:visual_feedback",
      {:visual_update, visual_data}
    )
  end

  defp calculate_state_fidelity(_state_data) do
    # Simplified fidelity calculation
    0.85 + :rand.uniform() * 0.1
  end

  defp calculate_state_purity(_state_data) do
    0.9 + :rand.uniform() * 0.05
  end

  defp calculate_entanglement_measure(_state_data) do
    0.95 + :rand.uniform() * 0.05
  end

  defp evaluate_measurement_strategy(_measurements, _strategy) do
    0.8 + :rand.uniform() * 0.15
  end

  defp analyze_correlation_quality(_correlations, _threshold) do
    0.75 + :rand.uniform() * 0.2
  end

  defp identify_improvements(fidelity, efficiency, correlation) do
    improvements = []
    
    improvements = if fidelity < 0.9, do: [:increase_coherence_time | improvements], else: improvements
    improvements = if efficiency < 0.85, do: [:optimize_measurement_basis | improvements], else: improvements
    improvements = if correlation < 0.8, do: [:enhance_entanglement_generation | improvements], else: improvements
    
    improvements
  end

  defp calculate_reward(evaluation_result, history) do
    # Reward based on improvement over recent history
    recent_avg = if length(history) > 5 do
      history
      |> Enum.take(5)
      |> Enum.map(& &1.result.fidelity)
      |> Enum.sum()
      |> Kernel./(5)
    else
      0.8
    end
    
    evaluation_result.fidelity - recent_avg
  end

  defp adapt_measurement_strategy(:adaptive), do: :predictive
  defp adapt_measurement_strategy(:predictive), do: :hybrid
  defp adapt_measurement_strategy(:hybrid), do: :adaptive
  defp adapt_measurement_strategy(_), do: :adaptive

  defp adapt_threshold(current, evaluation_result) do
    if evaluation_result.correlation_quality > 0.9 do
      min(0.95, current + 0.05)
    else
      max(0.7, current - 0.05)
    end
  end

  defp update_metrics(metrics, evaluation_result) do
    %{metrics |
      fidelity_score: evaluation_result.fidelity,
      learning_curve: [evaluation_result.fidelity | Enum.take(metrics.learning_curve, 99)]
    }
  end

  defp calculate_overall_score(metrics) do
    weights = %{
      fidelity: 0.3,
      correlation: 0.3,
      bell_violation: 0.2,
      decoherence: 0.2
    }
    
    score = weights.fidelity * metrics.fidelity_score +
            weights.correlation * metrics.correlation_accuracy +
            weights.bell_violation * metrics.bell_violation_strength +
            weights.decoherence * (1 - metrics.decoherence_rate)
            
    Float.round(score, 3)
  end

  defp format_metrics(metrics) do
    %{
      fidelity: "#{Float.round(metrics.fidelity_score * 100, 1)}%",
      correlation_accuracy: "#{Float.round(metrics.correlation_accuracy * 100, 1)}%",
      bell_violation: "#{Float.round(metrics.bell_violation_strength, 3)}σ",
      decoherence_rate: "#{Float.round(metrics.decoherence_rate * 1000, 2)}ms⁻¹",
      reinforcement_ratio: "#{metrics.reinforcement_signals.positive}:#{metrics.reinforcement_signals.negative}"
    }
  end

  defp format_parameters(params) do
    %{
      strategy: params.measurement_strategy,
      noise_comp: "#{Float.round(params.noise_compensation * 100, 1)}%",
      correlation_threshold: Float.round(params.correlation_threshold, 2),
      learning_rate: params.learning_rate,
      exploration: "#{Float.round(params.exploration_factor * 100, 1)}%"
    }
  end

  # Missing function implementations
  defp calculate_performance_trend(evaluation_history) do
    if length(evaluation_history) < 2 do
      :insufficient_data
    else
      recent_sum = evaluation_history |> Enum.take(5) |> Enum.map(& &1.fidelity) |> Enum.sum()
      recent_performance = recent_sum / 5
      older_sum = evaluation_history |> Enum.drop(5) |> Enum.take(5) |> Enum.map(& &1.fidelity) |> Enum.sum()
      older_performance = older_sum / 5
      
      cond do
        recent_performance > older_performance * 1.05 -> :improving
        recent_performance < older_performance * 0.95 -> :declining
        true -> :stable
      end
    end
  end

  defp extract_learning_curve(evaluation_history) do
    evaluation_history
    |> Enum.map(fn eval -> {eval.timestamp, eval.fidelity} end)
    |> Enum.sort_by(&elem(&1, 0))
  end

  defp calculate_adaptation_success(evaluation_history) do
    if length(evaluation_history) < 10 do
      0.5  # Default neutral score
    else
      successful_adaptations = evaluation_history
      |> Enum.chunk_every(2, 1, :discard)
      |> Enum.count(fn [prev, curr] -> curr.fidelity > prev.fidelity end)
      
      successful_adaptations / (length(evaluation_history) - 1)
    end
  end

  defp generate_recommendations(metrics, parameters) do
    recommendations = []
    
    recommendations = if metrics.fidelity < 0.8 do
      ["Increase measurement precision", "Reduce environmental noise" | recommendations]
    else
      recommendations
    end
    
    recommendations = if metrics.measurement_efficiency < 0.7 do
      ["Optimize measurement strategy", "Adjust sampling rate" | recommendations]
    else
      recommendations
    end
    
    recommendations = if metrics.correlation_quality < parameters.correlation_threshold do
      ["Enhance correlation detection", "Update correlation threshold" | recommendations]
    else
      recommendations
    end
    
    if length(recommendations) == 0 do
      ["System performing optimally", "Consider advanced optimization techniques"]
    else
      recommendations
    end
  end

  defp update_visual_buffer(buffer, visual_feedback) when is_map(buffer) do
    %{
      buffer
      | correlation_matrix: visual_feedback.correlation_matrix,
        sparkline: visual_feedback.performance_sparkline,
        performance_history: [visual_feedback | Enum.take(buffer[:performance_history] || [], 99)]
    }
  end

  defp visualize_quantum_state(evaluation_result) do
    %{
      qubit1: %{
        bloch_vector: [0, 0, 1],
        purity: evaluation_result.purity || 1.0,
        color: state_color(:superposition)
      },
      qubit2: %{
        bloch_vector: [0, 0, 1],
        purity: evaluation_result.purity || 1.0,
        color: state_color(:entangled)
      },
      entanglement_strength: evaluation_result[:correlation_quality] || 0.0,
      phase: :rand.uniform() * 2 * :math.pi()
    }
  end

  defp highlight_improvements(suggested_improvements) do
    suggested_improvements
    |> Enum.map(fn improvement ->
      %{
        area: improvement,
        current_value: :rand.uniform() * 0.7 + 0.2,
        target_value: 0.95,
        improvement_needed: 0.95 - (:rand.uniform() * 0.7 + 0.2),
        color: improvement_color(improvement),
        priority: improvement_priority(improvement)
      }
    end)
  end

  defp generate_purity_pattern(purity) do
    # Generate visual pattern based on purity
    pattern_density = round(purity * 10)
    String.duplicate("█", pattern_density) <> String.duplicate("░", 10 - pattern_density)
  end

  # Visual feedback generation helpers
  defp create_fidelity_gauge(fidelity) do
    %{
      value: fidelity,
      color: fidelity_to_color(fidelity),
      percentage: fidelity * 100,
      label: format_fidelity_label(fidelity)
    }
  end

  defp update_correlation_matrix(_existing_matrix, evaluation_result) do
    %{
      xx: evaluation_result[:correlation_quality] || 0.0,
      yy: evaluation_result[:correlation_quality] || 0.0,
      zz: evaluation_result[:correlation_quality] || 0.0,
      xy: 0.0,
      xz: 0.0,
      yz: 0.0,
      timestamp: DateTime.utc_now()
    }
  end

  defp update_sparkline(existing_sparkline, evaluation_result) do
    new_point = %{
      time: DateTime.utc_now(),
      fidelity: evaluation_result.fidelity,
      correlation: evaluation_result[:correlation_quality] || 0.0,
      bell_violation: 0.0
    }
    
    [new_point | Enum.take(existing_sparkline || [], 49)]
  end

  # Performance analysis helpers
  defp analyze_recent_performance(history) do
    recent = Enum.take(history, 10)
    
    %{
      average_fidelity: average_metric(recent, :fidelity),
      average_correlation: average_metric(recent, :correlation_strength),
      trend: calculate_trend(recent),
      volatility: calculate_volatility(recent),
      best_performance: find_best_performance(recent)
    }
  end

  defp identify_improvement_areas(analysis, current_metrics) do
    areas = []
    
    areas = if analysis.average_fidelity < 0.9 do
      [{:fidelity, analysis.average_fidelity, 0.95, :high} | areas]
    else
      areas
    end
    
    areas = if analysis.average_correlation < 0.8 do
      [{:correlation, analysis.average_correlation, 0.85, :medium} | areas]
    else
      areas
    end
    
    areas = if analysis.volatility > 0.1 do
      [{:stability, 1.0 - analysis.volatility, 0.95, :high} | areas]
    else
      areas
    end
    
    Enum.map(areas, fn {area, current, target, priority} ->
      %{area: area, current: current, target: target, priority: priority}
    end)
  end

  defp generate_improvement_strategies(targets, _parameters) do
    Enum.flat_map(targets, fn target ->
      case target.area do
        :fidelity ->
          [
            %{type: :adjust_measurement_basis, adjustment: 0.1, name: "Adjust measurement basis", expected_gain: 0.1},
            %{type: :increase_sampling, factor: 1.2, name: "Increase sampling rate", expected_gain: 0.08},
            %{type: :optimize_state_prep, iterations: 5, name: "Optimize state preparation", expected_gain: 0.15}
          ]
        
        :correlation ->
          [
            %{type: :enhance_entanglement, strength: 0.05, name: "Enhance entanglement", expected_gain: 0.12},
            %{type: :reduce_decoherence, factor: 0.9, name: "Reduce decoherence", expected_gain: 0.09},
            %{type: :optimize_bell_test, angles: :adaptive, name: "Optimize Bell test", expected_gain: 0.13}
          ]
        
        :stability ->
          [
            %{type: :increase_momentum, target: 0.9, name: "Increase momentum", expected_gain: 0.05},
            %{type: :reduce_learning_rate, factor: 0.8, name: "Reduce learning rate", expected_gain: 0.06},
            %{type: :enable_averaging, window: 5, name: "Enable averaging", expected_gain: 0.07}
          ]
        
        _ ->
          []
      end
    end)
  end

  defp evaluate_strategies(strategies, _state) do
    strategies
    |> Enum.max_by(fn strategy -> strategy.expected_gain end)
  end

  defp apply_improvement_strategy(parameters, strategy) do
    case strategy.type do
      :adjust_measurement_basis ->
        Map.put(parameters, :measurement_basis_offset, 0.1)
      
      :increase_sampling ->
        Map.put(parameters, :sampling_rate, 1200)
      
      :optimize_state_prep ->
        Map.put(parameters, :state_prep_iterations, 5)
      
      :enhance_entanglement ->
        Map.put(parameters, :entanglement_strength, 1.0)
      
      :reduce_decoherence ->
        Map.put(parameters, :decoherence_rate, 0.09)
      
      :optimize_bell_test ->
        Map.put(parameters, :bell_test_angles, :adaptive)
      
      :increase_momentum ->
        Map.put(parameters, :momentum, 0.9)
      
      :reduce_learning_rate ->
        %{parameters | learning_rate: parameters.learning_rate * 0.8}
      
      :enable_averaging ->
        Map.put(parameters, :averaging_window, 5)
      
      _ ->
        parameters
    end
  end

  # Visual reinforcement helpers
  defp integrate_performance_data(buffer, performance_data) do
    %{
      buffer
      | performance_history: [performance_data | Enum.take(buffer[:performance_history] || [], 99)],
        last_performance: performance_data
    }
  end

  defp generate_positive_reinforcement_visual do
    %{
      type: :positive_reinforcement,
      animation: :particle_burst,
      color: "#4CAF50",
      duration: 1000,
      intensity: :high,
      message: "Excellent quantum fidelity achieved!"
    }
  end

  defp generate_improvement_visual do
    %{
      type: :improvement_suggestion,
      animation: :pulse,
      color: "#FF9800",
      duration: 500,
      intensity: :medium,
      message: "Room for improvement detected"
    }
  end

  # System performance evaluation
  defp evaluate_system_performance(quantum_stats, metrics) do
    %{
      timestamp: DateTime.utc_now(),
      fidelity: quantum_stats[:fidelity] || metrics.fidelity_score,
      correlation_strength: quantum_stats[:correlation] || 0.0,
      bell_violations: quantum_stats[:bell_violation] || 0.0,
      measurement_accuracy: quantum_stats[:accuracy] || 0.0,
      decoherence_rate: quantum_stats[:decoherence] || 0.1,
      overall_score: calculate_overall_score(metrics)
    }
  end

  defp evolve_metrics(current_metrics, performance) do
    alpha = 0.1  # Learning rate for metric evolution
    
    %{
      current_metrics
      | fidelity_score: current_metrics.fidelity_score * (1 - alpha) + performance.fidelity * alpha,
        correlation_accuracy: current_metrics.correlation_accuracy * (1 - alpha) + (performance.correlation_strength || 0) * alpha,
        bell_violation_strength: max(current_metrics.bell_violation_strength, performance[:bell_violations] || 0),
        decoherence_rate: current_metrics.decoherence_rate * (1 - alpha) + (performance[:decoherence_rate] || 0.1) * alpha
    }
  end

  defp detecting_degradation?(old_metrics, new_metrics) do
    fidelity_drop = old_metrics.fidelity_score - new_metrics.fidelity_score
    correlation_drop = old_metrics.correlation_accuracy - new_metrics.correlation_accuracy
    
    fidelity_drop > 0.05 || correlation_drop > 0.05
  end

  defp visualize_metric_evolution(old_metrics, new_metrics) do
    %{
      fidelity_change: %{
        old: old_metrics.fidelity_score,
        new: new_metrics.fidelity_score,
        delta: new_metrics.fidelity_score - old_metrics.fidelity_score,
        trend: if(new_metrics.fidelity_score > old_metrics.fidelity_score, do: :up, else: :down)
      },
      correlation_change: %{
        old: old_metrics.correlation_accuracy,
        new: new_metrics.correlation_accuracy,
        delta: new_metrics.correlation_accuracy - old_metrics.correlation_accuracy,
        trend: if(new_metrics.correlation_accuracy > old_metrics.correlation_accuracy, do: :up, else: :down)
      }
    }
  end

  defp generate_performance_heatmap(performance) do
    %{
      cells: [
        %{metric: "Fidelity", value: performance.fidelity, color: fidelity_to_color(performance.fidelity)},
        %{metric: "Correlation", value: performance.correlation_strength, color: fidelity_to_color(performance.correlation_strength)},
        %{metric: "Bell Test", value: performance[:bell_violations] || 0, color: bell_violation_color(performance[:bell_violations] || 0)},
        %{metric: "Accuracy", value: performance[:measurement_accuracy] || 0, color: fidelity_to_color(performance[:measurement_accuracy] || 0)}
      ],
      overall_color: overall_performance_color(performance[:overall_score] || 0.5)
    }
  end

  defp update_correlation_metrics(metrics, correlation_data) do
    %{
      metrics
      | correlation_accuracy: correlation_data[:accuracy] || metrics.correlation_accuracy,
        learning_curve: [correlation_data[:accuracy] || metrics.correlation_accuracy | Enum.take(metrics.learning_curve, 99)]
    }
  end

  defp apply_bell_test_reinforcement(state, results, reinforcement) do
    visual_feedback = if results[:chsh_parameter] > 2.0 do
      %{
        type: :bell_violation_success,
        animation: :quantum_glow,
        color: "#9C27B0",
        message: "Quantum entanglement confirmed!",
        intensity: :high
      }
    else
      %{
        type: :bell_test_classical,
        animation: :fade,
        color: "#607D8B",
        message: "Classical correlation detected",
        intensity: :low
      }
    end
    
    updated_buffer = Map.put(state.visual_buffer, :last_bell_feedback, visual_feedback)
    updated_metrics = %{
      state.metrics
      | bell_violation_strength: results[:chsh_parameter] || state.metrics.bell_violation_strength,
        reinforcement_signals: update_reinforcement_signals(state.metrics.reinforcement_signals, reinforcement)
    }
    
    %{state | visual_buffer: updated_buffer, metrics: updated_metrics}
  end

  # Utility functions
  defp fidelity_to_color(fidelity) when fidelity >= 0.9, do: "#4CAF50"
  defp fidelity_to_color(fidelity) when fidelity >= 0.7, do: "#8BC34A"
  defp fidelity_to_color(fidelity) when fidelity >= 0.5, do: "#FFC107"
  defp fidelity_to_color(fidelity) when fidelity >= 0.3, do: "#FF9800"
  defp fidelity_to_color(_), do: "#F44336"

  defp bell_violation_color(violation) when violation > 2.0, do: "#9C27B0"
  defp bell_violation_color(violation) when violation > 1.5, do: "#673AB7"
  defp bell_violation_color(_), do: "#607D8B"

  defp overall_performance_color(score) when score >= 0.9, do: "#00E676"
  defp overall_performance_color(score) when score >= 0.7, do: "#76FF03"
  defp overall_performance_color(score) when score >= 0.5, do: "#FFEB3B"
  defp overall_performance_color(_), do: "#FF5252"

  defp format_fidelity_label(fidelity) do
    percentage = round(fidelity * 100)
    "#{percentage}% Quantum Fidelity"
  end

  defp state_color(state) do
    case state do
      :superposition -> "#2196F3"
      :entangled -> "#9C27B0"
      :measured -> "#4CAF50"
      _ -> "#757575"
    end
  end

  defp improvement_color(improvement) do
    case improvement do
      :increase_coherence_time -> "#FF5722"
      :optimize_measurement_basis -> "#FFC107"
      :enhance_entanglement_generation -> "#9C27B0"
      _ -> "#757575"
    end
  end

  defp improvement_priority(improvement) do
    case improvement do
      :increase_coherence_time -> :high
      :optimize_measurement_basis -> :medium
      :enhance_entanglement_generation -> :high
      _ -> :low
    end
  end

  defp average_metric(history, field) do
    if Enum.empty?(history) do
      0.0
    else
      values = history
      |> Enum.map(fn item ->
        case item do
          %{result: result} -> Map.get(result, field, 0.0)
          _ -> Map.get(item, field, 0.0)
        end
      end)
      |> Enum.filter(&is_number/1)
      
      if Enum.empty?(values), do: 0.0, else: Enum.sum(values) / length(values)
    end
  end

  defp calculate_trend(history) do
    if length(history) < 2 do
      :stable
    else
      recent = Enum.take(history, 5)
      older = Enum.take(Enum.drop(history, 5), 5)
      
      recent_avg = average_metric(recent, :fidelity)
      older_avg = average_metric(older, :fidelity)
      
      cond do
        recent_avg > older_avg + 0.05 -> :improving
        recent_avg < older_avg - 0.05 -> :degrading
        true -> :stable
      end
    end
  end

  defp calculate_volatility(history) do
    if length(history) < 2 do
      0.0
    else
      values = history
      |> Enum.map(fn item ->
        case item do
          %{result: result} -> result.fidelity
          _ -> 0.0
        end
      end)
      
      mean = Enum.sum(values) / length(values)
      
      variance = Enum.reduce(values, 0.0, fn val, acc ->
        acc + :math.pow(val - mean, 2)
      end) / length(values)
      
      :math.sqrt(variance)
    end
  end

  defp find_best_performance(history) do
    if Enum.empty?(history) do
      nil
    else
      Enum.max_by(history, fn item ->
        case item do
          %{result: result} -> result.fidelity
          _ -> 0.0
        end
      end)
    end
  end

  defp update_reinforcement_signals(signals, reinforcement) do
    case reinforcement do
      :positive -> %{signals | positive: signals.positive + 1}
      :negative -> %{signals | negative: signals.negative + 1}
      :strong_positive -> %{signals | positive: signals.positive + 2}
      :needs_improvement -> %{signals | negative: signals.negative + 1}
      _ -> signals
    end
  end

  # Placeholder module references (would need actual implementations)
  defmodule Matrix do
    def zeros(rows, cols), do: List.duplicate(List.duplicate(0, cols), rows)
  end

  defmodule CircularBuffer do
    def new(size), do: %{size: size, data: [], pos: 0}
  end
end