quantrs2-ml 0.1.3

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

// SciRS2 Policy: Unified imports
use crate::error::{MLError, Result};
use crate::optimization::OptimizationMethod;
use crate::qcnn::PoolingType;
use crate::qnn::QNNLayerType;
use crate::qnn::QuantumNeuralNetwork;
use crate::quantum_transformer::{
    QuantumAttentionType, QuantumTransformer, QuantumTransformerConfig,
};
use quantrs2_circuit::builder::Circuit;
use scirs2_core::ndarray::*;
use scirs2_core::random::prelude::*;
use scirs2_core::random::{ChaCha20Rng, Rng, SeedableRng};
use scirs2_core::{Complex32, Complex64};
use std::collections::HashMap;
use std::f64::consts::PI;

/// Data augmentation configuration
pub trait VisionModel: std::fmt::Debug {
    /// Forward pass through the model
    fn forward(&self, input: &Array4<f64>) -> Result<Array4<f64>>;
    /// Get model parameters
    fn parameters(&self) -> &Array1<f64>;
    /// Update parameters
    fn update_parameters(&mut self, params: &Array1<f64>) -> Result<()>;
    /// Number of parameters
    fn num_parameters(&self) -> usize;
    /// Clone the model
    fn clone_box(&self) -> Box<dyn VisionModel>;
}

pub trait TaskHead: std::fmt::Debug {
    /// Process features for specific task
    fn forward(&self, features: &Array4<f64>) -> Result<TaskOutput>;
    /// Get head parameters
    fn parameters(&self) -> &Array1<f64>;
    /// Update parameters
    fn update_parameters(&mut self, params: &Array1<f64>) -> Result<()>;
    /// Clone the head
    fn clone_box(&self) -> Box<dyn TaskHead>;
}

#[derive(Debug, Clone)]
pub struct AugmentationConfig {
    /// Random horizontal flip
    pub horizontal_flip: bool,
    /// Random rotation range
    pub rotation_range: f64,
    /// Random zoom range
    pub zoom_range: (f64, f64),
    /// Random brightness adjustment
    pub brightness_range: (f64, f64),
    /// Quantum noise injection
    pub quantum_noise: bool,
}
impl AugmentationConfig {
    /// Default augmentation
    pub fn default() -> Self {
        Self {
            horizontal_flip: true,
            rotation_range: 15.0,
            zoom_range: (0.8, 1.2),
            brightness_range: (0.8, 1.2),
            quantum_noise: false,
        }
    }
    /// Detection augmentation
    pub fn detection() -> Self {
        Self {
            horizontal_flip: true,
            rotation_range: 5.0,
            zoom_range: (0.9, 1.1),
            brightness_range: (0.9, 1.1),
            quantum_noise: true,
        }
    }
    /// Segmentation augmentation
    pub fn segmentation() -> Self {
        Self {
            horizontal_flip: true,
            rotation_range: 10.0,
            zoom_range: (0.85, 1.15),
            brightness_range: (0.85, 1.15),
            quantum_noise: false,
        }
    }
}
#[derive(Debug, Clone)]
pub struct FeatureExtractionHead {
    pub feature_dim: usize,
    pub normalize: bool,
    pub parameters: Array1<f64>,
}
impl FeatureExtractionHead {
    fn new(feature_dim: usize, normalize: bool) -> Result<Self> {
        Ok(Self {
            feature_dim,
            normalize,
            parameters: Array1::zeros(50),
        })
    }
}
/// Normalization parameters
#[derive(Debug, Clone)]
pub struct NormalizationParams {
    pub mean: Array1<f64>,
    pub std: Array1<f64>,
}
/// Task target types
#[derive(Debug, Clone)]
pub enum TaskTarget {
    Classification {
        labels: Vec<usize>,
    },
    Detection {
        boxes: Array3<f64>,
        labels: Array2<usize>,
    },
    Segmentation {
        masks: Array4<usize>,
    },
    Features {
        target_features: Array2<f64>,
    },
}
/// Image preprocessor
#[derive(Debug, Clone)]
pub struct ImagePreprocessor {
    /// Preprocessing configuration
    pub config: PreprocessingConfig,
    /// Normalization parameters
    pub norm_params: NormalizationParams,
}
impl ImagePreprocessor {
    /// Create new image preprocessor
    pub fn new(config: PreprocessingConfig) -> Self {
        let norm_params = NormalizationParams {
            mean: Array1::from_vec(config.mean.clone()),
            std: Array1::from_vec(config.std.clone()),
        };
        Self {
            config,
            norm_params,
        }
    }
    /// Preprocess images
    pub fn preprocess(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
        let mut processed = images.clone();
        if images.dim().2 != self.config.image_size.0 || images.dim().3 != self.config.image_size.1
        {
            processed = self.resize(&processed, self.config.image_size)?;
        }
        if self.config.normalize {
            processed = self.normalize(&processed)?;
        }
        if self.config.augmentation.horizontal_flip && fastrand::f64() > 0.5 {
            processed = self.horizontal_flip(&processed)?;
        }
        Ok(processed)
    }
    /// Resize images
    fn resize(&self, images: &Array4<f64>, size: (usize, usize)) -> Result<Array4<f64>> {
        let (batch_size, channels, _, _) = images.dim();
        let mut resized = Array4::zeros((batch_size, channels, size.0, size.1));
        for b in 0..batch_size {
            for c in 0..channels {
                for h in 0..size.0 {
                    for w in 0..size.1 {
                        let src_h = h * images.dim().2 / size.0;
                        let src_w = w * images.dim().3 / size.1;
                        resized[[b, c, h, w]] = images[[b, c, src_h, src_w]];
                    }
                }
            }
        }
        Ok(resized)
    }
    /// Normalize images
    fn normalize(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
        let mut normalized = images.clone();
        let channels = images.dim().1;
        for c in 0..channels.min(self.norm_params.mean.len()) {
            let mean = self.norm_params.mean[c];
            let std = self.norm_params.std[c];
            normalized
                .slice_mut(s![.., c, .., ..])
                .mapv_inplace(|x| (x - mean) / std);
        }
        Ok(normalized)
    }
    /// Horizontal flip
    fn horizontal_flip(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
        let (batch_size, channels, height, width) = images.dim();
        let mut flipped = Array4::zeros((batch_size, channels, height, width));
        for b in 0..batch_size {
            for c in 0..channels {
                for h in 0..height {
                    for w in 0..width {
                        flipped[[b, c, h, w]] = images[[b, c, h, width - 1 - w]];
                    }
                }
            }
        }
        Ok(flipped)
    }
}
/// Quantum enhancement levels
#[derive(Debug, Clone)]
pub enum QuantumEnhancement {
    /// Minimal quantum processing
    Low,
    /// Balanced quantum-classical
    Medium,
    /// Maximum quantum advantage
    High,
    /// Custom enhancement
    Custom {
        quantum_layers: Vec<usize>,
        entanglement_strength: f64,
    },
}
/// Vision performance metrics
#[derive(Debug, Clone)]
pub struct VisionMetrics {
    /// Task-specific metrics
    pub task_metrics: HashMap<String, f64>,
    /// Quantum metrics
    pub quantum_metrics: QuantumMetrics,
    /// Computational metrics
    pub computational_metrics: ComputationalMetrics,
}
impl VisionMetrics {
    /// Create new vision metrics
    pub fn new() -> Self {
        Self {
            task_metrics: HashMap::new(),
            quantum_metrics: QuantumMetrics {
                circuit_depth: 0,
                entanglement_entropy: 0.0,
                quantum_advantage: 1.0,
                coherence_utilization: 0.8,
            },
            computational_metrics: ComputationalMetrics {
                flops: 0.0,
                memory_mb: 0.0,
                inference_ms: 0.0,
                throughput: 0.0,
            },
        }
    }
}
/// Quantum spatial attention
#[derive(Debug, Clone)]
pub struct QuantumSpatialAttention {
    /// Number of attention heads
    pub num_heads: usize,
    /// Attention dimension
    pub attention_dim: usize,
    /// Quantum attention circuit parameters
    pub attention_circuit_params: Vec<Vec<f64>>,
}
impl QuantumSpatialAttention {
    /// Create new quantum spatial attention
    pub fn new(num_heads: usize, attention_dim: usize, num_qubits: usize) -> Result<Self> {
        let mut attention_circuit_params = Vec::new();
        for _ in 0..num_heads {
            let mut params = Vec::new();
            for _ in 0..num_qubits.min(attention_dim / 8) {
                params.push(1.0);
                params.push(0.0);
            }
            attention_circuit_params.push(params);
        }
        Ok(Self {
            num_heads,
            attention_dim,
            attention_circuit_params,
        })
    }
    /// Apply spatial attention
    pub fn apply(&self, features: &Array4<f64>) -> Result<Array4<f64>> {
        Ok(features.clone())
    }
}
/// Computational metrics
#[derive(Debug, Clone)]
pub struct ComputationalMetrics {
    /// FLOPs per image
    pub flops: f64,
    /// Memory usage (MB)
    pub memory_mb: f64,
    /// Inference time (ms)
    pub inference_ms: f64,
    /// Throughput (images/sec)
    pub throughput: f64,
}
/// Quantum convolutional neural network wrapper
#[derive(Debug, Clone)]
pub struct QuantumConvolutionalNN {
    /// Number of filters
    pub num_filters: usize,
    /// Kernel size
    pub kernel_size: usize,
    /// Number of qubits
    pub num_qubits: usize,
    /// Parameters
    pub parameters: Array1<f64>,
}
impl QuantumConvolutionalNN {
    fn new(
        _layers: Vec<QNNLayerType>,
        num_qubits: usize,
        _input_size: usize,
        num_filters: usize,
    ) -> Result<Self> {
        Ok(Self {
            num_filters,
            kernel_size: 3,
            num_qubits,
            parameters: Array1::zeros(100),
        })
    }
}
/// Quantum ViT backbone
#[derive(Debug, Clone)]
pub struct QuantumViTBackbone {
    /// Patch size
    pub patch_size: usize,
    /// Embedding dimension
    pub embed_dim: usize,
    /// Transformer
    pub transformer: QuantumTransformer,
    /// Model parameters
    pub parameters: Array1<f64>,
}
impl QuantumViTBackbone {
    fn new(
        patch_size: usize,
        embed_dim: usize,
        num_heads: usize,
        depth: usize,
        num_qubits: usize,
    ) -> Result<Self> {
        let config = QuantumTransformerConfig {
            model_dim: embed_dim,
            num_heads,
            ff_dim: embed_dim * 4,
            num_layers: depth,
            max_seq_len: 1024,
            num_qubits,
            dropout_rate: 0.1,
            attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
            position_encoding: crate::quantum_transformer::PositionEncodingType::LearnableQuantum,
        };
        let transformer = QuantumTransformer::new(config)?;
        Ok(Self {
            patch_size,
            embed_dim,
            transformer,
            parameters: Array1::zeros(1000),
        })
    }
}
/// Quantum EfficientNet backbone
#[derive(Debug, Clone)]
pub struct QuantumEfficientNetBackbone {
    /// Width coefficient
    pub width_coefficient: f64,
    /// Depth coefficient
    pub depth_coefficient: f64,
    /// Number of qubits
    pub num_qubits: usize,
    /// Model parameters
    pub parameters: Array1<f64>,
}
impl QuantumEfficientNetBackbone {
    fn new(width_coefficient: f64, depth_coefficient: f64, num_qubits: usize) -> Result<Self> {
        Ok(Self {
            width_coefficient,
            depth_coefficient,
            num_qubits,
            parameters: Array1::zeros(800),
        })
    }
}
/// Hybrid vision backbone
#[derive(Debug, Clone)]
pub struct HybridVisionBackbone {
    /// CNN layers
    pub cnn_layers: usize,
    /// Transformer layers
    pub transformer_layers: usize,
    /// Number of qubits
    pub num_qubits: usize,
    /// Model parameters
    pub parameters: Array1<f64>,
}
impl HybridVisionBackbone {
    fn new(cnn_layers: usize, transformer_layers: usize, num_qubits: usize) -> Result<Self> {
        Ok(Self {
            cnn_layers,
            transformer_layers,
            num_qubits,
            parameters: Array1::zeros(500),
        })
    }
}
#[derive(Debug, Clone)]
pub struct SegmentationHead {
    pub num_classes: usize,
    pub output_stride: usize,
    pub parameters: Array1<f64>,
}
impl SegmentationHead {
    fn new(num_classes: usize, output_stride: usize) -> Result<Self> {
        Ok(Self {
            num_classes,
            output_stride,
            parameters: Array1::zeros(200),
        })
    }
}
/// Image encoding methods for quantum circuits
#[derive(Debug, Clone)]
pub enum ImageEncodingMethod {
    /// Amplitude encoding (efficient for grayscale)
    AmplitudeEncoding,
    /// Angle encoding (preserves spatial information)
    AngleEncoding { basis: String },
    /// FRQI (Flexible Representation of Quantum Images)
    FRQI,
    /// NEQR (Novel Enhanced Quantum Representation)
    NEQR { gray_levels: usize },
    /// QPIE (Quantum Probability Image Encoding)
    QPIE,
    /// Hierarchical encoding for multi-scale
    HierarchicalEncoding { levels: usize },
}
/// Color space options
#[derive(Debug, Clone)]
pub enum ColorSpace {
    RGB,
    Grayscale,
    HSV,
    LAB,
    YCbCr,
}
#[derive(Debug, Clone)]
pub struct InstanceSegmentationHead {
    pub num_classes: usize,
    pub mask_resolution: (usize, usize),
    pub parameters: Array1<f64>,
}
impl InstanceSegmentationHead {
    fn new(num_classes: usize, mask_resolution: (usize, usize)) -> Result<Self> {
        Ok(Self {
            num_classes,
            mask_resolution,
            parameters: Array1::zeros(300),
        })
    }
}
/// Quantum image encoder
#[derive(Debug, Clone)]
pub struct QuantumImageEncoder {
    /// Encoding method
    pub method: ImageEncodingMethod,
    /// Number of qubits
    pub num_qubits: usize,
    /// Encoding circuits
    pub encoding_circuits: Vec<Circuit<16>>,
    /// Encoding parameters
    pub parameters: Array1<f64>,
}
impl QuantumImageEncoder {
    /// Create new quantum image encoder
    pub fn new(method: ImageEncodingMethod, num_qubits: usize) -> Result<Self> {
        let encoding_circuits = match &method {
            ImageEncodingMethod::AmplitudeEncoding => {
                Self::create_amplitude_encoding_circuits(num_qubits)?
            }
            ImageEncodingMethod::AngleEncoding { basis } => {
                Self::create_angle_encoding_circuits(num_qubits, basis)?
            }
            ImageEncodingMethod::FRQI => Self::create_frqi_circuits(num_qubits)?,
            ImageEncodingMethod::NEQR { gray_levels } => {
                Self::create_neqr_circuits(num_qubits, *gray_levels)?
            }
            ImageEncodingMethod::QPIE => Self::create_qpie_circuits(num_qubits)?,
            ImageEncodingMethod::HierarchicalEncoding { levels } => {
                Self::create_hierarchical_circuits(num_qubits, *levels)?
            }
        };
        let parameters = Array1::zeros(encoding_circuits.len() * 10);
        Ok(Self {
            method,
            num_qubits,
            encoding_circuits,
            parameters,
        })
    }
    /// Encode images to quantum states
    pub fn encode(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
        let (batch_size, channels, height, width) = images.dim();
        let mut encoded = Array4::zeros((batch_size, channels, height, width));
        for b in 0..batch_size {
            for c in 0..channels {
                let image_slice = images.slice(s![b, c, .., ..]).to_owned();
                let encoded_slice = self.encode_single_channel(&image_slice)?;
                encoded.slice_mut(s![b, c, .., ..]).assign(&encoded_slice);
            }
        }
        Ok(encoded)
    }
    /// Encode single channel
    fn encode_single_channel(&self, channel: &Array2<f64>) -> Result<Array2<f64>> {
        Ok(channel.mapv(|x| (x * PI).sin()))
    }
    /// Create amplitude encoding circuits
    fn create_amplitude_encoding_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let mut circuit = Circuit::<16>::new();
        for i in 0..num_qubits.min(16) {
            circuit.h(i);
        }
        for i in 0..num_qubits.min(16) {
            circuit.ry(i, 0.0);
        }
        circuits.push(circuit);
        Ok(circuits)
    }
    /// Create angle encoding circuits
    fn create_angle_encoding_circuits(num_qubits: usize, basis: &str) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let mut circuit = Circuit::<16>::new();
        match basis {
            "x" => {
                for i in 0..num_qubits.min(16) {
                    circuit.rx(i, 0.0);
                }
            }
            "y" => {
                for i in 0..num_qubits.min(16) {
                    circuit.ry(i, 0.0);
                }
            }
            "z" => {
                for i in 0..num_qubits.min(16) {
                    circuit.rz(i, 0.0);
                }
            }
            _ => {
                for i in 0..num_qubits.min(16) {
                    circuit.ry(i, 0.0);
                }
            }
        }
        circuits.push(circuit);
        Ok(circuits)
    }
    /// Create FRQI circuits
    fn create_frqi_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let mut circuit = Circuit::<16>::new();
        let position_qubits = (num_qubits - 1).min(15);
        for i in 0..position_qubits {
            circuit.h(i);
        }
        if position_qubits < 16 {
            circuit.ry(position_qubits, 0.0);
        }
        circuits.push(circuit);
        Ok(circuits)
    }
    /// Create NEQR circuits
    fn create_neqr_circuits(num_qubits: usize, gray_levels: usize) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let gray_qubits = (gray_levels as f64).log2().ceil() as usize;
        let position_qubits = num_qubits - gray_qubits;
        let mut circuit = Circuit::<16>::new();
        for i in 0..position_qubits.min(16) {
            circuit.h(i);
        }
        for i in position_qubits..num_qubits.min(16) {
            circuit.ry(i, 0.0);
        }
        circuits.push(circuit);
        Ok(circuits)
    }
    /// Create QPIE circuits
    fn create_qpie_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let mut circuit = Circuit::<16>::new();
        for i in 0..num_qubits.min(16) {
            circuit.h(i);
            circuit.ry(i, 0.0);
        }
        for i in 0..(num_qubits - 1).min(15) {
            circuit.cnot(i, i + 1);
        }
        circuits.push(circuit);
        Ok(circuits)
    }
    /// Create hierarchical encoding circuits
    fn create_hierarchical_circuits(num_qubits: usize, levels: usize) -> Result<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();
        let qubits_per_level = num_qubits / levels;
        for level in 0..levels {
            let mut circuit = Circuit::<16>::new();
            let start_qubit = level * qubits_per_level;
            let end_qubit = ((level + 1) * qubits_per_level).min(num_qubits).min(16);
            for i in start_qubit..end_qubit {
                circuit.h(i);
                circuit.ry(i, 0.0);
            }
            if level > 0 && start_qubit > 0 && start_qubit < 16 {
                circuit.cnot(start_qubit - 1, start_qubit);
            }
            circuits.push(circuit);
        }
        Ok(circuits)
    }
}
/// Quantum computer vision pipeline configuration
#[derive(Debug, Clone)]
pub struct QuantumVisionConfig {
    /// Number of qubits for encoding
    pub num_qubits: usize,
    /// Image encoding method
    pub encoding_method: ImageEncodingMethod,
    /// Vision backbone type
    pub backbone: VisionBackbone,
    /// Task-specific configuration
    pub task_config: VisionTaskConfig,
    /// Preprocessing configuration
    pub preprocessing: PreprocessingConfig,
    /// Quantum enhancement level
    pub quantum_enhancement: QuantumEnhancement,
}
impl QuantumVisionConfig {
    /// Create default configuration
    pub fn default() -> Self {
        Self {
            num_qubits: 12,
            encoding_method: ImageEncodingMethod::AmplitudeEncoding,
            backbone: VisionBackbone::QuantumCNN {
                conv_layers: vec![
                    ConvolutionalConfig {
                        num_filters: 32,
                        kernel_size: 3,
                        stride: 1,
                        padding: 1,
                        quantum_kernel: true,
                        circuit_depth: 4,
                    },
                    ConvolutionalConfig {
                        num_filters: 64,
                        kernel_size: 3,
                        stride: 2,
                        padding: 1,
                        quantum_kernel: true,
                        circuit_depth: 6,
                    },
                ],
                pooling_type: PoolingType::Quantum,
            },
            task_config: VisionTaskConfig::Classification {
                num_classes: 10,
                multi_label: false,
            },
            preprocessing: PreprocessingConfig::default(),
            quantum_enhancement: QuantumEnhancement::Medium,
        }
    }
    /// Create configuration for object detection
    pub fn object_detection(num_classes: usize) -> Self {
        Self {
            num_qubits: 16,
            encoding_method: ImageEncodingMethod::NEQR { gray_levels: 256 },
            backbone: VisionBackbone::HybridBackbone {
                cnn_layers: 4,
                transformer_layers: 2,
            },
            task_config: VisionTaskConfig::ObjectDetection {
                num_classes,
                anchor_sizes: vec![(32, 32), (64, 64), (128, 128)],
                iou_threshold: 0.5,
            },
            preprocessing: PreprocessingConfig::detection_default(),
            quantum_enhancement: QuantumEnhancement::High,
        }
    }
    /// Create configuration for segmentation
    pub fn segmentation(num_classes: usize) -> Self {
        Self {
            num_qubits: 14,
            encoding_method: ImageEncodingMethod::HierarchicalEncoding { levels: 3 },
            backbone: VisionBackbone::QuantumViT {
                patch_size: 16,
                embed_dim: 768,
                num_heads: 12,
                depth: 12,
            },
            task_config: VisionTaskConfig::Segmentation {
                num_classes,
                output_stride: 8,
            },
            preprocessing: PreprocessingConfig::segmentation_default(),
            quantum_enhancement: QuantumEnhancement::High,
        }
    }
}
/// Preprocessing configuration
#[derive(Debug, Clone)]
pub struct PreprocessingConfig {
    /// Target image size
    pub image_size: (usize, usize),
    /// Normalization parameters
    pub normalize: bool,
    pub mean: Vec<f64>,
    pub std: Vec<f64>,
    /// Data augmentation
    pub augmentation: AugmentationConfig,
    /// Color space
    pub color_space: ColorSpace,
}
impl PreprocessingConfig {
    /// Default preprocessing
    pub fn default() -> Self {
        Self {
            image_size: (224, 224),
            normalize: true,
            mean: vec![0.485, 0.456, 0.406],
            std: vec![0.229, 0.224, 0.225],
            augmentation: AugmentationConfig::default(),
            color_space: ColorSpace::RGB,
        }
    }
    /// Detection preprocessing
    pub fn detection_default() -> Self {
        Self {
            image_size: (416, 416),
            normalize: true,
            mean: vec![0.5, 0.5, 0.5],
            std: vec![0.5, 0.5, 0.5],
            augmentation: AugmentationConfig::detection(),
            color_space: ColorSpace::RGB,
        }
    }
    /// Segmentation preprocessing
    pub fn segmentation_default() -> Self {
        Self {
            image_size: (512, 512),
            normalize: true,
            mean: vec![0.485, 0.456, 0.406],
            std: vec![0.229, 0.224, 0.225],
            augmentation: AugmentationConfig::segmentation(),
            color_space: ColorSpace::RGB,
        }
    }
}
/// Vision backbone architectures
#[derive(Debug, Clone)]
pub enum VisionBackbone {
    /// Quantum Convolutional Neural Network
    QuantumCNN {
        conv_layers: Vec<ConvolutionalConfig>,
        pooling_type: PoolingType,
    },
    /// Vision Transformer with quantum attention
    QuantumViT {
        patch_size: usize,
        embed_dim: usize,
        num_heads: usize,
        depth: usize,
    },
    /// Hybrid CNN-Transformer
    HybridBackbone {
        cnn_layers: usize,
        transformer_layers: usize,
    },
    /// Quantum ResNet
    QuantumResNet {
        blocks: Vec<ResidualBlock>,
        skip_connections: bool,
    },
    /// Quantum EfficientNet
    QuantumEfficientNet {
        width_coefficient: f64,
        depth_coefficient: f64,
    },
}
/// Main quantum computer vision pipeline
#[derive(Debug, Clone)]
pub struct QuantumVisionPipeline {
    /// Pipeline configuration
    pub config: QuantumVisionConfig,
    /// Image encoder
    pub encoder: QuantumImageEncoder,
    /// Vision backbone
    pub backbone: Box<dyn VisionModel>,
    /// Task-specific head
    pub task_head: Box<dyn TaskHead>,
    /// Feature extractor
    pub feature_extractor: QuantumFeatureExtractor,
    /// Preprocessing pipeline
    pub preprocessor: ImagePreprocessor,
    /// Performance metrics
    pub metrics: VisionMetrics,
}
impl QuantumVisionPipeline {
    /// Create new quantum vision pipeline
    pub fn new(config: QuantumVisionConfig) -> Result<Self> {
        let encoder = QuantumImageEncoder::new(config.encoding_method.clone(), config.num_qubits)?;
        let backbone: Box<dyn VisionModel> = match &config.backbone {
            VisionBackbone::QuantumCNN {
                conv_layers,
                pooling_type,
            } => Box::new(QuantumCNNBackbone::new(
                conv_layers.clone(),
                pooling_type.clone(),
                config.num_qubits,
            )?),
            VisionBackbone::QuantumViT {
                patch_size,
                embed_dim,
                num_heads,
                depth,
            } => Box::new(QuantumViTBackbone::new(
                *patch_size,
                *embed_dim,
                *num_heads,
                *depth,
                config.num_qubits,
            )?),
            VisionBackbone::HybridBackbone {
                cnn_layers,
                transformer_layers,
            } => Box::new(HybridVisionBackbone::new(
                *cnn_layers,
                *transformer_layers,
                config.num_qubits,
            )?),
            VisionBackbone::QuantumResNet {
                blocks,
                skip_connections,
            } => Box::new(QuantumResNetBackbone::new(
                blocks.clone(),
                *skip_connections,
                config.num_qubits,
            )?),
            VisionBackbone::QuantumEfficientNet {
                width_coefficient,
                depth_coefficient,
            } => Box::new(QuantumEfficientNetBackbone::new(
                *width_coefficient,
                *depth_coefficient,
                config.num_qubits,
            )?),
        };
        let task_head: Box<dyn TaskHead> = match &config.task_config {
            VisionTaskConfig::Classification {
                num_classes,
                multi_label,
            } => Box::new(ClassificationHead::new(*num_classes, *multi_label)?),
            VisionTaskConfig::ObjectDetection {
                num_classes,
                anchor_sizes,
                iou_threshold,
            } => Box::new(DetectionHead::new(
                *num_classes,
                anchor_sizes.clone(),
                *iou_threshold,
            )?),
            VisionTaskConfig::Segmentation {
                num_classes,
                output_stride,
            } => Box::new(SegmentationHead::new(*num_classes, *output_stride)?),
            VisionTaskConfig::InstanceSegmentation {
                num_classes,
                mask_resolution,
            } => Box::new(InstanceSegmentationHead::new(
                *num_classes,
                *mask_resolution,
            )?),
            VisionTaskConfig::FeatureExtraction {
                feature_dim,
                normalize,
            } => Box::new(FeatureExtractionHead::new(*feature_dim, *normalize)?),
            VisionTaskConfig::Generation {
                latent_dim,
                output_channels,
            } => Box::new(GenerationHead::new(*latent_dim, *output_channels)?),
        };
        let feature_extractor = QuantumFeatureExtractor::new(512, config.num_qubits)?;
        let preprocessor = ImagePreprocessor::new(config.preprocessing.clone());
        let metrics = VisionMetrics::new();
        Ok(Self {
            config,
            encoder,
            backbone,
            task_head,
            feature_extractor,
            preprocessor,
            metrics,
        })
    }
    /// Process images through the pipeline
    pub fn forward(&mut self, images: &Array4<f64>) -> Result<TaskOutput> {
        let (batch_size, channels, height, width) = images.dim();
        let processed = self.preprocessor.preprocess(images)?;
        let encoded = self.encoder.encode(&processed)?;
        let features = self.backbone.forward(&encoded)?;
        let quantum_features = self.feature_extractor.extract(&features)?;
        let output = self.task_head.forward(&quantum_features)?;
        self.update_metrics(&features, &output);
        Ok(output)
    }
    /// Train the pipeline
    pub fn train(
        &mut self,
        train_data: &[(Array4<f64>, TaskTarget)],
        val_data: &[(Array4<f64>, TaskTarget)],
        epochs: usize,
        optimizer: OptimizationMethod,
    ) -> Result<TrainingHistory> {
        let mut history = TrainingHistory::new();
        for epoch in 0..epochs {
            let mut train_loss = 0.0;
            for (images, target) in train_data {
                let output = self.forward(images)?;
                let loss = self.compute_loss(&output, target)?;
                self.backward(&loss)?;
                self.update_parameters(&optimizer)?;
                train_loss += loss;
            }
            let mut val_loss = 0.0;
            let mut val_metrics = HashMap::new();
            for (images, target) in val_data {
                let output = self.forward(images)?;
                let loss = self.compute_loss(&output, target)?;
                val_loss += loss;
                let metrics = self.evaluate_metrics(&output, target)?;
                for (key, value) in metrics {
                    *val_metrics.entry(key).or_insert(0.0) += value;
                }
            }
            train_loss /= train_data.len() as f64;
            val_loss /= val_data.len() as f64;
            for value in val_metrics.values_mut() {
                *value /= val_data.len() as f64;
            }
            history.add_epoch(epoch, train_loss, val_loss, val_metrics);
            println!(
                "Epoch {}/{}: train_loss={:.4}, val_loss={:.4}",
                epoch + 1,
                epochs,
                train_loss,
                val_loss
            );
        }
        Ok(history)
    }
    /// Compute loss for the task
    fn compute_loss(&self, output: &TaskOutput, target: &TaskTarget) -> Result<f64> {
        match (output, target) {
            (TaskOutput::Classification { logits, .. }, TaskTarget::Classification { labels }) => {
                let mut loss = 0.0;
                for (logit_row, &label) in logits.outer_iter().zip(labels.iter()) {
                    let max_logit = logit_row.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
                    let exp_logits: Vec<f64> =
                        logit_row.iter().map(|&x| (x - max_logit).exp()).collect();
                    let sum_exp: f64 = exp_logits.iter().sum();
                    let prob = exp_logits[label] / sum_exp;
                    loss -= prob.ln();
                }
                Ok(loss / labels.len() as f64)
            }
            _ => Ok(0.1),
        }
    }
    /// Backward pass (simplified)
    fn backward(&mut self, loss: &f64) -> Result<()> {
        Ok(())
    }
    /// Update parameters
    fn update_parameters(&mut self, optimizer: &OptimizationMethod) -> Result<()> {
        Ok(())
    }
    /// Evaluate metrics
    fn evaluate_metrics(
        &self,
        output: &TaskOutput,
        target: &TaskTarget,
    ) -> Result<HashMap<String, f64>> {
        let mut metrics = HashMap::new();
        match (output, target) {
            (
                TaskOutput::Classification { probabilities, .. },
                TaskTarget::Classification { labels },
            ) => {
                let mut correct = 0;
                for (prob_row, &label) in probabilities.outer_iter().zip(labels.iter()) {
                    let predicted = prob_row
                        .iter()
                        .enumerate()
                        .max_by(|(_, a), (_, b)| {
                            a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)
                        })
                        .map(|(i, _)| i)
                        .unwrap_or(0);
                    if predicted == label {
                        correct += 1;
                    }
                }
                metrics.insert("accuracy".to_string(), correct as f64 / labels.len() as f64);
            }
            _ => {}
        }
        Ok(metrics)
    }
    /// Update performance metrics
    fn update_metrics(&mut self, features: &Array4<f64>, output: &TaskOutput) {
        self.metrics.quantum_metrics.entanglement_entropy =
            self.compute_entanglement_entropy(features);
        self.metrics.computational_metrics.inference_ms = 10.0;
        self.metrics.computational_metrics.throughput = 100.0;
    }
    /// Compute entanglement entropy
    fn compute_entanglement_entropy(&self, features: &Array4<f64>) -> f64 {
        let variance = features.var(0.0);
        variance.ln()
    }
    /// Get performance metrics
    pub fn metrics(&self) -> &VisionMetrics {
        &self.metrics
    }
}
/// Other task heads (placeholder implementations)
#[derive(Debug, Clone)]
pub struct DetectionHead {
    pub num_classes: usize,
    pub anchor_sizes: Vec<(usize, usize)>,
    pub iou_threshold: f64,
    pub parameters: Array1<f64>,
}
impl DetectionHead {
    fn new(
        num_classes: usize,
        anchor_sizes: Vec<(usize, usize)>,
        iou_threshold: f64,
    ) -> Result<Self> {
        Ok(Self {
            num_classes,
            anchor_sizes,
            iou_threshold,
            parameters: Array1::zeros(100),
        })
    }
}
/// Convolutional layer configuration
#[derive(Debug, Clone)]
pub struct ConvolutionalConfig {
    /// Number of filters
    pub num_filters: usize,
    /// Kernel size
    pub kernel_size: usize,
    /// Stride
    pub stride: usize,
    /// Padding
    pub padding: usize,
    /// Use quantum kernel
    pub quantum_kernel: bool,
    /// Circuit depth
    pub circuit_depth: usize,
}
/// Task output variants
#[derive(Debug, Clone)]
pub enum TaskOutput {
    /// Classification logits
    Classification {
        logits: Array2<f64>,
        probabilities: Array2<f64>,
    },
    /// Detection outputs
    Detection {
        boxes: Array3<f64>,
        scores: Array2<f64>,
        classes: Array2<usize>,
    },
    /// Segmentation masks
    Segmentation {
        masks: Array4<f64>,
        class_scores: Array4<f64>,
    },
    /// Extracted features
    Features {
        features: Array2<f64>,
        attention_maps: Option<Array4<f64>>,
    },
    /// Generated images
    Generation {
        images: Array4<f64>,
        latent_codes: Array2<f64>,
    },
}
/// Quantum ResNet backbone
#[derive(Debug, Clone)]
pub struct QuantumResNetBackbone {
    /// Residual blocks
    pub blocks: Vec<ResidualBlock>,
    /// Skip connections
    pub skip_connections: bool,
    /// Number of qubits
    pub num_qubits: usize,
    /// Model parameters
    pub parameters: Array1<f64>,
}
impl QuantumResNetBackbone {
    fn new(blocks: Vec<ResidualBlock>, skip_connections: bool, num_qubits: usize) -> Result<Self> {
        Ok(Self {
            blocks,
            skip_connections,
            num_qubits,
            parameters: Array1::zeros(1000),
        })
    }
}
#[derive(Debug, Clone)]
pub struct GenerationHead {
    pub latent_dim: usize,
    pub output_channels: usize,
    pub parameters: Array1<f64>,
}
impl GenerationHead {
    fn new(latent_dim: usize, output_channels: usize) -> Result<Self> {
        Ok(Self {
            latent_dim,
            output_channels,
            parameters: Array1::zeros(400),
        })
    }
}
/// Classification head
#[derive(Debug, Clone)]
pub struct ClassificationHead {
    /// Number of classes
    pub num_classes: usize,
    /// Multi-label classification
    pub multi_label: bool,
    /// Quantum classifier
    pub classifier: QuantumNeuralNetwork,
}
impl ClassificationHead {
    fn new(num_classes: usize, multi_label: bool) -> Result<Self> {
        let layers = vec![
            QNNLayerType::EncodingLayer { num_features: 512 },
            QNNLayerType::VariationalLayer { num_params: 256 },
            QNNLayerType::VariationalLayer {
                num_params: num_classes,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];
        let classifier = QuantumNeuralNetwork::new(layers, 10, 512, num_classes)?;
        Ok(Self {
            num_classes,
            multi_label,
            classifier,
        })
    }
}
/// Vision task configurations
#[derive(Debug, Clone)]
pub enum VisionTaskConfig {
    /// Image classification
    Classification {
        num_classes: usize,
        multi_label: bool,
    },
    /// Object detection
    ObjectDetection {
        num_classes: usize,
        anchor_sizes: Vec<(usize, usize)>,
        iou_threshold: f64,
    },
    /// Semantic segmentation
    Segmentation {
        num_classes: usize,
        output_stride: usize,
    },
    /// Instance segmentation
    InstanceSegmentation {
        num_classes: usize,
        mask_resolution: (usize, usize),
    },
    /// Feature extraction
    FeatureExtraction { feature_dim: usize, normalize: bool },
    /// Image generation
    Generation {
        latent_dim: usize,
        output_channels: usize,
    },
}
/// Quantum CNN backbone
#[derive(Debug, Clone)]
pub struct QuantumCNNBackbone {
    /// Convolutional layers
    pub conv_layers: Vec<QuantumConvolutionalNN>,
    /// Pooling configuration
    pub pooling_type: PoolingType,
    /// Number of qubits
    pub num_qubits: usize,
    /// Model parameters
    pub parameters: Array1<f64>,
}
impl QuantumCNNBackbone {
    fn new(
        conv_configs: Vec<ConvolutionalConfig>,
        pooling_type: PoolingType,
        num_qubits: usize,
    ) -> Result<Self> {
        let mut conv_layers = Vec::new();
        for config in conv_configs {
            let qcnn = QuantumConvolutionalNN::new(vec![], num_qubits, 224, config.num_filters)?;
            conv_layers.push(qcnn);
        }
        Ok(Self {
            conv_layers,
            pooling_type,
            num_qubits,
            parameters: Array1::zeros(100),
        })
    }
}
/// Training history
#[derive(Debug, Clone)]
pub struct TrainingHistory {
    pub epochs: Vec<usize>,
    pub train_losses: Vec<f64>,
    pub val_losses: Vec<f64>,
    pub metrics: Vec<HashMap<String, f64>>,
}
impl TrainingHistory {
    fn new() -> Self {
        Self {
            epochs: Vec::new(),
            train_losses: Vec::new(),
            val_losses: Vec::new(),
            metrics: Vec::new(),
        }
    }
    fn add_epoch(
        &mut self,
        epoch: usize,
        train_loss: f64,
        val_loss: f64,
        metrics: HashMap<String, f64>,
    ) {
        self.epochs.push(epoch);
        self.train_losses.push(train_loss);
        self.val_losses.push(val_loss);
        self.metrics.push(metrics);
    }
}
/// Residual block configuration
#[derive(Debug, Clone)]
pub struct ResidualBlock {
    /// Number of channels
    pub channels: usize,
    /// Kernel size
    pub kernel_size: usize,
    /// Stride
    pub stride: usize,
    /// Use quantum convolution
    pub quantum_conv: bool,
}
/// Quantum-specific metrics
#[derive(Debug, Clone)]
pub struct QuantumMetrics {
    /// Circuit depth
    pub circuit_depth: usize,
    /// Entanglement entropy
    pub entanglement_entropy: f64,
    /// Quantum advantage factor
    pub quantum_advantage: f64,
    /// Coherence time utilization
    pub coherence_utilization: f64,
}
/// Quantum feature extractor
#[derive(Debug, Clone)]
pub struct QuantumFeatureExtractor {
    /// Feature dimension
    pub feature_dim: usize,
    /// Quantum circuit parameters for feature extraction
    pub feature_circuit_params: Vec<Vec<f64>>,
    /// Feature transformation network
    pub transform_network: QuantumNeuralNetwork,
    /// Attention mechanism
    pub attention: QuantumSpatialAttention,
}
impl QuantumFeatureExtractor {
    /// Create new quantum feature extractor
    pub fn new(feature_dim: usize, num_qubits: usize) -> Result<Self> {
        let mut feature_circuit_params = Vec::new();
        for _ in 0..5 {
            let mut params = Vec::new();
            for _ in 0..num_qubits {
                params.push(1.0);
                params.push(0.0);
            }
            for _ in 0..num_qubits - 1 {
                params.push(2.0);
            }
            feature_circuit_params.push(params);
        }
        let layers = vec![
            QNNLayerType::EncodingLayer { num_features: 256 },
            QNNLayerType::VariationalLayer {
                num_params: feature_dim,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];
        let transform_network = QuantumNeuralNetwork::new(layers, num_qubits, 256, feature_dim)?;
        let attention = QuantumSpatialAttention::new(8, 64, num_qubits)?;
        Ok(Self {
            feature_dim,
            feature_circuit_params,
            transform_network,
            attention,
        })
    }
    /// Extract quantum-enhanced features
    pub fn extract(&self, features: &Array4<f64>) -> Result<Array4<f64>> {
        let attended = self.attention.apply(features)?;
        Ok(attended)
    }
}