QuantumBoltzmannMachine

quantrs2_ml::boltzmann

Struct QuantumBoltzmannMachine 

Source 
pub struct QuantumBoltzmannMachine { /* private fields */ }
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Quantum Boltzmann Machine

Implementations§

Source§

impl QuantumBoltzmannMachine

Source

pub fn new( num_visible: usize, num_hidden: usize, temperature: f64, learning_rate: f64, ) -> Result<Self>

Create a new Quantum Boltzmann Machine

Examples found in repository?
examples/quantum_boltzmann.rs (lines 41-46)
39fn basic_qbm_demo() -> Result<()> {
40    // Create a small QBM
41    let mut qbm = QuantumBoltzmannMachine::new(
42        4,    // visible units
43        2,    // hidden units
44        1.0,  // temperature
45        0.01, // learning rate
46    )?;
47
48    println!("   Created QBM with 4 visible and 2 hidden units");
49
50    // Generate synthetic binary data
51    let data = generate_binary_patterns(100, 4);
52
53    // Train the QBM
54    println!("   Training on binary patterns...");
55    let losses = qbm.train(&data, 50, 10)?;
56
57    println!("   Training complete:");
58    println!("   - Initial loss: {:.4}", losses[0]);
59    println!("   - Final loss: {:.4}", losses.last().unwrap());
60
61    // Sample from trained model
62    let samples = qbm.sample(5)?;
63    println!("\n   Generated samples:");
64    for (i, sample) in samples.outer_iter().enumerate() {
65        print!("   Sample {}: [", i + 1);
66        for val in sample {
67            print!("{val:.0} ");
68        }
69        println!("]");
70    }
71
72    Ok(())
73}
74
75/// RBM demonstration with persistent contrastive divergence
76fn rbm_demo() -> Result<()> {
77    // Create RBM with annealing
78    let annealing = AnnealingSchedule::new(2.0, 0.5, 100);
79
80    let mut rbm = QuantumRBM::new(
81        6,    // visible units
82        3,    // hidden units
83        2.0,  // initial temperature
84        0.01, // learning rate
85    )?
86    .with_annealing(annealing);
87
88    println!("   Created Quantum RBM with annealing schedule");
89
90    // Generate correlated binary data
91    let data = generate_correlated_data(200, 6);
92
93    // Train with PCD
94    println!("   Training with Persistent Contrastive Divergence...");
95    let losses = rbm.train_pcd(
96        &data, 100, // epochs
97        20,  // batch size
98        50,  // persistent chains
99    )?;
100
101    // Analyze training
102    let improvement = (losses[0] - losses.last().unwrap()) / losses[0] * 100.0;
103    println!("   Training statistics:");
104    println!("   - Loss reduction: {improvement:.1}%");
105    println!("   - Final temperature: 0.5");
106
107    // Test reconstruction
108    let test_data = data.slice(s![0..5, ..]).to_owned();
109    let reconstructed = rbm.qbm().reconstruct(&test_data)?;
110
111    println!("\n   Reconstruction quality:");
112    for i in 0..3 {
113        print!("   Original:      [");
114        for val in test_data.row(i) {
115            print!("{val:.0} ");
116        }
117        print!("]  →  Reconstructed: [");
118        for val in reconstructed.row(i) {
119            print!("{val:.0} ");
120        }
121        println!("]");
122    }
123
124    Ok(())
125}
126
127/// Deep Boltzmann Machine demonstration
128fn deep_boltzmann_demo() -> Result<()> {
129    // Create a 3-layer DBM
130    let layer_sizes = vec![8, 4, 2];
131    let mut dbm = DeepBoltzmannMachine::new(
132        layer_sizes.clone(),
133        1.0,  // temperature
134        0.01, // learning rate
135    )?;
136
137    println!("   Created Deep Boltzmann Machine:");
138    println!("   - Architecture: {layer_sizes:?}");
139    println!("   - Total layers: {}", dbm.rbms().len());
140
141    // Generate hierarchical data
142    let data = generate_hierarchical_data(300, 8);
143
144    // Layer-wise pretraining
145    println!("\n   Performing layer-wise pretraining...");
146    dbm.pretrain(
147        &data, 50, // epochs per layer
148        30, // batch size
149    )?;
150
151    println!("\n   Pretraining complete!");
152    println!("   Each layer learned increasingly abstract features");
153
154    Ok(())
155}
156
157/// Energy landscape visualization
158fn energy_landscape_demo() -> Result<()> {
159    // Create small QBM for visualization
160    let qbm = QuantumBoltzmannMachine::new(
161        2,    // visible units (for 2D visualization)
162        1,    // hidden unit
163        0.5,  // temperature
164        0.01, // learning rate
165    )?;
166
167    println!("   Analyzing energy landscape of 2-unit system");
168
169    // Compute energy for all 4 possible states
170    let states = [
171        Array1::from_vec(vec![0.0, 0.0]),
172        Array1::from_vec(vec![0.0, 1.0]),
173        Array1::from_vec(vec![1.0, 0.0]),
174        Array1::from_vec(vec![1.0, 1.0]),
175    ];
176
177    println!("\n   State energies:");
178    for (i, state) in states.iter().enumerate() {
179        let energy = qbm.energy(state);
180        let prob = (-energy / qbm.temperature()).exp();
181        println!(
182            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
183            state[0], state[1], energy, prob
184        );
185    }
186
187    // Show coupling matrix
188    println!("\n   Coupling matrix:");
189    for i in 0..3 {
190        print!("   [");
191        for j in 0..3 {
192            print!("{:6.3} ", qbm.couplings()[[i, j]]);
193        }
194        println!("]");
195    }
196
197    Ok(())
198}
Source

pub fn energy(&self, state: &Array1<f64>) -> f64

Compute energy of a configuration

Examples found in repository?
examples/quantum_boltzmann.rs (line 179)
158fn energy_landscape_demo() -> Result<()> {
159    // Create small QBM for visualization
160    let qbm = QuantumBoltzmannMachine::new(
161        2,    // visible units (for 2D visualization)
162        1,    // hidden unit
163        0.5,  // temperature
164        0.01, // learning rate
165    )?;
166
167    println!("   Analyzing energy landscape of 2-unit system");
168
169    // Compute energy for all 4 possible states
170    let states = [
171        Array1::from_vec(vec![0.0, 0.0]),
172        Array1::from_vec(vec![0.0, 1.0]),
173        Array1::from_vec(vec![1.0, 0.0]),
174        Array1::from_vec(vec![1.0, 1.0]),
175    ];
176
177    println!("\n   State energies:");
178    for (i, state) in states.iter().enumerate() {
179        let energy = qbm.energy(state);
180        let prob = (-energy / qbm.temperature()).exp();
181        println!(
182            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
183            state[0], state[1], energy, prob
184        );
185    }
186
187    // Show coupling matrix
188    println!("\n   Coupling matrix:");
189    for i in 0..3 {
190        print!("   [");
191        for j in 0..3 {
192            print!("{:6.3} ", qbm.couplings()[[i, j]]);
193        }
194        println!("]");
195    }
196
197    Ok(())
198}
Source

pub fn create_gibbs_circuit(&self) -> Result<()>

Create quantum circuit for Gibbs state preparation

Source

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

Sample from the Boltzmann distribution

Examples found in repository?
examples/quantum_boltzmann.rs (line 62)
39fn basic_qbm_demo() -> Result<()> {
40    // Create a small QBM
41    let mut qbm = QuantumBoltzmannMachine::new(
42        4,    // visible units
43        2,    // hidden units
44        1.0,  // temperature
45        0.01, // learning rate
46    )?;
47
48    println!("   Created QBM with 4 visible and 2 hidden units");
49
50    // Generate synthetic binary data
51    let data = generate_binary_patterns(100, 4);
52
53    // Train the QBM
54    println!("   Training on binary patterns...");
55    let losses = qbm.train(&data, 50, 10)?;
56
57    println!("   Training complete:");
58    println!("   - Initial loss: {:.4}", losses[0]);
59    println!("   - Final loss: {:.4}", losses.last().unwrap());
60
61    // Sample from trained model
62    let samples = qbm.sample(5)?;
63    println!("\n   Generated samples:");
64    for (i, sample) in samples.outer_iter().enumerate() {
65        print!("   Sample {}: [", i + 1);
66        for val in sample {
67            print!("{val:.0} ");
68        }
69        println!("]");
70    }
71
72    Ok(())
73}
Source

pub fn compute_gradients( &self, data: &Array2<f64>, ) -> Result<(Array2<f64>, Array1<f64>)>

Compute gradients using contrastive divergence

Source

pub fn sample_hidden_given_visible( &self, visible: &ArrayView1<'_, f64>, ) -> Result<Array1<f64>>

Sample hidden units given visible units

Examples found in repository?
examples/quantum_boltzmann.rs (line 361)
356fn complete_pattern(rbm: &QuantumRBM, partial: &Array1<f64>) -> Result<Array1<f64>> {
357    // Use Gibbs sampling to complete pattern
358    let mut current = partial.clone();
359
360    for _ in 0..10 {
361        let hidden = rbm.qbm().sample_hidden_given_visible(&current.view())?;
362        current = rbm.qbm().sample_visible_given_hidden(&hidden)?;
363    }
364
365    Ok(current)
366}
Source

pub fn train( &mut self, data: &Array2<f64>, epochs: usize, batch_size: usize, ) -> Result<Vec<f64>>

Train the Boltzmann machine

Examples found in repository?
examples/quantum_boltzmann.rs (line 55)
39fn basic_qbm_demo() -> Result<()> {
40    // Create a small QBM
41    let mut qbm = QuantumBoltzmannMachine::new(
42        4,    // visible units
43        2,    // hidden units
44        1.0,  // temperature
45        0.01, // learning rate
46    )?;
47
48    println!("   Created QBM with 4 visible and 2 hidden units");
49
50    // Generate synthetic binary data
51    let data = generate_binary_patterns(100, 4);
52
53    // Train the QBM
54    println!("   Training on binary patterns...");
55    let losses = qbm.train(&data, 50, 10)?;
56
57    println!("   Training complete:");
58    println!("   - Initial loss: {:.4}", losses[0]);
59    println!("   - Final loss: {:.4}", losses.last().unwrap());
60
61    // Sample from trained model
62    let samples = qbm.sample(5)?;
63    println!("\n   Generated samples:");
64    for (i, sample) in samples.outer_iter().enumerate() {
65        print!("   Sample {}: [", i + 1);
66        for val in sample {
67            print!("{val:.0} ");
68        }
69        println!("]");
70    }
71
72    Ok(())
73}
Source

pub fn reconstruct(&self, visible: &Array2<f64>) -> Result<Array2<f64>>

Reconstruct visible units

Examples found in repository?
examples/quantum_boltzmann.rs (line 109)
76fn rbm_demo() -> Result<()> {
77    // Create RBM with annealing
78    let annealing = AnnealingSchedule::new(2.0, 0.5, 100);
79
80    let mut rbm = QuantumRBM::new(
81        6,    // visible units
82        3,    // hidden units
83        2.0,  // initial temperature
84        0.01, // learning rate
85    )?
86    .with_annealing(annealing);
87
88    println!("   Created Quantum RBM with annealing schedule");
89
90    // Generate correlated binary data
91    let data = generate_correlated_data(200, 6);
92
93    // Train with PCD
94    println!("   Training with Persistent Contrastive Divergence...");
95    let losses = rbm.train_pcd(
96        &data, 100, // epochs
97        20,  // batch size
98        50,  // persistent chains
99    )?;
100
101    // Analyze training
102    let improvement = (losses[0] - losses.last().unwrap()) / losses[0] * 100.0;
103    println!("   Training statistics:");
104    println!("   - Loss reduction: {improvement:.1}%");
105    println!("   - Final temperature: 0.5");
106
107    // Test reconstruction
108    let test_data = data.slice(s![0..5, ..]).to_owned();
109    let reconstructed = rbm.qbm().reconstruct(&test_data)?;
110
111    println!("\n   Reconstruction quality:");
112    for i in 0..3 {
113        print!("   Original:      [");
114        for val in test_data.row(i) {
115            print!("{val:.0} ");
116        }
117        print!("]  →  Reconstructed: [");
118        for val in reconstructed.row(i) {
119            print!("{val:.0} ");
120        }
121        println!("]");
122    }
123
124    Ok(())
125}
Source

pub fn sample_visible_given_hidden( &self, hidden: &Array1<f64>, ) -> Result<Array1<f64>>

Sample visible units given hidden units

Examples found in repository?
examples/quantum_boltzmann.rs (line 362)
356fn complete_pattern(rbm: &QuantumRBM, partial: &Array1<f64>) -> Result<Array1<f64>> {
357    // Use Gibbs sampling to complete pattern
358    let mut current = partial.clone();
359
360    for _ in 0..10 {
361        let hidden = rbm.qbm().sample_hidden_given_visible(&current.view())?;
362        current = rbm.qbm().sample_visible_given_hidden(&hidden)?;
363    }
364
365    Ok(current)
366}
Source

pub fn temperature(&self) -> f64

Get temperature

Examples found in repository?
examples/quantum_boltzmann.rs (line 180)
158fn energy_landscape_demo() -> Result<()> {
159    // Create small QBM for visualization
160    let qbm = QuantumBoltzmannMachine::new(
161        2,    // visible units (for 2D visualization)
162        1,    // hidden unit
163        0.5,  // temperature
164        0.01, // learning rate
165    )?;
166
167    println!("   Analyzing energy landscape of 2-unit system");
168
169    // Compute energy for all 4 possible states
170    let states = [
171        Array1::from_vec(vec![0.0, 0.0]),
172        Array1::from_vec(vec![0.0, 1.0]),
173        Array1::from_vec(vec![1.0, 0.0]),
174        Array1::from_vec(vec![1.0, 1.0]),
175    ];
176
177    println!("\n   State energies:");
178    for (i, state) in states.iter().enumerate() {
179        let energy = qbm.energy(state);
180        let prob = (-energy / qbm.temperature()).exp();
181        println!(
182            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
183            state[0], state[1], energy, prob
184        );
185    }
186
187    // Show coupling matrix
188    println!("\n   Coupling matrix:");
189    for i in 0..3 {
190        print!("   [");
191        for j in 0..3 {
192            print!("{:6.3} ", qbm.couplings()[[i, j]]);
193        }
194        println!("]");
195    }
196
197    Ok(())
198}
Source

pub fn couplings(&self) -> &Array2<f64>

Get couplings matrix

Examples found in repository?
examples/quantum_boltzmann.rs (line 192)
158fn energy_landscape_demo() -> Result<()> {
159    // Create small QBM for visualization
160    let qbm = QuantumBoltzmannMachine::new(
161        2,    // visible units (for 2D visualization)
162        1,    // hidden unit
163        0.5,  // temperature
164        0.01, // learning rate
165    )?;
166
167    println!("   Analyzing energy landscape of 2-unit system");
168
169    // Compute energy for all 4 possible states
170    let states = [
171        Array1::from_vec(vec![0.0, 0.0]),
172        Array1::from_vec(vec![0.0, 1.0]),
173        Array1::from_vec(vec![1.0, 0.0]),
174        Array1::from_vec(vec![1.0, 1.0]),
175    ];
176
177    println!("\n   State energies:");
178    for (i, state) in states.iter().enumerate() {
179        let energy = qbm.energy(state);
180        let prob = (-energy / qbm.temperature()).exp();
181        println!(
182            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
183            state[0], state[1], energy, prob
184        );
185    }
186
187    // Show coupling matrix
188    println!("\n   Coupling matrix:");
189    for i in 0..3 {
190        print!("   [");
191        for j in 0..3 {
192            print!("{:6.3} ", qbm.couplings()[[i, j]]);
193        }
194        println!("]");
195    }
196
197    Ok(())
198}

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