QuantumGAN

quantrs2_ml::gan

Struct QuantumGAN 

Source 
pub struct QuantumGAN {
    pub generator: QuantumGenerator,
    pub discriminator: QuantumDiscriminator,
    pub training_history: GANTrainingHistory,
}
Expand description

Quantum Generative Adversarial Network

Fields§

§generator: QuantumGenerator

Generator model

§discriminator: QuantumDiscriminator

Discriminator model

§training_history: GANTrainingHistory

Training history

Implementations§

Source§

impl QuantumGAN

Source

pub fn new( num_qubits_gen: usize, num_qubits_disc: usize, latent_dim: usize, data_dim: usize, generator_type: GeneratorType, discriminator_type: DiscriminatorType, ) -> Result<Self>

Creates a new quantum GAN

Examples found in repository?
examples/quantum_gan.rs (lines 25-32)
8fn main() -> Result<()> {
9    println!("Quantum Generative Adversarial Network Example");
10    println!("=============================================");
11
12    // GAN parameters
13    let num_qubits_gen = 6;
14    let num_qubits_disc = 6;
15    let latent_dim = 4;
16    let data_dim = 8;
17
18    println!("Creating Quantum GAN...");
19    println!("  Generator: {num_qubits_gen} qubits");
20    println!("  Discriminator: {num_qubits_disc} qubits");
21    println!("  Latent dimension: {latent_dim}");
22    println!("  Data dimension: {data_dim}");
23
24    // Create quantum GAN
25    let mut qgan = QuantumGAN::new(
26        num_qubits_gen,
27        num_qubits_disc,
28        latent_dim,
29        data_dim,
30        GeneratorType::HybridClassicalQuantum,
31        DiscriminatorType::HybridQuantumFeatures,
32    )?;
33
34    // Generate synthetic data for training
35    println!("Generating synthetic data for training...");
36    let real_data = generate_sine_wave_data(500, data_dim);
37
38    // Train GAN
39    println!("Training quantum GAN...");
40    let training_params = [
41        (50, 32, 0.01, 0.01, 1), // (epochs, batch_size, lr_gen, lr_disc, disc_steps)
42    ];
43
44    for (epochs, batch_size, lr_gen, lr_disc, disc_steps) in training_params {
45        println!("Training with parameters:");
46        println!("  Epochs: {epochs}");
47        println!("  Batch size: {batch_size}");
48        println!("  Generator learning rate: {lr_gen}");
49        println!("  Discriminator learning rate: {lr_disc}");
50        println!("  Discriminator steps per iteration: {disc_steps}");
51
52        let start = Instant::now();
53        let history = qgan.train(&real_data, epochs, batch_size, lr_gen, lr_disc, disc_steps)?;
54
55        println!("Training completed in {:.2?}", start.elapsed());
56        println!("Final losses:");
57        println!(
58            "  Generator: {:.4}",
59            history.gen_losses.last().unwrap_or(&0.0)
60        );
61        println!(
62            "  Discriminator: {:.4}",
63            history.disc_losses.last().unwrap_or(&0.0)
64        );
65    }
66
67    // Generate samples
68    println!("\nGenerating samples from trained GAN...");
69    let num_samples = 10;
70    let generated_samples = qgan.generate(num_samples)?;
71
72    println!("Generated {num_samples} samples");
73    println!("First sample:");
74    print_sample(
75        &generated_samples
76            .slice(scirs2_core::ndarray::s![0, ..])
77            .to_owned(),
78    );
79
80    // Evaluate GAN
81    println!("\nEvaluating GAN quality...");
82    let eval_metrics = qgan.evaluate(&real_data, num_samples)?;
83
84    println!("Evaluation metrics:");
85    println!(
86        "  Real data accuracy: {:.2}%",
87        eval_metrics.real_accuracy * 100.0
88    );
89    println!(
90        "  Fake data accuracy: {:.2}%",
91        eval_metrics.fake_accuracy * 100.0
92    );
93    println!(
94        "  Overall discriminator accuracy: {:.2}%",
95        eval_metrics.overall_accuracy * 100.0
96    );
97    println!("  JS Divergence: {:.4}", eval_metrics.js_divergence);
98
99    // Use physics-specific GAN
100    println!("\nCreating specialized particle physics GAN...");
101    let particle_gan = quantrs2_ml::gan::physics_gan::ParticleGAN::new(
102        num_qubits_gen,
103        num_qubits_disc,
104        latent_dim,
105        data_dim,
106    )?;
107
108    println!("Particle GAN created successfully");
109
110    Ok(())
111}
Source

pub fn train( &mut self, real_data: &Array2<f64>, epochs: usize, batch_size: usize, gen_learning_rate: f64, disc_learning_rate: f64, disc_steps: usize, ) -> Result<&GANTrainingHistory>

Trains the GAN on a dataset

Examples found in repository?
examples/quantum_gan.rs (line 53)
8fn main() -> Result<()> {
9    println!("Quantum Generative Adversarial Network Example");
10    println!("=============================================");
11
12    // GAN parameters
13    let num_qubits_gen = 6;
14    let num_qubits_disc = 6;
15    let latent_dim = 4;
16    let data_dim = 8;
17
18    println!("Creating Quantum GAN...");
19    println!("  Generator: {num_qubits_gen} qubits");
20    println!("  Discriminator: {num_qubits_disc} qubits");
21    println!("  Latent dimension: {latent_dim}");
22    println!("  Data dimension: {data_dim}");
23
24    // Create quantum GAN
25    let mut qgan = QuantumGAN::new(
26        num_qubits_gen,
27        num_qubits_disc,
28        latent_dim,
29        data_dim,
30        GeneratorType::HybridClassicalQuantum,
31        DiscriminatorType::HybridQuantumFeatures,
32    )?;
33
34    // Generate synthetic data for training
35    println!("Generating synthetic data for training...");
36    let real_data = generate_sine_wave_data(500, data_dim);
37
38    // Train GAN
39    println!("Training quantum GAN...");
40    let training_params = [
41        (50, 32, 0.01, 0.01, 1), // (epochs, batch_size, lr_gen, lr_disc, disc_steps)
42    ];
43
44    for (epochs, batch_size, lr_gen, lr_disc, disc_steps) in training_params {
45        println!("Training with parameters:");
46        println!("  Epochs: {epochs}");
47        println!("  Batch size: {batch_size}");
48        println!("  Generator learning rate: {lr_gen}");
49        println!("  Discriminator learning rate: {lr_disc}");
50        println!("  Discriminator steps per iteration: {disc_steps}");
51
52        let start = Instant::now();
53        let history = qgan.train(&real_data, epochs, batch_size, lr_gen, lr_disc, disc_steps)?;
54
55        println!("Training completed in {:.2?}", start.elapsed());
56        println!("Final losses:");
57        println!(
58            "  Generator: {:.4}",
59            history.gen_losses.last().unwrap_or(&0.0)
60        );
61        println!(
62            "  Discriminator: {:.4}",
63            history.disc_losses.last().unwrap_or(&0.0)
64        );
65    }
66
67    // Generate samples
68    println!("\nGenerating samples from trained GAN...");
69    let num_samples = 10;
70    let generated_samples = qgan.generate(num_samples)?;
71
72    println!("Generated {num_samples} samples");
73    println!("First sample:");
74    print_sample(
75        &generated_samples
76            .slice(scirs2_core::ndarray::s![0, ..])
77            .to_owned(),
78    );
79
80    // Evaluate GAN
81    println!("\nEvaluating GAN quality...");
82    let eval_metrics = qgan.evaluate(&real_data, num_samples)?;
83
84    println!("Evaluation metrics:");
85    println!(
86        "  Real data accuracy: {:.2}%",
87        eval_metrics.real_accuracy * 100.0
88    );
89    println!(
90        "  Fake data accuracy: {:.2}%",
91        eval_metrics.fake_accuracy * 100.0
92    );
93    println!(
94        "  Overall discriminator accuracy: {:.2}%",
95        eval_metrics.overall_accuracy * 100.0
96    );
97    println!("  JS Divergence: {:.4}", eval_metrics.js_divergence);
98
99    // Use physics-specific GAN
100    println!("\nCreating specialized particle physics GAN...");
101    let particle_gan = quantrs2_ml::gan::physics_gan::ParticleGAN::new(
102        num_qubits_gen,
103        num_qubits_disc,
104        latent_dim,
105        data_dim,
106    )?;
107
108    println!("Particle GAN created successfully");
109
110    Ok(())
111}
Source

pub fn generate(&self, num_samples: usize) -> Result<Array2<f64>>

Generates samples from the trained generator

Examples found in repository?
examples/quantum_gan.rs (line 70)
8fn main() -> Result<()> {
9    println!("Quantum Generative Adversarial Network Example");
10    println!("=============================================");
11
12    // GAN parameters
13    let num_qubits_gen = 6;
14    let num_qubits_disc = 6;
15    let latent_dim = 4;
16    let data_dim = 8;
17
18    println!("Creating Quantum GAN...");
19    println!("  Generator: {num_qubits_gen} qubits");
20    println!("  Discriminator: {num_qubits_disc} qubits");
21    println!("  Latent dimension: {latent_dim}");
22    println!("  Data dimension: {data_dim}");
23
24    // Create quantum GAN
25    let mut qgan = QuantumGAN::new(
26        num_qubits_gen,
27        num_qubits_disc,
28        latent_dim,
29        data_dim,
30        GeneratorType::HybridClassicalQuantum,
31        DiscriminatorType::HybridQuantumFeatures,
32    )?;
33
34    // Generate synthetic data for training
35    println!("Generating synthetic data for training...");
36    let real_data = generate_sine_wave_data(500, data_dim);
37
38    // Train GAN
39    println!("Training quantum GAN...");
40    let training_params = [
41        (50, 32, 0.01, 0.01, 1), // (epochs, batch_size, lr_gen, lr_disc, disc_steps)
42    ];
43
44    for (epochs, batch_size, lr_gen, lr_disc, disc_steps) in training_params {
45        println!("Training with parameters:");
46        println!("  Epochs: {epochs}");
47        println!("  Batch size: {batch_size}");
48        println!("  Generator learning rate: {lr_gen}");
49        println!("  Discriminator learning rate: {lr_disc}");
50        println!("  Discriminator steps per iteration: {disc_steps}");
51
52        let start = Instant::now();
53        let history = qgan.train(&real_data, epochs, batch_size, lr_gen, lr_disc, disc_steps)?;
54
55        println!("Training completed in {:.2?}", start.elapsed());
56        println!("Final losses:");
57        println!(
58            "  Generator: {:.4}",
59            history.gen_losses.last().unwrap_or(&0.0)
60        );
61        println!(
62            "  Discriminator: {:.4}",
63            history.disc_losses.last().unwrap_or(&0.0)
64        );
65    }
66
67    // Generate samples
68    println!("\nGenerating samples from trained GAN...");
69    let num_samples = 10;
70    let generated_samples = qgan.generate(num_samples)?;
71
72    println!("Generated {num_samples} samples");
73    println!("First sample:");
74    print_sample(
75        &generated_samples
76            .slice(scirs2_core::ndarray::s![0, ..])
77            .to_owned(),
78    );
79
80    // Evaluate GAN
81    println!("\nEvaluating GAN quality...");
82    let eval_metrics = qgan.evaluate(&real_data, num_samples)?;
83
84    println!("Evaluation metrics:");
85    println!(
86        "  Real data accuracy: {:.2}%",
87        eval_metrics.real_accuracy * 100.0
88    );
89    println!(
90        "  Fake data accuracy: {:.2}%",
91        eval_metrics.fake_accuracy * 100.0
92    );
93    println!(
94        "  Overall discriminator accuracy: {:.2}%",
95        eval_metrics.overall_accuracy * 100.0
96    );
97    println!("  JS Divergence: {:.4}", eval_metrics.js_divergence);
98
99    // Use physics-specific GAN
100    println!("\nCreating specialized particle physics GAN...");
101    let particle_gan = quantrs2_ml::gan::physics_gan::ParticleGAN::new(
102        num_qubits_gen,
103        num_qubits_disc,
104        latent_dim,
105        data_dim,
106    )?;
107
108    println!("Particle GAN created successfully");
109
110    Ok(())
111}
Source

pub fn generate_conditional( &self, num_samples: usize, conditions: &[(usize, f64)], ) -> Result<Array2<f64>>

Generates samples with specific conditions

Source

pub fn evaluate( &self, real_data: &Array2<f64>, num_samples: usize, ) -> Result<GANEvaluationMetrics>

Evaluates the GAN model

Examples found in repository?
examples/quantum_gan.rs (line 82)
8fn main() -> Result<()> {
9    println!("Quantum Generative Adversarial Network Example");
10    println!("=============================================");
11
12    // GAN parameters
13    let num_qubits_gen = 6;
14    let num_qubits_disc = 6;
15    let latent_dim = 4;
16    let data_dim = 8;
17
18    println!("Creating Quantum GAN...");
19    println!("  Generator: {num_qubits_gen} qubits");
20    println!("  Discriminator: {num_qubits_disc} qubits");
21    println!("  Latent dimension: {latent_dim}");
22    println!("  Data dimension: {data_dim}");
23
24    // Create quantum GAN
25    let mut qgan = QuantumGAN::new(
26        num_qubits_gen,
27        num_qubits_disc,
28        latent_dim,
29        data_dim,
30        GeneratorType::HybridClassicalQuantum,
31        DiscriminatorType::HybridQuantumFeatures,
32    )?;
33
34    // Generate synthetic data for training
35    println!("Generating synthetic data for training...");
36    let real_data = generate_sine_wave_data(500, data_dim);
37
38    // Train GAN
39    println!("Training quantum GAN...");
40    let training_params = [
41        (50, 32, 0.01, 0.01, 1), // (epochs, batch_size, lr_gen, lr_disc, disc_steps)
42    ];
43
44    for (epochs, batch_size, lr_gen, lr_disc, disc_steps) in training_params {
45        println!("Training with parameters:");
46        println!("  Epochs: {epochs}");
47        println!("  Batch size: {batch_size}");
48        println!("  Generator learning rate: {lr_gen}");
49        println!("  Discriminator learning rate: {lr_disc}");
50        println!("  Discriminator steps per iteration: {disc_steps}");
51
52        let start = Instant::now();
53        let history = qgan.train(&real_data, epochs, batch_size, lr_gen, lr_disc, disc_steps)?;
54
55        println!("Training completed in {:.2?}", start.elapsed());
56        println!("Final losses:");
57        println!(
58            "  Generator: {:.4}",
59            history.gen_losses.last().unwrap_or(&0.0)
60        );
61        println!(
62            "  Discriminator: {:.4}",
63            history.disc_losses.last().unwrap_or(&0.0)
64        );
65    }
66
67    // Generate samples
68    println!("\nGenerating samples from trained GAN...");
69    let num_samples = 10;
70    let generated_samples = qgan.generate(num_samples)?;
71
72    println!("Generated {num_samples} samples");
73    println!("First sample:");
74    print_sample(
75        &generated_samples
76            .slice(scirs2_core::ndarray::s![0, ..])
77            .to_owned(),
78    );
79
80    // Evaluate GAN
81    println!("\nEvaluating GAN quality...");
82    let eval_metrics = qgan.evaluate(&real_data, num_samples)?;
83
84    println!("Evaluation metrics:");
85    println!(
86        "  Real data accuracy: {:.2}%",
87        eval_metrics.real_accuracy * 100.0
88    );
89    println!(
90        "  Fake data accuracy: {:.2}%",
91        eval_metrics.fake_accuracy * 100.0
92    );
93    println!(
94        "  Overall discriminator accuracy: {:.2}%",
95        eval_metrics.overall_accuracy * 100.0
96    );
97    println!("  JS Divergence: {:.4}", eval_metrics.js_divergence);
98
99    // Use physics-specific GAN
100    println!("\nCreating specialized particle physics GAN...");
101    let particle_gan = quantrs2_ml::gan::physics_gan::ParticleGAN::new(
102        num_qubits_gen,
103        num_qubits_disc,
104        latent_dim,
105        data_dim,
106    )?;
107
108    println!("Particle GAN created successfully");
109
110    Ok(())
111}

Trait Implementations§

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impl Clone for QuantumGAN

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fn clone(&self) -> QuantumGAN

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for QuantumGAN

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more

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