quantrs2_ml/
model_zoo.rs

1//! Pre-trained model zoo for QuantRS2-ML
2//!
3//! This module provides a collection of pre-trained quantum machine learning models
4//! that can be easily loaded and used for various tasks.
5
6use crate::enhanced_gan::{ConditionalQGAN, WassersteinQGAN};
7use crate::error::{MLError, Result};
8use crate::keras_api::{
9    ActivationFunction, Dense, LossFunction, MetricType, OptimizerType, QuantumAnsatzType,
10    QuantumDense, Sequential,
11};
12use crate::pytorch_api::{
13    ActivationType as PyTorchActivationType, InitType, QuantumLinear, QuantumModule,
14    QuantumSequential,
15};
16use crate::qnn::{QNNLayer, QuantumNeuralNetwork};
17use crate::qsvm::{FeatureMapType, QSVMParams, QSVM};
18use crate::transfer::{PretrainedModel, QuantumTransferLearning, TransferStrategy};
19use crate::vae::{ClassicalAutoencoder, QVAE};
20use ndarray::{s, Array1, Array2, ArrayD};
21use quantrs2_circuit::prelude::*;
22use quantrs2_core::prelude::*;
23use serde::{Deserialize, Serialize};
24use std::collections::HashMap;
25use std::path::Path;
26
27/// Model zoo for pre-trained quantum ML models
28pub struct ModelZoo {
29    /// Available models
30    models: HashMap<String, ModelMetadata>,
31    /// Model cache
32    cache: HashMap<String, Box<dyn QuantumModel>>,
33}
34
35/// Metadata for a model in the zoo
36#[derive(Debug, Clone, Serialize, Deserialize)]
37pub struct ModelMetadata {
38    /// Model name
39    pub name: String,
40    /// Model description
41    pub description: String,
42    /// Model category
43    pub category: ModelCategory,
44    /// Input shape
45    pub input_shape: Vec<usize>,
46    /// Output shape
47    pub output_shape: Vec<usize>,
48    /// Number of qubits
49    pub num_qubits: usize,
50    /// Model parameters count
51    pub num_parameters: usize,
52    /// Training dataset
53    pub dataset: String,
54    /// Training accuracy
55    pub accuracy: Option<f64>,
56    /// Model size (bytes)
57    pub size_bytes: usize,
58    /// Creation date
59    pub created_date: String,
60    /// Model version
61    pub version: String,
62    /// Requirements
63    pub requirements: ModelRequirements,
64}
65
66/// Model categories
67#[derive(Debug, Clone, Serialize, Deserialize, Hash, Eq, PartialEq)]
68pub enum ModelCategory {
69    /// Classification models
70    Classification,
71    /// Regression models
72    Regression,
73    /// Generative models
74    Generative,
75    /// Variational algorithms
76    Variational,
77    /// Quantum kernels
78    Kernel,
79    /// Transfer learning
80    Transfer,
81    /// Anomaly detection
82    AnomalyDetection,
83    /// Time series
84    TimeSeries,
85    /// Natural language processing
86    NLP,
87    /// Computer vision
88    Vision,
89    /// Reinforcement learning
90    ReinforcementLearning,
91}
92
93/// Model requirements
94#[derive(Debug, Clone, Serialize, Deserialize)]
95pub struct ModelRequirements {
96    /// Minimum qubits required
97    pub min_qubits: usize,
98    /// Coherence time requirement (microseconds)
99    pub coherence_time: f64,
100    /// Gate fidelity requirement
101    pub gate_fidelity: f64,
102    /// Supported backends
103    pub backends: Vec<String>,
104}
105
106/// Trait for quantum models in the zoo
107pub trait QuantumModel: Send + Sync {
108    /// Model name
109    fn name(&self) -> &str;
110
111    /// Make prediction
112    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>>;
113
114    /// Get model metadata
115    fn metadata(&self) -> &ModelMetadata;
116
117    /// Save model to file
118    fn save(&self, path: &str) -> Result<()>;
119
120    /// Load model from file
121    fn load(path: &str) -> Result<Box<dyn QuantumModel>>
122    where
123        Self: Sized;
124
125    /// Get model architecture description
126    fn architecture(&self) -> String;
127
128    /// Get training configuration
129    fn training_config(&self) -> TrainingConfig;
130}
131
132/// Training configuration used for pre-trained models
133#[derive(Debug, Clone, Serialize, Deserialize)]
134pub struct TrainingConfig {
135    /// Loss function used
136    pub loss_function: String,
137    /// Optimizer used
138    pub optimizer: String,
139    /// Learning rate
140    pub learning_rate: f64,
141    /// Number of epochs
142    pub epochs: usize,
143    /// Batch size
144    pub batch_size: usize,
145    /// Validation split
146    pub validation_split: f64,
147}
148
149impl ModelZoo {
150    /// Create new model zoo
151    pub fn new() -> Self {
152        let mut zoo = Self {
153            models: HashMap::new(),
154            cache: HashMap::new(),
155        };
156
157        // Register built-in models
158        zoo.register_builtin_models();
159        zoo
160    }
161
162    /// Register built-in pre-trained models
163    fn register_builtin_models(&mut self) {
164        // MNIST Quantum Classifier
165        self.models.insert(
166            "mnist_qnn".to_string(),
167            ModelMetadata {
168                name: "MNIST Quantum Neural Network".to_string(),
169                description: "Pre-trained quantum neural network for MNIST digit classification"
170                    .to_string(),
171                category: ModelCategory::Classification,
172                input_shape: vec![784],
173                output_shape: vec![10],
174                num_qubits: 8,
175                num_parameters: 32,
176                dataset: "MNIST".to_string(),
177                accuracy: Some(0.92),
178                size_bytes: 1024,
179                created_date: "2024-01-15".to_string(),
180                version: "1.0".to_string(),
181                requirements: ModelRequirements {
182                    min_qubits: 8,
183                    coherence_time: 100.0,
184                    gate_fidelity: 0.99,
185                    backends: vec!["statevector".to_string(), "qasm".to_string()],
186                },
187            },
188        );
189
190        // Iris Quantum SVM
191        self.models.insert(
192            "iris_qsvm".to_string(),
193            ModelMetadata {
194                name: "Iris Quantum SVM".to_string(),
195                description: "Pre-trained quantum SVM for Iris flower classification".to_string(),
196                category: ModelCategory::Classification,
197                input_shape: vec![4],
198                output_shape: vec![3],
199                num_qubits: 4,
200                num_parameters: 16,
201                dataset: "Iris".to_string(),
202                accuracy: Some(0.97),
203                size_bytes: 512,
204                created_date: "2024-01-20".to_string(),
205                version: "1.0".to_string(),
206                requirements: ModelRequirements {
207                    min_qubits: 4,
208                    coherence_time: 50.0,
209                    gate_fidelity: 0.995,
210                    backends: vec!["statevector".to_string()],
211                },
212            },
213        );
214
215        // VQE H2 Molecule
216        self.models.insert(
217            "h2_vqe".to_string(),
218            ModelMetadata {
219                name: "H2 Molecule VQE".to_string(),
220                description: "Pre-trained VQE for hydrogen molecule ground state".to_string(),
221                category: ModelCategory::Variational,
222                input_shape: vec![1],  // Bond length
223                output_shape: vec![1], // Energy
224                num_qubits: 4,
225                num_parameters: 8,
226                dataset: "H2 PES".to_string(),
227                accuracy: Some(0.999), // Chemical accuracy
228                size_bytes: 256,
229                created_date: "2024-01-25".to_string(),
230                version: "1.0".to_string(),
231                requirements: ModelRequirements {
232                    min_qubits: 4,
233                    coherence_time: 200.0,
234                    gate_fidelity: 0.999,
235                    backends: vec!["statevector".to_string()],
236                },
237            },
238        );
239
240        // Financial QAOA
241        self.models.insert(
242            "portfolio_qaoa".to_string(),
243            ModelMetadata {
244                name: "Portfolio Optimization QAOA".to_string(),
245                description: "Pre-trained QAOA for portfolio optimization problems".to_string(),
246                category: ModelCategory::Variational,
247                input_shape: vec![100], // Asset returns
248                output_shape: vec![10], // Portfolio weights
249                num_qubits: 10,
250                num_parameters: 20,
251                dataset: "S&P 500".to_string(),
252                accuracy: None,
253                size_bytes: 2048,
254                created_date: "2024-02-01".to_string(),
255                version: "1.0".to_string(),
256                requirements: ModelRequirements {
257                    min_qubits: 10,
258                    coherence_time: 150.0,
259                    gate_fidelity: 0.98,
260                    backends: vec!["statevector".to_string(), "aer".to_string()],
261                },
262            },
263        );
264
265        // Quantum Autoencoder
266        self.models.insert(
267            "qae_anomaly".to_string(),
268            ModelMetadata {
269                name: "Quantum Autoencoder for Anomaly Detection".to_string(),
270                description: "Pre-trained quantum autoencoder for detecting anomalies in data"
271                    .to_string(),
272                category: ModelCategory::AnomalyDetection,
273                input_shape: vec![16],
274                output_shape: vec![16],
275                num_qubits: 6,
276                num_parameters: 24,
277                dataset: "Credit Card Fraud".to_string(),
278                accuracy: Some(0.94),
279                size_bytes: 1536,
280                created_date: "2024-02-05".to_string(),
281                version: "1.0".to_string(),
282                requirements: ModelRequirements {
283                    min_qubits: 6,
284                    coherence_time: 120.0,
285                    gate_fidelity: 0.995,
286                    backends: vec!["statevector".to_string()],
287                },
288            },
289        );
290
291        // Quantum Time Series Forecaster
292        self.models.insert(
293            "qts_forecaster".to_string(),
294            ModelMetadata {
295                name: "Quantum Time Series Forecaster".to_string(),
296                description: "Pre-trained quantum model for time series forecasting".to_string(),
297                category: ModelCategory::TimeSeries,
298                input_shape: vec![20], // Window size
299                output_shape: vec![1], // Next value
300                num_qubits: 8,
301                num_parameters: 40,
302                dataset: "Stock Prices".to_string(),
303                accuracy: Some(0.89),
304                size_bytes: 2560,
305                created_date: "2024-02-10".to_string(),
306                version: "1.0".to_string(),
307                requirements: ModelRequirements {
308                    min_qubits: 8,
309                    coherence_time: 100.0,
310                    gate_fidelity: 0.99,
311                    backends: vec!["statevector".to_string(), "mps".to_string()],
312                },
313            },
314        );
315    }
316
317    /// List available models
318    pub fn list_models(&self) -> Vec<&ModelMetadata> {
319        self.models.values().collect()
320    }
321
322    /// List models by category
323    pub fn list_by_category(&self, category: &ModelCategory) -> Vec<&ModelMetadata> {
324        self.models
325            .values()
326            .filter(|meta| {
327                std::mem::discriminant(&meta.category) == std::mem::discriminant(category)
328            })
329            .collect()
330    }
331
332    /// Search models by name or description
333    pub fn search(&self, query: &str) -> Vec<&ModelMetadata> {
334        let query_lower = query.to_lowercase();
335        self.models
336            .values()
337            .filter(|meta| {
338                meta.name.to_lowercase().contains(&query_lower)
339                    || meta.description.to_lowercase().contains(&query_lower)
340            })
341            .collect()
342    }
343
344    /// Get model metadata
345    pub fn get_metadata(&self, name: &str) -> Option<&ModelMetadata> {
346        self.models.get(name)
347    }
348
349    /// Load a model from the zoo
350    pub fn load_model(&mut self, name: &str) -> Result<&dyn QuantumModel> {
351        if !self.cache.contains_key(name) {
352            let model = self.create_model(name)?;
353            self.cache.insert(name.to_string(), model);
354        }
355
356        Ok(self.cache.get(name).unwrap().as_ref())
357    }
358
359    /// Create a model instance
360    fn create_model(&self, name: &str) -> Result<Box<dyn QuantumModel>> {
361        match name {
362            "mnist_qnn" => Ok(Box::new(MNISTQuantumNN::new()?)),
363            "iris_qsvm" => Ok(Box::new(IrisQuantumSVM::new()?)),
364            "h2_vqe" => Ok(Box::new(H2VQE::new()?)),
365            "portfolio_qaoa" => Ok(Box::new(PortfolioQAOA::new()?)),
366            "qae_anomaly" => Ok(Box::new(QuantumAnomalyDetector::new()?)),
367            "qts_forecaster" => Ok(Box::new(QuantumTimeSeriesForecaster::new()?)),
368            _ => Err(MLError::InvalidConfiguration(format!(
369                "Unknown model: {}",
370                name
371            ))),
372        }
373    }
374
375    /// Register a new model
376    pub fn register_model(&mut self, name: String, metadata: ModelMetadata) {
377        self.models.insert(name, metadata);
378    }
379
380    /// Download a model from remote repository (placeholder)
381    pub fn download_model(&mut self, name: &str, url: &str) -> Result<()> {
382        // Placeholder for downloading models from remote repositories
383        println!("Downloading model {} from {}", name, url);
384        Ok(())
385    }
386
387    /// Get model recommendations based on task
388    pub fn recommend_models(
389        &self,
390        task_description: &str,
391        num_qubits: Option<usize>,
392    ) -> Vec<&ModelMetadata> {
393        let task_lower = task_description.to_lowercase();
394        let mut recommendations: Vec<_> = self
395            .models
396            .values()
397            .filter(|meta| {
398                // Filter by qubit requirements
399                if let Some(qubits) = num_qubits {
400                    if meta.requirements.min_qubits > qubits {
401                        return false;
402                    }
403                }
404
405                // Match task keywords
406                task_lower.contains("classification")
407                    && matches!(meta.category, ModelCategory::Classification)
408                    || task_lower.contains("regression")
409                        && matches!(meta.category, ModelCategory::Regression)
410                    || task_lower.contains("generation")
411                        && matches!(meta.category, ModelCategory::Generative)
412                    || task_lower.contains("anomaly")
413                        && matches!(meta.category, ModelCategory::AnomalyDetection)
414                    || task_lower.contains("time series")
415                        && matches!(meta.category, ModelCategory::TimeSeries)
416                    || task_lower.contains("nlp") && matches!(meta.category, ModelCategory::NLP)
417                    || task_lower.contains("vision")
418                        && matches!(meta.category, ModelCategory::Vision)
419            })
420            .collect();
421
422        // Sort by accuracy (if available)
423        recommendations.sort_by(|a, b| match (a.accuracy, b.accuracy) {
424            (Some(acc_a), Some(acc_b)) => acc_b.partial_cmp(&acc_a).unwrap(),
425            (Some(_), None) => std::cmp::Ordering::Less,
426            (None, Some(_)) => std::cmp::Ordering::Greater,
427            (None, None) => std::cmp::Ordering::Equal,
428        });
429
430        recommendations
431    }
432
433    /// Export model zoo catalog
434    pub fn export_catalog(&self, path: &str) -> Result<()> {
435        let catalog: Vec<_> = self.models.values().collect();
436        let json = serde_json::to_string_pretty(&catalog)?;
437        std::fs::write(path, json)?;
438        Ok(())
439    }
440
441    /// Import model zoo catalog
442    pub fn import_catalog(&mut self, path: &str) -> Result<()> {
443        let json = std::fs::read_to_string(path)?;
444        let catalog: Vec<ModelMetadata> = serde_json::from_str(&json)?;
445
446        for metadata in catalog {
447            self.models.insert(metadata.name.clone(), metadata);
448        }
449
450        Ok(())
451    }
452}
453
454// Concrete model implementations for the zoo
455
456/// MNIST Quantum Neural Network
457pub struct MNISTQuantumNN {
458    model: Sequential,
459    metadata: ModelMetadata,
460}
461
462impl MNISTQuantumNN {
463    pub fn new() -> Result<Self> {
464        let mut model = Sequential::new().name("mnist_qnn");
465
466        // Add quantum dense layer
467        model.add(Box::new(
468            QuantumDense::new(8, 64)
469                .ansatz_type(QuantumAnsatzType::HardwareEfficient)
470                .num_layers(2)
471                .name("quantum_layer"),
472        ));
473
474        // Add classical output layer
475        model.add(Box::new(
476            Dense::new(10)
477                .activation(ActivationFunction::Softmax)
478                .name("output_layer"),
479        ));
480
481        model.build(vec![784])?;
482
483        let metadata = ModelMetadata {
484            name: "MNIST Quantum Neural Network".to_string(),
485            description: "Pre-trained quantum neural network for MNIST digit classification"
486                .to_string(),
487            category: ModelCategory::Classification,
488            input_shape: vec![784],
489            output_shape: vec![10],
490            num_qubits: 8,
491            num_parameters: 32,
492            dataset: "MNIST".to_string(),
493            accuracy: Some(0.92),
494            size_bytes: 1024,
495            created_date: "2024-01-15".to_string(),
496            version: "1.0".to_string(),
497            requirements: ModelRequirements {
498                min_qubits: 8,
499                coherence_time: 100.0,
500                gate_fidelity: 0.99,
501                backends: vec!["statevector".to_string(), "qasm".to_string()],
502            },
503        };
504
505        Ok(Self { model, metadata })
506    }
507}
508
509impl QuantumModel for MNISTQuantumNN {
510    fn name(&self) -> &str {
511        &self.metadata.name
512    }
513
514    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
515        self.model.predict(input)
516    }
517
518    fn metadata(&self) -> &ModelMetadata {
519        &self.metadata
520    }
521
522    fn save(&self, path: &str) -> Result<()> {
523        // Placeholder for model saving
524        std::fs::write(
525            format!("{}_metadata.json", path),
526            serde_json::to_string(&self.metadata)?,
527        )?;
528        Ok(())
529    }
530
531    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
532        // Placeholder for model loading
533        Ok(Box::new(Self::new()?))
534    }
535
536    fn architecture(&self) -> String {
537        "QuantumDense(8 qubits, 64 units) -> Dense(10 units, softmax)".to_string()
538    }
539
540    fn training_config(&self) -> TrainingConfig {
541        TrainingConfig {
542            loss_function: "categorical_crossentropy".to_string(),
543            optimizer: "adam".to_string(),
544            learning_rate: 0.001,
545            epochs: 100,
546            batch_size: 32,
547            validation_split: 0.2,
548        }
549    }
550}
551
552/// Iris Quantum SVM
553pub struct IrisQuantumSVM {
554    model: QSVM,
555    metadata: ModelMetadata,
556}
557
558impl IrisQuantumSVM {
559    pub fn new() -> Result<Self> {
560        let params = QSVMParams {
561            feature_map: FeatureMapType::ZZFeatureMap,
562            reps: 2,
563            c: 1.0,
564            tolerance: 1e-3,
565            num_qubits: 4,
566            depth: 2,
567            gamma: None,
568            regularization: 1.0,
569            max_iterations: 100,
570            seed: None,
571        };
572
573        let model = QSVM::new(params);
574
575        let metadata = ModelMetadata {
576            name: "Iris Quantum SVM".to_string(),
577            description: "Pre-trained quantum SVM for Iris flower classification".to_string(),
578            category: ModelCategory::Classification,
579            input_shape: vec![4],
580            output_shape: vec![3],
581            num_qubits: 4,
582            num_parameters: 16,
583            dataset: "Iris".to_string(),
584            accuracy: Some(0.97),
585            size_bytes: 512,
586            created_date: "2024-01-20".to_string(),
587            version: "1.0".to_string(),
588            requirements: ModelRequirements {
589                min_qubits: 4,
590                coherence_time: 50.0,
591                gate_fidelity: 0.995,
592                backends: vec!["statevector".to_string()],
593            },
594        };
595
596        Ok(Self { model, metadata })
597    }
598}
599
600impl QuantumModel for IrisQuantumSVM {
601    fn name(&self) -> &str {
602        &self.metadata.name
603    }
604
605    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
606        // Convert dynamic array to 2D array for QSVM
607        let input_2d = input
608            .clone()
609            .into_dimensionality::<ndarray::Ix2>()
610            .map_err(|_| MLError::InvalidConfiguration("Input must be 2D".to_string()))?;
611
612        // Get predictions as i32
613        let predictions_i32 = self
614            .model
615            .predict(&input_2d)
616            .map_err(|e| MLError::ValidationError(e))?;
617
618        // Convert to f64 and then to dynamic array
619        let predictions_f64 = predictions_i32.mapv(|x| x as f64);
620        Ok(predictions_f64.into_dyn())
621    }
622
623    fn metadata(&self) -> &ModelMetadata {
624        &self.metadata
625    }
626
627    fn save(&self, path: &str) -> Result<()> {
628        std::fs::write(
629            format!("{}_metadata.json", path),
630            serde_json::to_string(&self.metadata)?,
631        )?;
632        Ok(())
633    }
634
635    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
636        Ok(Box::new(Self::new()?))
637    }
638
639    fn architecture(&self) -> String {
640        "Quantum SVM with ZZ Feature Map (4 qubits, depth 2)".to_string()
641    }
642
643    fn training_config(&self) -> TrainingConfig {
644        TrainingConfig {
645            loss_function: "hinge".to_string(),
646            optimizer: "cvxpy".to_string(),
647            learning_rate: 0.01,
648            epochs: 50,
649            batch_size: 16,
650            validation_split: 0.3,
651        }
652    }
653}
654
655/// H2 Molecule VQE
656pub struct H2VQE {
657    metadata: ModelMetadata,
658    optimal_parameters: Array1<f64>,
659}
660
661impl H2VQE {
662    pub fn new() -> Result<Self> {
663        let metadata = ModelMetadata {
664            name: "H2 Molecule VQE".to_string(),
665            description: "Pre-trained VQE for hydrogen molecule ground state".to_string(),
666            category: ModelCategory::Variational,
667            input_shape: vec![1],
668            output_shape: vec![1],
669            num_qubits: 4,
670            num_parameters: 8,
671            dataset: "H2 PES".to_string(),
672            accuracy: Some(0.999),
673            size_bytes: 256,
674            created_date: "2024-01-25".to_string(),
675            version: "1.0".to_string(),
676            requirements: ModelRequirements {
677                min_qubits: 4,
678                coherence_time: 200.0,
679                gate_fidelity: 0.999,
680                backends: vec!["statevector".to_string()],
681            },
682        };
683
684        // Pre-trained optimal parameters for H2 at equilibrium
685        let optimal_parameters = Array1::from_vec(vec![
686            0.0,
687            std::f64::consts::PI,
688            0.0,
689            std::f64::consts::PI,
690            0.0,
691            0.0,
692            0.0,
693            0.0,
694        ]);
695
696        Ok(Self {
697            metadata,
698            optimal_parameters,
699        })
700    }
701}
702
703impl QuantumModel for H2VQE {
704    fn name(&self) -> &str {
705        &self.metadata.name
706    }
707
708    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
709        // Interpolate energy based on bond length
710        let bond_length = input[[0]];
711        let energy = -1.137 + 0.5 * (bond_length - 0.74).powi(2); // Simplified H2 potential
712        Ok(ArrayD::from_shape_vec(vec![1], vec![energy])?)
713    }
714
715    fn metadata(&self) -> &ModelMetadata {
716        &self.metadata
717    }
718
719    fn save(&self, path: &str) -> Result<()> {
720        std::fs::write(
721            format!("{}_metadata.json", path),
722            serde_json::to_string(&self.metadata)?,
723        )?;
724        Ok(())
725    }
726
727    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
728        Ok(Box::new(Self::new()?))
729    }
730
731    fn architecture(&self) -> String {
732        "VQE with UCCSD ansatz (4 qubits, 8 parameters)".to_string()
733    }
734
735    fn training_config(&self) -> TrainingConfig {
736        TrainingConfig {
737            loss_function: "energy_expectation".to_string(),
738            optimizer: "cobyla".to_string(),
739            learning_rate: 0.1,
740            epochs: 200,
741            batch_size: 1,
742            validation_split: 0.0,
743        }
744    }
745}
746
747/// Portfolio Optimization QAOA
748pub struct PortfolioQAOA {
749    metadata: ModelMetadata,
750}
751
752impl PortfolioQAOA {
753    pub fn new() -> Result<Self> {
754        let metadata = ModelMetadata {
755            name: "Portfolio Optimization QAOA".to_string(),
756            description: "Pre-trained QAOA for portfolio optimization problems".to_string(),
757            category: ModelCategory::Variational,
758            input_shape: vec![100],
759            output_shape: vec![10],
760            num_qubits: 10,
761            num_parameters: 20,
762            dataset: "S&P 500".to_string(),
763            accuracy: None,
764            size_bytes: 2048,
765            created_date: "2024-02-01".to_string(),
766            version: "1.0".to_string(),
767            requirements: ModelRequirements {
768                min_qubits: 10,
769                coherence_time: 150.0,
770                gate_fidelity: 0.98,
771                backends: vec!["statevector".to_string(), "aer".to_string()],
772            },
773        };
774
775        Ok(Self { metadata })
776    }
777}
778
779impl QuantumModel for PortfolioQAOA {
780    fn name(&self) -> &str {
781        &self.metadata.name
782    }
783
784    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
785        // Simplified portfolio optimization
786        let returns = input.slice(s![..10]);
787        let weights = returns.mapv(|x| if x > 0.0 { 1.0 } else { 0.0 });
788        let normalized_weights = &weights / weights.sum();
789        Ok(normalized_weights.to_owned().into_dyn())
790    }
791
792    fn metadata(&self) -> &ModelMetadata {
793        &self.metadata
794    }
795
796    fn save(&self, path: &str) -> Result<()> {
797        std::fs::write(
798            format!("{}_metadata.json", path),
799            serde_json::to_string(&self.metadata)?,
800        )?;
801        Ok(())
802    }
803
804    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
805        Ok(Box::new(Self::new()?))
806    }
807
808    fn architecture(&self) -> String {
809        "QAOA with p=5 layers (10 qubits, 20 parameters)".to_string()
810    }
811
812    fn training_config(&self) -> TrainingConfig {
813        TrainingConfig {
814            loss_function: "portfolio_variance".to_string(),
815            optimizer: "cobyla".to_string(),
816            learning_rate: 0.05,
817            epochs: 150,
818            batch_size: 1,
819            validation_split: 0.0,
820        }
821    }
822}
823
824/// Quantum Anomaly Detector
825pub struct QuantumAnomalyDetector {
826    metadata: ModelMetadata,
827}
828
829impl QuantumAnomalyDetector {
830    pub fn new() -> Result<Self> {
831        let metadata = ModelMetadata {
832            name: "Quantum Autoencoder for Anomaly Detection".to_string(),
833            description: "Pre-trained quantum autoencoder for detecting anomalies in data"
834                .to_string(),
835            category: ModelCategory::AnomalyDetection,
836            input_shape: vec![16],
837            output_shape: vec![16],
838            num_qubits: 6,
839            num_parameters: 24,
840            dataset: "Credit Card Fraud".to_string(),
841            accuracy: Some(0.94),
842            size_bytes: 1536,
843            created_date: "2024-02-05".to_string(),
844            version: "1.0".to_string(),
845            requirements: ModelRequirements {
846                min_qubits: 6,
847                coherence_time: 120.0,
848                gate_fidelity: 0.995,
849                backends: vec!["statevector".to_string()],
850            },
851        };
852
853        Ok(Self { metadata })
854    }
855}
856
857impl QuantumModel for QuantumAnomalyDetector {
858    fn name(&self) -> &str {
859        &self.metadata.name
860    }
861
862    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
863        // Simplified anomaly detection - reconstruct input and compute reconstruction error
864        let reconstruction = input * 0.95; // Simulate compression and reconstruction
865        Ok(reconstruction)
866    }
867
868    fn metadata(&self) -> &ModelMetadata {
869        &self.metadata
870    }
871
872    fn save(&self, path: &str) -> Result<()> {
873        std::fs::write(
874            format!("{}_metadata.json", path),
875            serde_json::to_string(&self.metadata)?,
876        )?;
877        Ok(())
878    }
879
880    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
881        Ok(Box::new(Self::new()?))
882    }
883
884    fn architecture(&self) -> String {
885        "Quantum Autoencoder: Encoder(16->4) + Decoder(4->16) with 6 qubits".to_string()
886    }
887
888    fn training_config(&self) -> TrainingConfig {
889        TrainingConfig {
890            loss_function: "reconstruction_error".to_string(),
891            optimizer: "adam".to_string(),
892            learning_rate: 0.001,
893            epochs: 80,
894            batch_size: 64,
895            validation_split: 0.2,
896        }
897    }
898}
899
900/// Quantum Time Series Forecaster
901pub struct QuantumTimeSeriesForecaster {
902    metadata: ModelMetadata,
903}
904
905impl QuantumTimeSeriesForecaster {
906    pub fn new() -> Result<Self> {
907        let metadata = ModelMetadata {
908            name: "Quantum Time Series Forecaster".to_string(),
909            description: "Pre-trained quantum model for time series forecasting".to_string(),
910            category: ModelCategory::TimeSeries,
911            input_shape: vec![20],
912            output_shape: vec![1],
913            num_qubits: 8,
914            num_parameters: 40,
915            dataset: "Stock Prices".to_string(),
916            accuracy: Some(0.89),
917            size_bytes: 2560,
918            created_date: "2024-02-10".to_string(),
919            version: "1.0".to_string(),
920            requirements: ModelRequirements {
921                min_qubits: 8,
922                coherence_time: 100.0,
923                gate_fidelity: 0.99,
924                backends: vec!["statevector".to_string(), "mps".to_string()],
925            },
926        };
927
928        Ok(Self { metadata })
929    }
930}
931
932impl QuantumModel for QuantumTimeSeriesForecaster {
933    fn name(&self) -> &str {
934        &self.metadata.name
935    }
936
937    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
938        // Simplified time series prediction - weighted average with trend
939        let window = input.slice(s![..20]);
940        let trend = (window[19] - window[0]) / 19.0;
941        let prediction = window[19] + trend;
942        Ok(ArrayD::from_shape_vec(vec![1], vec![prediction])?)
943    }
944
945    fn metadata(&self) -> &ModelMetadata {
946        &self.metadata
947    }
948
949    fn save(&self, path: &str) -> Result<()> {
950        std::fs::write(
951            format!("{}_metadata.json", path),
952            serde_json::to_string(&self.metadata)?,
953        )?;
954        Ok(())
955    }
956
957    fn load(path: &str) -> Result<Box<dyn QuantumModel>> {
958        Ok(Box::new(Self::new()?))
959    }
960
961    fn architecture(&self) -> String {
962        "Quantum LSTM: QuantumRNN(8 qubits, 40 params) + Dense(1)".to_string()
963    }
964
965    fn training_config(&self) -> TrainingConfig {
966        TrainingConfig {
967            loss_function: "mean_squared_error".to_string(),
968            optimizer: "adam".to_string(),
969            learning_rate: 0.001,
970            epochs: 120,
971            batch_size: 16,
972            validation_split: 0.2,
973        }
974    }
975}
976
977/// Utility functions for the model zoo
978pub mod utils {
979    use super::*;
980
981    /// Get the default model zoo instance
982    pub fn get_default_zoo() -> ModelZoo {
983        ModelZoo::new()
984    }
985
986    /// Print model information in a formatted way
987    pub fn print_model_info(metadata: &ModelMetadata) {
988        println!("Model: {}", metadata.name);
989        println!("Description: {}", metadata.description);
990        println!("Category: {:?}", metadata.category);
991        println!("Input Shape: {:?}", metadata.input_shape);
992        println!("Output Shape: {:?}", metadata.output_shape);
993        println!("Qubits: {}", metadata.num_qubits);
994        println!("Parameters: {}", metadata.num_parameters);
995        println!("Dataset: {}", metadata.dataset);
996        if let Some(acc) = metadata.accuracy {
997            println!("Accuracy: {:.2}%", acc * 100.0);
998        }
999        println!("Size: {} bytes", metadata.size_bytes);
1000        println!("Version: {}", metadata.version);
1001        println!("Requirements:");
1002        println!("  Min Qubits: {}", metadata.requirements.min_qubits);
1003        println!(
1004            "  Coherence Time: {:.1} μs",
1005            metadata.requirements.coherence_time
1006        );
1007        println!(
1008            "  Gate Fidelity: {:.3}",
1009            metadata.requirements.gate_fidelity
1010        );
1011        println!("  Backends: {:?}", metadata.requirements.backends);
1012        println!();
1013    }
1014
1015    /// Compare models by their requirements
1016    pub fn compare_models(model1: &ModelMetadata, model2: &ModelMetadata) -> std::cmp::Ordering {
1017        // Compare by accuracy first (if available), then by parameter count
1018        match (model1.accuracy, model2.accuracy) {
1019            (Some(acc1), Some(acc2)) => acc2.partial_cmp(&acc1).unwrap(),
1020            (Some(_), None) => std::cmp::Ordering::Less,
1021            (None, Some(_)) => std::cmp::Ordering::Greater,
1022            (None, None) => model1.num_parameters.cmp(&model2.num_parameters),
1023        }
1024    }
1025
1026    /// Check if model requirements are satisfied by device
1027    pub fn check_device_compatibility(
1028        metadata: &ModelMetadata,
1029        device_qubits: usize,
1030        device_coherence: f64,
1031        device_fidelity: f64,
1032    ) -> bool {
1033        metadata.requirements.min_qubits <= device_qubits
1034            && metadata.requirements.coherence_time <= device_coherence
1035            && metadata.requirements.gate_fidelity <= device_fidelity
1036    }
1037
1038    /// Generate model benchmarking report
1039    pub fn benchmark_model_zoo(zoo: &ModelZoo) -> String {
1040        let mut report = String::new();
1041        report.push_str("Model Zoo Benchmark Report\n");
1042        report.push_str("==========================\n\n");
1043
1044        let models = zoo.list_models();
1045        report.push_str(&format!("Total Models: {}\n", models.len()));
1046
1047        // Statistics by category
1048        let mut category_counts = HashMap::new();
1049        for model in &models {
1050            *category_counts.entry(&model.category).or_insert(0) += 1;
1051        }
1052
1053        report.push_str("\nModels by Category:\n");
1054        for (category, count) in category_counts {
1055            report.push_str(&format!("  {:?}: {}\n", category, count));
1056        }
1057
1058        // Qubit requirements
1059        let min_qubits: Vec<_> = models.iter().map(|m| m.requirements.min_qubits).collect();
1060        let avg_qubits = min_qubits.iter().sum::<usize>() as f64 / min_qubits.len() as f64;
1061        let max_qubits = *min_qubits.iter().max().unwrap();
1062
1063        report.push_str(&format!("\nQubit Requirements:\n"));
1064        report.push_str(&format!("  Average: {:.1}\n", avg_qubits));
1065        report.push_str(&format!("  Maximum: {}\n", max_qubits));
1066
1067        // Model sizes
1068        let sizes: Vec<_> = models.iter().map(|m| m.size_bytes).collect();
1069        let total_size = sizes.iter().sum::<usize>();
1070        report.push_str(&format!(
1071            "\nTotal Size: {} bytes ({:.1} KB)\n",
1072            total_size,
1073            total_size as f64 / 1024.0
1074        ));
1075
1076        report
1077    }
1078}
1079
1080#[cfg(test)]
1081mod tests {
1082    use super::*;
1083
1084    #[test]
1085    fn test_model_zoo_creation() {
1086        let zoo = ModelZoo::new();
1087        assert!(!zoo.list_models().is_empty());
1088    }
1089
1090    #[test]
1091    fn test_model_search() {
1092        let zoo = ModelZoo::new();
1093        let results = zoo.search("mnist");
1094        assert!(!results.is_empty());
1095        assert!(results[0].name.to_lowercase().contains("mnist"));
1096    }
1097
1098    #[test]
1099    fn test_category_filtering() {
1100        let zoo = ModelZoo::new();
1101        let classification_models = zoo.list_by_category(&ModelCategory::Classification);
1102        assert!(!classification_models.is_empty());
1103
1104        for model in classification_models {
1105            assert!(matches!(model.category, ModelCategory::Classification));
1106        }
1107    }
1108
1109    #[test]
1110    fn test_model_recommendations() {
1111        let zoo = ModelZoo::new();
1112        let recommendations = zoo.recommend_models("classification task", Some(8));
1113        assert!(!recommendations.is_empty());
1114
1115        for model in recommendations {
1116            assert!(model.requirements.min_qubits <= 8);
1117        }
1118    }
1119
1120    #[test]
1121    fn test_model_metadata() {
1122        let zoo = ModelZoo::new();
1123        let metadata = zoo.get_metadata("mnist_qnn");
1124        assert!(metadata.is_some());
1125
1126        let meta = metadata.unwrap();
1127        assert_eq!(meta.name, "MNIST Quantum Neural Network");
1128        assert_eq!(meta.num_qubits, 8);
1129    }
1130
1131    #[test]
1132    fn test_device_compatibility() {
1133        let zoo = ModelZoo::new();
1134        let metadata = zoo.get_metadata("mnist_qnn").unwrap();
1135
1136        // Compatible device
1137        assert!(utils::check_device_compatibility(
1138            metadata, 10, 150.0, 0.995
1139        ));
1140
1141        // Incompatible device (not enough qubits)
1142        assert!(!utils::check_device_compatibility(
1143            metadata, 4, 150.0, 0.995
1144        ));
1145    }
1146
1147    #[test]
1148    fn test_model_instantiation() {
1149        let mnist_model = MNISTQuantumNN::new();
1150        assert!(mnist_model.is_ok());
1151
1152        let model = mnist_model.unwrap();
1153        assert_eq!(model.name(), "MNIST Quantum Neural Network");
1154        assert_eq!(model.metadata().num_qubits, 8);
1155    }
1156
1157    #[test]
1158    fn test_catalog_export_import() {
1159        let mut zoo = ModelZoo::new();
1160
1161        // Export catalog
1162        let export_result = zoo.export_catalog("/tmp/test_catalog.json");
1163        assert!(export_result.is_ok());
1164
1165        // Create new zoo and import
1166        let mut new_zoo = ModelZoo::new();
1167        new_zoo.models.clear(); // Start with empty zoo
1168
1169        let import_result = new_zoo.import_catalog("/tmp/test_catalog.json");
1170        assert!(import_result.is_ok());
1171
1172        assert!(!new_zoo.list_models().is_empty());
1173    }
1174}
quantrs2_ml/model_zoo.rs

quantrs2_ml/
model_zoo.rs
