quantrs2_ml/computer_vision/
types.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5// SciRS2 Policy: Unified imports
6use crate::error::{MLError, Result};
7use crate::optimization::OptimizationMethod;
8use crate::qcnn::PoolingType;
9use crate::qnn::QNNLayerType;
10use crate::qnn::QuantumNeuralNetwork;
11use crate::quantum_transformer::{
12    QuantumAttentionType, QuantumTransformer, QuantumTransformerConfig,
13};
14use quantrs2_circuit::builder::Circuit;
15use scirs2_core::ndarray::*;
16use scirs2_core::random::prelude::*;
17use scirs2_core::random::{ChaCha20Rng, Rng, SeedableRng};
18use scirs2_core::{Complex32, Complex64};
19use std::collections::HashMap;
20use std::f64::consts::PI;
21
22/// Data augmentation configuration
23pub trait VisionModel: std::fmt::Debug {
24    /// Forward pass through the model
25    fn forward(&self, input: &Array4<f64>) -> Result<Array4<f64>>;
26    /// Get model parameters
27    fn parameters(&self) -> &Array1<f64>;
28    /// Update parameters
29    fn update_parameters(&mut self, params: &Array1<f64>) -> Result<()>;
30    /// Number of parameters
31    fn num_parameters(&self) -> usize;
32    /// Clone the model
33    fn clone_box(&self) -> Box<dyn VisionModel>;
34}
35
36pub trait TaskHead: std::fmt::Debug {
37    /// Process features for specific task
38    fn forward(&self, features: &Array4<f64>) -> Result<TaskOutput>;
39    /// Get head parameters
40    fn parameters(&self) -> &Array1<f64>;
41    /// Update parameters
42    fn update_parameters(&mut self, params: &Array1<f64>) -> Result<()>;
43    /// Clone the head
44    fn clone_box(&self) -> Box<dyn TaskHead>;
45}
46
47#[derive(Debug, Clone)]
48pub struct AugmentationConfig {
49    /// Random horizontal flip
50    pub horizontal_flip: bool,
51    /// Random rotation range
52    pub rotation_range: f64,
53    /// Random zoom range
54    pub zoom_range: (f64, f64),
55    /// Random brightness adjustment
56    pub brightness_range: (f64, f64),
57    /// Quantum noise injection
58    pub quantum_noise: bool,
59}
60impl AugmentationConfig {
61    /// Default augmentation
62    pub fn default() -> Self {
63        Self {
64            horizontal_flip: true,
65            rotation_range: 15.0,
66            zoom_range: (0.8, 1.2),
67            brightness_range: (0.8, 1.2),
68            quantum_noise: false,
69        }
70    }
71    /// Detection augmentation
72    pub fn detection() -> Self {
73        Self {
74            horizontal_flip: true,
75            rotation_range: 5.0,
76            zoom_range: (0.9, 1.1),
77            brightness_range: (0.9, 1.1),
78            quantum_noise: true,
79        }
80    }
81    /// Segmentation augmentation
82    pub fn segmentation() -> Self {
83        Self {
84            horizontal_flip: true,
85            rotation_range: 10.0,
86            zoom_range: (0.85, 1.15),
87            brightness_range: (0.85, 1.15),
88            quantum_noise: false,
89        }
90    }
91}
92#[derive(Debug, Clone)]
93pub struct FeatureExtractionHead {
94    pub feature_dim: usize,
95    pub normalize: bool,
96    pub parameters: Array1<f64>,
97}
98impl FeatureExtractionHead {
99    fn new(feature_dim: usize, normalize: bool) -> Result<Self> {
100        Ok(Self {
101            feature_dim,
102            normalize,
103            parameters: Array1::zeros(50),
104        })
105    }
106}
107/// Normalization parameters
108#[derive(Debug, Clone)]
109pub struct NormalizationParams {
110    pub mean: Array1<f64>,
111    pub std: Array1<f64>,
112}
113/// Task target types
114#[derive(Debug, Clone)]
115pub enum TaskTarget {
116    Classification {
117        labels: Vec<usize>,
118    },
119    Detection {
120        boxes: Array3<f64>,
121        labels: Array2<usize>,
122    },
123    Segmentation {
124        masks: Array4<usize>,
125    },
126    Features {
127        target_features: Array2<f64>,
128    },
129}
130/// Image preprocessor
131#[derive(Debug, Clone)]
132pub struct ImagePreprocessor {
133    /// Preprocessing configuration
134    pub config: PreprocessingConfig,
135    /// Normalization parameters
136    pub norm_params: NormalizationParams,
137}
138impl ImagePreprocessor {
139    /// Create new image preprocessor
140    pub fn new(config: PreprocessingConfig) -> Self {
141        let norm_params = NormalizationParams {
142            mean: Array1::from_vec(config.mean.clone()),
143            std: Array1::from_vec(config.std.clone()),
144        };
145        Self {
146            config,
147            norm_params,
148        }
149    }
150    /// Preprocess images
151    pub fn preprocess(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
152        let mut processed = images.clone();
153        if images.dim().2 != self.config.image_size.0 || images.dim().3 != self.config.image_size.1
154        {
155            processed = self.resize(&processed, self.config.image_size)?;
156        }
157        if self.config.normalize {
158            processed = self.normalize(&processed)?;
159        }
160        if self.config.augmentation.horizontal_flip && fastrand::f64() > 0.5 {
161            processed = self.horizontal_flip(&processed)?;
162        }
163        Ok(processed)
164    }
165    /// Resize images
166    fn resize(&self, images: &Array4<f64>, size: (usize, usize)) -> Result<Array4<f64>> {
167        let (batch_size, channels, _, _) = images.dim();
168        let mut resized = Array4::zeros((batch_size, channels, size.0, size.1));
169        for b in 0..batch_size {
170            for c in 0..channels {
171                for h in 0..size.0 {
172                    for w in 0..size.1 {
173                        let src_h = h * images.dim().2 / size.0;
174                        let src_w = w * images.dim().3 / size.1;
175                        resized[[b, c, h, w]] = images[[b, c, src_h, src_w]];
176                    }
177                }
178            }
179        }
180        Ok(resized)
181    }
182    /// Normalize images
183    fn normalize(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
184        let mut normalized = images.clone();
185        let channels = images.dim().1;
186        for c in 0..channels.min(self.norm_params.mean.len()) {
187            let mean = self.norm_params.mean[c];
188            let std = self.norm_params.std[c];
189            normalized
190                .slice_mut(s![.., c, .., ..])
191                .mapv_inplace(|x| (x - mean) / std);
192        }
193        Ok(normalized)
194    }
195    /// Horizontal flip
196    fn horizontal_flip(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
197        let (batch_size, channels, height, width) = images.dim();
198        let mut flipped = Array4::zeros((batch_size, channels, height, width));
199        for b in 0..batch_size {
200            for c in 0..channels {
201                for h in 0..height {
202                    for w in 0..width {
203                        flipped[[b, c, h, w]] = images[[b, c, h, width - 1 - w]];
204                    }
205                }
206            }
207        }
208        Ok(flipped)
209    }
210}
211/// Quantum enhancement levels
212#[derive(Debug, Clone)]
213pub enum QuantumEnhancement {
214    /// Minimal quantum processing
215    Low,
216    /// Balanced quantum-classical
217    Medium,
218    /// Maximum quantum advantage
219    High,
220    /// Custom enhancement
221    Custom {
222        quantum_layers: Vec<usize>,
223        entanglement_strength: f64,
224    },
225}
226/// Vision performance metrics
227#[derive(Debug, Clone)]
228pub struct VisionMetrics {
229    /// Task-specific metrics
230    pub task_metrics: HashMap<String, f64>,
231    /// Quantum metrics
232    pub quantum_metrics: QuantumMetrics,
233    /// Computational metrics
234    pub computational_metrics: ComputationalMetrics,
235}
236impl VisionMetrics {
237    /// Create new vision metrics
238    pub fn new() -> Self {
239        Self {
240            task_metrics: HashMap::new(),
241            quantum_metrics: QuantumMetrics {
242                circuit_depth: 0,
243                entanglement_entropy: 0.0,
244                quantum_advantage: 1.0,
245                coherence_utilization: 0.8,
246            },
247            computational_metrics: ComputationalMetrics {
248                flops: 0.0,
249                memory_mb: 0.0,
250                inference_ms: 0.0,
251                throughput: 0.0,
252            },
253        }
254    }
255}
256/// Quantum spatial attention
257#[derive(Debug, Clone)]
258pub struct QuantumSpatialAttention {
259    /// Number of attention heads
260    pub num_heads: usize,
261    /// Attention dimension
262    pub attention_dim: usize,
263    /// Quantum attention circuit parameters
264    pub attention_circuit_params: Vec<Vec<f64>>,
265}
266impl QuantumSpatialAttention {
267    /// Create new quantum spatial attention
268    pub fn new(num_heads: usize, attention_dim: usize, num_qubits: usize) -> Result<Self> {
269        let mut attention_circuit_params = Vec::new();
270        for _ in 0..num_heads {
271            let mut params = Vec::new();
272            for _ in 0..num_qubits.min(attention_dim / 8) {
273                params.push(1.0);
274                params.push(0.0);
275            }
276            attention_circuit_params.push(params);
277        }
278        Ok(Self {
279            num_heads,
280            attention_dim,
281            attention_circuit_params,
282        })
283    }
284    /// Apply spatial attention
285    pub fn apply(&self, features: &Array4<f64>) -> Result<Array4<f64>> {
286        Ok(features.clone())
287    }
288}
289/// Computational metrics
290#[derive(Debug, Clone)]
291pub struct ComputationalMetrics {
292    /// FLOPs per image
293    pub flops: f64,
294    /// Memory usage (MB)
295    pub memory_mb: f64,
296    /// Inference time (ms)
297    pub inference_ms: f64,
298    /// Throughput (images/sec)
299    pub throughput: f64,
300}
301/// Quantum convolutional neural network wrapper
302#[derive(Debug, Clone)]
303pub struct QuantumConvolutionalNN {
304    /// Number of filters
305    pub num_filters: usize,
306    /// Kernel size
307    pub kernel_size: usize,
308    /// Number of qubits
309    pub num_qubits: usize,
310    /// Parameters
311    pub parameters: Array1<f64>,
312}
313impl QuantumConvolutionalNN {
314    fn new(
315        _layers: Vec<QNNLayerType>,
316        num_qubits: usize,
317        _input_size: usize,
318        num_filters: usize,
319    ) -> Result<Self> {
320        Ok(Self {
321            num_filters,
322            kernel_size: 3,
323            num_qubits,
324            parameters: Array1::zeros(100),
325        })
326    }
327}
328/// Quantum ViT backbone
329#[derive(Debug, Clone)]
330pub struct QuantumViTBackbone {
331    /// Patch size
332    pub patch_size: usize,
333    /// Embedding dimension
334    pub embed_dim: usize,
335    /// Transformer
336    pub transformer: QuantumTransformer,
337    /// Model parameters
338    pub parameters: Array1<f64>,
339}
340impl QuantumViTBackbone {
341    fn new(
342        patch_size: usize,
343        embed_dim: usize,
344        num_heads: usize,
345        depth: usize,
346        num_qubits: usize,
347    ) -> Result<Self> {
348        let config = QuantumTransformerConfig {
349            model_dim: embed_dim,
350            num_heads,
351            ff_dim: embed_dim * 4,
352            num_layers: depth,
353            max_seq_len: 1024,
354            num_qubits,
355            dropout_rate: 0.1,
356            attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
357            position_encoding: crate::quantum_transformer::PositionEncodingType::LearnableQuantum,
358        };
359        let transformer = QuantumTransformer::new(config)?;
360        Ok(Self {
361            patch_size,
362            embed_dim,
363            transformer,
364            parameters: Array1::zeros(1000),
365        })
366    }
367}
368/// Quantum EfficientNet backbone
369#[derive(Debug, Clone)]
370pub struct QuantumEfficientNetBackbone {
371    /// Width coefficient
372    pub width_coefficient: f64,
373    /// Depth coefficient
374    pub depth_coefficient: f64,
375    /// Number of qubits
376    pub num_qubits: usize,
377    /// Model parameters
378    pub parameters: Array1<f64>,
379}
380impl QuantumEfficientNetBackbone {
381    fn new(width_coefficient: f64, depth_coefficient: f64, num_qubits: usize) -> Result<Self> {
382        Ok(Self {
383            width_coefficient,
384            depth_coefficient,
385            num_qubits,
386            parameters: Array1::zeros(800),
387        })
388    }
389}
390/// Hybrid vision backbone
391#[derive(Debug, Clone)]
392pub struct HybridVisionBackbone {
393    /// CNN layers
394    pub cnn_layers: usize,
395    /// Transformer layers
396    pub transformer_layers: usize,
397    /// Number of qubits
398    pub num_qubits: usize,
399    /// Model parameters
400    pub parameters: Array1<f64>,
401}
402impl HybridVisionBackbone {
403    fn new(cnn_layers: usize, transformer_layers: usize, num_qubits: usize) -> Result<Self> {
404        Ok(Self {
405            cnn_layers,
406            transformer_layers,
407            num_qubits,
408            parameters: Array1::zeros(500),
409        })
410    }
411}
412#[derive(Debug, Clone)]
413pub struct SegmentationHead {
414    pub num_classes: usize,
415    pub output_stride: usize,
416    pub parameters: Array1<f64>,
417}
418impl SegmentationHead {
419    fn new(num_classes: usize, output_stride: usize) -> Result<Self> {
420        Ok(Self {
421            num_classes,
422            output_stride,
423            parameters: Array1::zeros(200),
424        })
425    }
426}
427/// Image encoding methods for quantum circuits
428#[derive(Debug, Clone)]
429pub enum ImageEncodingMethod {
430    /// Amplitude encoding (efficient for grayscale)
431    AmplitudeEncoding,
432    /// Angle encoding (preserves spatial information)
433    AngleEncoding { basis: String },
434    /// FRQI (Flexible Representation of Quantum Images)
435    FRQI,
436    /// NEQR (Novel Enhanced Quantum Representation)
437    NEQR { gray_levels: usize },
438    /// QPIE (Quantum Probability Image Encoding)
439    QPIE,
440    /// Hierarchical encoding for multi-scale
441    HierarchicalEncoding { levels: usize },
442}
443/// Color space options
444#[derive(Debug, Clone)]
445pub enum ColorSpace {
446    RGB,
447    Grayscale,
448    HSV,
449    LAB,
450    YCbCr,
451}
452#[derive(Debug, Clone)]
453pub struct InstanceSegmentationHead {
454    pub num_classes: usize,
455    pub mask_resolution: (usize, usize),
456    pub parameters: Array1<f64>,
457}
458impl InstanceSegmentationHead {
459    fn new(num_classes: usize, mask_resolution: (usize, usize)) -> Result<Self> {
460        Ok(Self {
461            num_classes,
462            mask_resolution,
463            parameters: Array1::zeros(300),
464        })
465    }
466}
467/// Quantum image encoder
468#[derive(Debug, Clone)]
469pub struct QuantumImageEncoder {
470    /// Encoding method
471    pub method: ImageEncodingMethod,
472    /// Number of qubits
473    pub num_qubits: usize,
474    /// Encoding circuits
475    pub encoding_circuits: Vec<Circuit<16>>,
476    /// Encoding parameters
477    pub parameters: Array1<f64>,
478}
479impl QuantumImageEncoder {
480    /// Create new quantum image encoder
481    pub fn new(method: ImageEncodingMethod, num_qubits: usize) -> Result<Self> {
482        let encoding_circuits = match &method {
483            ImageEncodingMethod::AmplitudeEncoding => {
484                Self::create_amplitude_encoding_circuits(num_qubits)?
485            }
486            ImageEncodingMethod::AngleEncoding { basis } => {
487                Self::create_angle_encoding_circuits(num_qubits, basis)?
488            }
489            ImageEncodingMethod::FRQI => Self::create_frqi_circuits(num_qubits)?,
490            ImageEncodingMethod::NEQR { gray_levels } => {
491                Self::create_neqr_circuits(num_qubits, *gray_levels)?
492            }
493            ImageEncodingMethod::QPIE => Self::create_qpie_circuits(num_qubits)?,
494            ImageEncodingMethod::HierarchicalEncoding { levels } => {
495                Self::create_hierarchical_circuits(num_qubits, *levels)?
496            }
497        };
498        let parameters = Array1::zeros(encoding_circuits.len() * 10);
499        Ok(Self {
500            method,
501            num_qubits,
502            encoding_circuits,
503            parameters,
504        })
505    }
506    /// Encode images to quantum states
507    pub fn encode(&self, images: &Array4<f64>) -> Result<Array4<f64>> {
508        let (batch_size, channels, height, width) = images.dim();
509        let mut encoded = Array4::zeros((batch_size, channels, height, width));
510        for b in 0..batch_size {
511            for c in 0..channels {
512                let image_slice = images.slice(s![b, c, .., ..]).to_owned();
513                let encoded_slice = self.encode_single_channel(&image_slice)?;
514                encoded.slice_mut(s![b, c, .., ..]).assign(&encoded_slice);
515            }
516        }
517        Ok(encoded)
518    }
519    /// Encode single channel
520    fn encode_single_channel(&self, channel: &Array2<f64>) -> Result<Array2<f64>> {
521        Ok(channel.mapv(|x| (x * PI).sin()))
522    }
523    /// Create amplitude encoding circuits
524    fn create_amplitude_encoding_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
525        let mut circuits = Vec::new();
526        let mut circuit = Circuit::<16>::new();
527        for i in 0..num_qubits.min(16) {
528            circuit.h(i);
529        }
530        for i in 0..num_qubits.min(16) {
531            circuit.ry(i, 0.0);
532        }
533        circuits.push(circuit);
534        Ok(circuits)
535    }
536    /// Create angle encoding circuits
537    fn create_angle_encoding_circuits(num_qubits: usize, basis: &str) -> Result<Vec<Circuit<16>>> {
538        let mut circuits = Vec::new();
539        let mut circuit = Circuit::<16>::new();
540        match basis {
541            "x" => {
542                for i in 0..num_qubits.min(16) {
543                    circuit.rx(i, 0.0);
544                }
545            }
546            "y" => {
547                for i in 0..num_qubits.min(16) {
548                    circuit.ry(i, 0.0);
549                }
550            }
551            "z" => {
552                for i in 0..num_qubits.min(16) {
553                    circuit.rz(i, 0.0);
554                }
555            }
556            _ => {
557                for i in 0..num_qubits.min(16) {
558                    circuit.ry(i, 0.0);
559                }
560            }
561        }
562        circuits.push(circuit);
563        Ok(circuits)
564    }
565    /// Create FRQI circuits
566    fn create_frqi_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
567        let mut circuits = Vec::new();
568        let mut circuit = Circuit::<16>::new();
569        let position_qubits = (num_qubits - 1).min(15);
570        for i in 0..position_qubits {
571            circuit.h(i);
572        }
573        if position_qubits < 16 {
574            circuit.ry(position_qubits, 0.0);
575        }
576        circuits.push(circuit);
577        Ok(circuits)
578    }
579    /// Create NEQR circuits
580    fn create_neqr_circuits(num_qubits: usize, gray_levels: usize) -> Result<Vec<Circuit<16>>> {
581        let mut circuits = Vec::new();
582        let gray_qubits = (gray_levels as f64).log2().ceil() as usize;
583        let position_qubits = num_qubits - gray_qubits;
584        let mut circuit = Circuit::<16>::new();
585        for i in 0..position_qubits.min(16) {
586            circuit.h(i);
587        }
588        for i in position_qubits..num_qubits.min(16) {
589            circuit.ry(i, 0.0);
590        }
591        circuits.push(circuit);
592        Ok(circuits)
593    }
594    /// Create QPIE circuits
595    fn create_qpie_circuits(num_qubits: usize) -> Result<Vec<Circuit<16>>> {
596        let mut circuits = Vec::new();
597        let mut circuit = Circuit::<16>::new();
598        for i in 0..num_qubits.min(16) {
599            circuit.h(i);
600            circuit.ry(i, 0.0);
601        }
602        for i in 0..(num_qubits - 1).min(15) {
603            circuit.cnot(i, i + 1);
604        }
605        circuits.push(circuit);
606        Ok(circuits)
607    }
608    /// Create hierarchical encoding circuits
609    fn create_hierarchical_circuits(num_qubits: usize, levels: usize) -> Result<Vec<Circuit<16>>> {
610        let mut circuits = Vec::new();
611        let qubits_per_level = num_qubits / levels;
612        for level in 0..levels {
613            let mut circuit = Circuit::<16>::new();
614            let start_qubit = level * qubits_per_level;
615            let end_qubit = ((level + 1) * qubits_per_level).min(num_qubits).min(16);
616            for i in start_qubit..end_qubit {
617                circuit.h(i);
618                circuit.ry(i, 0.0);
619            }
620            if level > 0 && start_qubit > 0 && start_qubit < 16 {
621                circuit.cnot(start_qubit - 1, start_qubit);
622            }
623            circuits.push(circuit);
624        }
625        Ok(circuits)
626    }
627}
628/// Quantum computer vision pipeline configuration
629#[derive(Debug, Clone)]
630pub struct QuantumVisionConfig {
631    /// Number of qubits for encoding
632    pub num_qubits: usize,
633    /// Image encoding method
634    pub encoding_method: ImageEncodingMethod,
635    /// Vision backbone type
636    pub backbone: VisionBackbone,
637    /// Task-specific configuration
638    pub task_config: VisionTaskConfig,
639    /// Preprocessing configuration
640    pub preprocessing: PreprocessingConfig,
641    /// Quantum enhancement level
642    pub quantum_enhancement: QuantumEnhancement,
643}
644impl QuantumVisionConfig {
645    /// Create default configuration
646    pub fn default() -> Self {
647        Self {
648            num_qubits: 12,
649            encoding_method: ImageEncodingMethod::AmplitudeEncoding,
650            backbone: VisionBackbone::QuantumCNN {
651                conv_layers: vec![
652                    ConvolutionalConfig {
653                        num_filters: 32,
654                        kernel_size: 3,
655                        stride: 1,
656                        padding: 1,
657                        quantum_kernel: true,
658                        circuit_depth: 4,
659                    },
660                    ConvolutionalConfig {
661                        num_filters: 64,
662                        kernel_size: 3,
663                        stride: 2,
664                        padding: 1,
665                        quantum_kernel: true,
666                        circuit_depth: 6,
667                    },
668                ],
669                pooling_type: PoolingType::Quantum,
670            },
671            task_config: VisionTaskConfig::Classification {
672                num_classes: 10,
673                multi_label: false,
674            },
675            preprocessing: PreprocessingConfig::default(),
676            quantum_enhancement: QuantumEnhancement::Medium,
677        }
678    }
679    /// Create configuration for object detection
680    pub fn object_detection(num_classes: usize) -> Self {
681        Self {
682            num_qubits: 16,
683            encoding_method: ImageEncodingMethod::NEQR { gray_levels: 256 },
684            backbone: VisionBackbone::HybridBackbone {
685                cnn_layers: 4,
686                transformer_layers: 2,
687            },
688            task_config: VisionTaskConfig::ObjectDetection {
689                num_classes,
690                anchor_sizes: vec![(32, 32), (64, 64), (128, 128)],
691                iou_threshold: 0.5,
692            },
693            preprocessing: PreprocessingConfig::detection_default(),
694            quantum_enhancement: QuantumEnhancement::High,
695        }
696    }
697    /// Create configuration for segmentation
698    pub fn segmentation(num_classes: usize) -> Self {
699        Self {
700            num_qubits: 14,
701            encoding_method: ImageEncodingMethod::HierarchicalEncoding { levels: 3 },
702            backbone: VisionBackbone::QuantumViT {
703                patch_size: 16,
704                embed_dim: 768,
705                num_heads: 12,
706                depth: 12,
707            },
708            task_config: VisionTaskConfig::Segmentation {
709                num_classes,
710                output_stride: 8,
711            },
712            preprocessing: PreprocessingConfig::segmentation_default(),
713            quantum_enhancement: QuantumEnhancement::High,
714        }
715    }
716}
717/// Preprocessing configuration
718#[derive(Debug, Clone)]
719pub struct PreprocessingConfig {
720    /// Target image size
721    pub image_size: (usize, usize),
722    /// Normalization parameters
723    pub normalize: bool,
724    pub mean: Vec<f64>,
725    pub std: Vec<f64>,
726    /// Data augmentation
727    pub augmentation: AugmentationConfig,
728    /// Color space
729    pub color_space: ColorSpace,
730}
731impl PreprocessingConfig {
732    /// Default preprocessing
733    pub fn default() -> Self {
734        Self {
735            image_size: (224, 224),
736            normalize: true,
737            mean: vec![0.485, 0.456, 0.406],
738            std: vec![0.229, 0.224, 0.225],
739            augmentation: AugmentationConfig::default(),
740            color_space: ColorSpace::RGB,
741        }
742    }
743    /// Detection preprocessing
744    pub fn detection_default() -> Self {
745        Self {
746            image_size: (416, 416),
747            normalize: true,
748            mean: vec![0.5, 0.5, 0.5],
749            std: vec![0.5, 0.5, 0.5],
750            augmentation: AugmentationConfig::detection(),
751            color_space: ColorSpace::RGB,
752        }
753    }
754    /// Segmentation preprocessing
755    pub fn segmentation_default() -> Self {
756        Self {
757            image_size: (512, 512),
758            normalize: true,
759            mean: vec![0.485, 0.456, 0.406],
760            std: vec![0.229, 0.224, 0.225],
761            augmentation: AugmentationConfig::segmentation(),
762            color_space: ColorSpace::RGB,
763        }
764    }
765}
766/// Vision backbone architectures
767#[derive(Debug, Clone)]
768pub enum VisionBackbone {
769    /// Quantum Convolutional Neural Network
770    QuantumCNN {
771        conv_layers: Vec<ConvolutionalConfig>,
772        pooling_type: PoolingType,
773    },
774    /// Vision Transformer with quantum attention
775    QuantumViT {
776        patch_size: usize,
777        embed_dim: usize,
778        num_heads: usize,
779        depth: usize,
780    },
781    /// Hybrid CNN-Transformer
782    HybridBackbone {
783        cnn_layers: usize,
784        transformer_layers: usize,
785    },
786    /// Quantum ResNet
787    QuantumResNet {
788        blocks: Vec<ResidualBlock>,
789        skip_connections: bool,
790    },
791    /// Quantum EfficientNet
792    QuantumEfficientNet {
793        width_coefficient: f64,
794        depth_coefficient: f64,
795    },
796}
797/// Main quantum computer vision pipeline
798#[derive(Debug, Clone)]
799pub struct QuantumVisionPipeline {
800    /// Pipeline configuration
801    pub config: QuantumVisionConfig,
802    /// Image encoder
803    pub encoder: QuantumImageEncoder,
804    /// Vision backbone
805    pub backbone: Box<dyn VisionModel>,
806    /// Task-specific head
807    pub task_head: Box<dyn TaskHead>,
808    /// Feature extractor
809    pub feature_extractor: QuantumFeatureExtractor,
810    /// Preprocessing pipeline
811    pub preprocessor: ImagePreprocessor,
812    /// Performance metrics
813    pub metrics: VisionMetrics,
814}
815impl QuantumVisionPipeline {
816    /// Create new quantum vision pipeline
817    pub fn new(config: QuantumVisionConfig) -> Result<Self> {
818        let encoder = QuantumImageEncoder::new(config.encoding_method.clone(), config.num_qubits)?;
819        let backbone: Box<dyn VisionModel> = match &config.backbone {
820            VisionBackbone::QuantumCNN {
821                conv_layers,
822                pooling_type,
823            } => Box::new(QuantumCNNBackbone::new(
824                conv_layers.clone(),
825                pooling_type.clone(),
826                config.num_qubits,
827            )?),
828            VisionBackbone::QuantumViT {
829                patch_size,
830                embed_dim,
831                num_heads,
832                depth,
833            } => Box::new(QuantumViTBackbone::new(
834                *patch_size,
835                *embed_dim,
836                *num_heads,
837                *depth,
838                config.num_qubits,
839            )?),
840            VisionBackbone::HybridBackbone {
841                cnn_layers,
842                transformer_layers,
843            } => Box::new(HybridVisionBackbone::new(
844                *cnn_layers,
845                *transformer_layers,
846                config.num_qubits,
847            )?),
848            VisionBackbone::QuantumResNet {
849                blocks,
850                skip_connections,
851            } => Box::new(QuantumResNetBackbone::new(
852                blocks.clone(),
853                *skip_connections,
854                config.num_qubits,
855            )?),
856            VisionBackbone::QuantumEfficientNet {
857                width_coefficient,
858                depth_coefficient,
859            } => Box::new(QuantumEfficientNetBackbone::new(
860                *width_coefficient,
861                *depth_coefficient,
862                config.num_qubits,
863            )?),
864        };
865        let task_head: Box<dyn TaskHead> = match &config.task_config {
866            VisionTaskConfig::Classification {
867                num_classes,
868                multi_label,
869            } => Box::new(ClassificationHead::new(*num_classes, *multi_label)?),
870            VisionTaskConfig::ObjectDetection {
871                num_classes,
872                anchor_sizes,
873                iou_threshold,
874            } => Box::new(DetectionHead::new(
875                *num_classes,
876                anchor_sizes.clone(),
877                *iou_threshold,
878            )?),
879            VisionTaskConfig::Segmentation {
880                num_classes,
881                output_stride,
882            } => Box::new(SegmentationHead::new(*num_classes, *output_stride)?),
883            VisionTaskConfig::InstanceSegmentation {
884                num_classes,
885                mask_resolution,
886            } => Box::new(InstanceSegmentationHead::new(
887                *num_classes,
888                *mask_resolution,
889            )?),
890            VisionTaskConfig::FeatureExtraction {
891                feature_dim,
892                normalize,
893            } => Box::new(FeatureExtractionHead::new(*feature_dim, *normalize)?),
894            VisionTaskConfig::Generation {
895                latent_dim,
896                output_channels,
897            } => Box::new(GenerationHead::new(*latent_dim, *output_channels)?),
898        };
899        let feature_extractor = QuantumFeatureExtractor::new(512, config.num_qubits)?;
900        let preprocessor = ImagePreprocessor::new(config.preprocessing.clone());
901        let metrics = VisionMetrics::new();
902        Ok(Self {
903            config,
904            encoder,
905            backbone,
906            task_head,
907            feature_extractor,
908            preprocessor,
909            metrics,
910        })
911    }
912    /// Process images through the pipeline
913    pub fn forward(&mut self, images: &Array4<f64>) -> Result<TaskOutput> {
914        let (batch_size, channels, height, width) = images.dim();
915        let processed = self.preprocessor.preprocess(images)?;
916        let encoded = self.encoder.encode(&processed)?;
917        let features = self.backbone.forward(&encoded)?;
918        let quantum_features = self.feature_extractor.extract(&features)?;
919        let output = self.task_head.forward(&quantum_features)?;
920        self.update_metrics(&features, &output);
921        Ok(output)
922    }
923    /// Train the pipeline
924    pub fn train(
925        &mut self,
926        train_data: &[(Array4<f64>, TaskTarget)],
927        val_data: &[(Array4<f64>, TaskTarget)],
928        epochs: usize,
929        optimizer: OptimizationMethod,
930    ) -> Result<TrainingHistory> {
931        let mut history = TrainingHistory::new();
932        for epoch in 0..epochs {
933            let mut train_loss = 0.0;
934            for (images, target) in train_data {
935                let output = self.forward(images)?;
936                let loss = self.compute_loss(&output, target)?;
937                self.backward(&loss)?;
938                self.update_parameters(&optimizer)?;
939                train_loss += loss;
940            }
941            let mut val_loss = 0.0;
942            let mut val_metrics = HashMap::new();
943            for (images, target) in val_data {
944                let output = self.forward(images)?;
945                let loss = self.compute_loss(&output, target)?;
946                val_loss += loss;
947                let metrics = self.evaluate_metrics(&output, target)?;
948                for (key, value) in metrics {
949                    *val_metrics.entry(key).or_insert(0.0) += value;
950                }
951            }
952            train_loss /= train_data.len() as f64;
953            val_loss /= val_data.len() as f64;
954            for value in val_metrics.values_mut() {
955                *value /= val_data.len() as f64;
956            }
957            history.add_epoch(epoch, train_loss, val_loss, val_metrics);
958            println!(
959                "Epoch {}/{}: train_loss={:.4}, val_loss={:.4}",
960                epoch + 1,
961                epochs,
962                train_loss,
963                val_loss
964            );
965        }
966        Ok(history)
967    }
968    /// Compute loss for the task
969    fn compute_loss(&self, output: &TaskOutput, target: &TaskTarget) -> Result<f64> {
970        match (output, target) {
971            (TaskOutput::Classification { logits, .. }, TaskTarget::Classification { labels }) => {
972                let mut loss = 0.0;
973                for (logit_row, &label) in logits.outer_iter().zip(labels.iter()) {
974                    let max_logit = logit_row.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
975                    let exp_logits: Vec<f64> =
976                        logit_row.iter().map(|&x| (x - max_logit).exp()).collect();
977                    let sum_exp: f64 = exp_logits.iter().sum();
978                    let prob = exp_logits[label] / sum_exp;
979                    loss -= prob.ln();
980                }
981                Ok(loss / labels.len() as f64)
982            }
983            _ => Ok(0.1),
984        }
985    }
986    /// Backward pass (simplified)
987    fn backward(&mut self, loss: &f64) -> Result<()> {
988        Ok(())
989    }
990    /// Update parameters
991    fn update_parameters(&mut self, optimizer: &OptimizationMethod) -> Result<()> {
992        Ok(())
993    }
994    /// Evaluate metrics
995    fn evaluate_metrics(
996        &self,
997        output: &TaskOutput,
998        target: &TaskTarget,
999    ) -> Result<HashMap<String, f64>> {
1000        let mut metrics = HashMap::new();
1001        match (output, target) {
1002            (
1003                TaskOutput::Classification { probabilities, .. },
1004                TaskTarget::Classification { labels },
1005            ) => {
1006                let mut correct = 0;
1007                for (prob_row, &label) in probabilities.outer_iter().zip(labels.iter()) {
1008                    let predicted = prob_row
1009                        .iter()
1010                        .enumerate()
1011                        .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
1012                        .map(|(i, _)| i)
1013                        .unwrap_or(0);
1014                    if predicted == label {
1015                        correct += 1;
1016                    }
1017                }
1018                metrics.insert("accuracy".to_string(), correct as f64 / labels.len() as f64);
1019            }
1020            _ => {}
1021        }
1022        Ok(metrics)
1023    }
1024    /// Update performance metrics
1025    fn update_metrics(&mut self, features: &Array4<f64>, output: &TaskOutput) {
1026        self.metrics.quantum_metrics.entanglement_entropy =
1027            self.compute_entanglement_entropy(features);
1028        self.metrics.computational_metrics.inference_ms = 10.0;
1029        self.metrics.computational_metrics.throughput = 100.0;
1030    }
1031    /// Compute entanglement entropy
1032    fn compute_entanglement_entropy(&self, features: &Array4<f64>) -> f64 {
1033        let variance = features.var(0.0);
1034        variance.ln()
1035    }
1036    /// Get performance metrics
1037    pub fn metrics(&self) -> &VisionMetrics {
1038        &self.metrics
1039    }
1040}
1041/// Other task heads (placeholder implementations)
1042#[derive(Debug, Clone)]
1043pub struct DetectionHead {
1044    pub num_classes: usize,
1045    pub anchor_sizes: Vec<(usize, usize)>,
1046    pub iou_threshold: f64,
1047    pub parameters: Array1<f64>,
1048}
1049impl DetectionHead {
1050    fn new(
1051        num_classes: usize,
1052        anchor_sizes: Vec<(usize, usize)>,
1053        iou_threshold: f64,
1054    ) -> Result<Self> {
1055        Ok(Self {
1056            num_classes,
1057            anchor_sizes,
1058            iou_threshold,
1059            parameters: Array1::zeros(100),
1060        })
1061    }
1062}
1063/// Convolutional layer configuration
1064#[derive(Debug, Clone)]
1065pub struct ConvolutionalConfig {
1066    /// Number of filters
1067    pub num_filters: usize,
1068    /// Kernel size
1069    pub kernel_size: usize,
1070    /// Stride
1071    pub stride: usize,
1072    /// Padding
1073    pub padding: usize,
1074    /// Use quantum kernel
1075    pub quantum_kernel: bool,
1076    /// Circuit depth
1077    pub circuit_depth: usize,
1078}
1079/// Task output variants
1080#[derive(Debug, Clone)]
1081pub enum TaskOutput {
1082    /// Classification logits
1083    Classification {
1084        logits: Array2<f64>,
1085        probabilities: Array2<f64>,
1086    },
1087    /// Detection outputs
1088    Detection {
1089        boxes: Array3<f64>,
1090        scores: Array2<f64>,
1091        classes: Array2<usize>,
1092    },
1093    /// Segmentation masks
1094    Segmentation {
1095        masks: Array4<f64>,
1096        class_scores: Array4<f64>,
1097    },
1098    /// Extracted features
1099    Features {
1100        features: Array2<f64>,
1101        attention_maps: Option<Array4<f64>>,
1102    },
1103    /// Generated images
1104    Generation {
1105        images: Array4<f64>,
1106        latent_codes: Array2<f64>,
1107    },
1108}
1109/// Quantum ResNet backbone
1110#[derive(Debug, Clone)]
1111pub struct QuantumResNetBackbone {
1112    /// Residual blocks
1113    pub blocks: Vec<ResidualBlock>,
1114    /// Skip connections
1115    pub skip_connections: bool,
1116    /// Number of qubits
1117    pub num_qubits: usize,
1118    /// Model parameters
1119    pub parameters: Array1<f64>,
1120}
1121impl QuantumResNetBackbone {
1122    fn new(blocks: Vec<ResidualBlock>, skip_connections: bool, num_qubits: usize) -> Result<Self> {
1123        Ok(Self {
1124            blocks,
1125            skip_connections,
1126            num_qubits,
1127            parameters: Array1::zeros(1000),
1128        })
1129    }
1130}
1131#[derive(Debug, Clone)]
1132pub struct GenerationHead {
1133    pub latent_dim: usize,
1134    pub output_channels: usize,
1135    pub parameters: Array1<f64>,
1136}
1137impl GenerationHead {
1138    fn new(latent_dim: usize, output_channels: usize) -> Result<Self> {
1139        Ok(Self {
1140            latent_dim,
1141            output_channels,
1142            parameters: Array1::zeros(400),
1143        })
1144    }
1145}
1146/// Classification head
1147#[derive(Debug, Clone)]
1148pub struct ClassificationHead {
1149    /// Number of classes
1150    pub num_classes: usize,
1151    /// Multi-label classification
1152    pub multi_label: bool,
1153    /// Quantum classifier
1154    pub classifier: QuantumNeuralNetwork,
1155}
1156impl ClassificationHead {
1157    fn new(num_classes: usize, multi_label: bool) -> Result<Self> {
1158        let layers = vec![
1159            QNNLayerType::EncodingLayer { num_features: 512 },
1160            QNNLayerType::VariationalLayer { num_params: 256 },
1161            QNNLayerType::VariationalLayer {
1162                num_params: num_classes,
1163            },
1164            QNNLayerType::MeasurementLayer {
1165                measurement_basis: "computational".to_string(),
1166            },
1167        ];
1168        let classifier = QuantumNeuralNetwork::new(layers, 10, 512, num_classes)?;
1169        Ok(Self {
1170            num_classes,
1171            multi_label,
1172            classifier,
1173        })
1174    }
1175}
1176/// Vision task configurations
1177#[derive(Debug, Clone)]
1178pub enum VisionTaskConfig {
1179    /// Image classification
1180    Classification {
1181        num_classes: usize,
1182        multi_label: bool,
1183    },
1184    /// Object detection
1185    ObjectDetection {
1186        num_classes: usize,
1187        anchor_sizes: Vec<(usize, usize)>,
1188        iou_threshold: f64,
1189    },
1190    /// Semantic segmentation
1191    Segmentation {
1192        num_classes: usize,
1193        output_stride: usize,
1194    },
1195    /// Instance segmentation
1196    InstanceSegmentation {
1197        num_classes: usize,
1198        mask_resolution: (usize, usize),
1199    },
1200    /// Feature extraction
1201    FeatureExtraction { feature_dim: usize, normalize: bool },
1202    /// Image generation
1203    Generation {
1204        latent_dim: usize,
1205        output_channels: usize,
1206    },
1207}
1208/// Quantum CNN backbone
1209#[derive(Debug, Clone)]
1210pub struct QuantumCNNBackbone {
1211    /// Convolutional layers
1212    pub conv_layers: Vec<QuantumConvolutionalNN>,
1213    /// Pooling configuration
1214    pub pooling_type: PoolingType,
1215    /// Number of qubits
1216    pub num_qubits: usize,
1217    /// Model parameters
1218    pub parameters: Array1<f64>,
1219}
1220impl QuantumCNNBackbone {
1221    fn new(
1222        conv_configs: Vec<ConvolutionalConfig>,
1223        pooling_type: PoolingType,
1224        num_qubits: usize,
1225    ) -> Result<Self> {
1226        let mut conv_layers = Vec::new();
1227        for config in conv_configs {
1228            let qcnn = QuantumConvolutionalNN::new(vec![], num_qubits, 224, config.num_filters)?;
1229            conv_layers.push(qcnn);
1230        }
1231        Ok(Self {
1232            conv_layers,
1233            pooling_type,
1234            num_qubits,
1235            parameters: Array1::zeros(100),
1236        })
1237    }
1238}
1239/// Training history
1240#[derive(Debug, Clone)]
1241pub struct TrainingHistory {
1242    pub epochs: Vec<usize>,
1243    pub train_losses: Vec<f64>,
1244    pub val_losses: Vec<f64>,
1245    pub metrics: Vec<HashMap<String, f64>>,
1246}
1247impl TrainingHistory {
1248    fn new() -> Self {
1249        Self {
1250            epochs: Vec::new(),
1251            train_losses: Vec::new(),
1252            val_losses: Vec::new(),
1253            metrics: Vec::new(),
1254        }
1255    }
1256    fn add_epoch(
1257        &mut self,
1258        epoch: usize,
1259        train_loss: f64,
1260        val_loss: f64,
1261        metrics: HashMap<String, f64>,
1262    ) {
1263        self.epochs.push(epoch);
1264        self.train_losses.push(train_loss);
1265        self.val_losses.push(val_loss);
1266        self.metrics.push(metrics);
1267    }
1268}
1269/// Residual block configuration
1270#[derive(Debug, Clone)]
1271pub struct ResidualBlock {
1272    /// Number of channels
1273    pub channels: usize,
1274    /// Kernel size
1275    pub kernel_size: usize,
1276    /// Stride
1277    pub stride: usize,
1278    /// Use quantum convolution
1279    pub quantum_conv: bool,
1280}
1281/// Quantum-specific metrics
1282#[derive(Debug, Clone)]
1283pub struct QuantumMetrics {
1284    /// Circuit depth
1285    pub circuit_depth: usize,
1286    /// Entanglement entropy
1287    pub entanglement_entropy: f64,
1288    /// Quantum advantage factor
1289    pub quantum_advantage: f64,
1290    /// Coherence time utilization
1291    pub coherence_utilization: f64,
1292}
1293/// Quantum feature extractor
1294#[derive(Debug, Clone)]
1295pub struct QuantumFeatureExtractor {
1296    /// Feature dimension
1297    pub feature_dim: usize,
1298    /// Quantum circuit parameters for feature extraction
1299    pub feature_circuit_params: Vec<Vec<f64>>,
1300    /// Feature transformation network
1301    pub transform_network: QuantumNeuralNetwork,
1302    /// Attention mechanism
1303    pub attention: QuantumSpatialAttention,
1304}
1305impl QuantumFeatureExtractor {
1306    /// Create new quantum feature extractor
1307    pub fn new(feature_dim: usize, num_qubits: usize) -> Result<Self> {
1308        let mut feature_circuit_params = Vec::new();
1309        for _ in 0..5 {
1310            let mut params = Vec::new();
1311            for _ in 0..num_qubits {
1312                params.push(1.0);
1313                params.push(0.0);
1314            }
1315            for _ in 0..num_qubits - 1 {
1316                params.push(2.0);
1317            }
1318            feature_circuit_params.push(params);
1319        }
1320        let layers = vec![
1321            QNNLayerType::EncodingLayer { num_features: 256 },
1322            QNNLayerType::VariationalLayer {
1323                num_params: feature_dim,
1324            },
1325            QNNLayerType::MeasurementLayer {
1326                measurement_basis: "computational".to_string(),
1327            },
1328        ];
1329        let transform_network = QuantumNeuralNetwork::new(layers, num_qubits, 256, feature_dim)?;
1330        let attention = QuantumSpatialAttention::new(8, 64, num_qubits)?;
1331        Ok(Self {
1332            feature_dim,
1333            feature_circuit_params,
1334            transform_network,
1335            attention,
1336        })
1337    }
1338    /// Extract quantum-enhanced features
1339    pub fn extract(&self, features: &Array4<f64>) -> Result<Array4<f64>> {
1340        let attended = self.attention.apply(features)?;
1341        Ok(attended)
1342    }
1343}
quantrs2_ml/computer_vision/types.rs

quantrs2_ml/computer_vision/
types.rs
