QuantumBoltzmannMachine

quantrs2_ml::boltzmann

Struct QuantumBoltzmannMachine 

Source 
pub struct QuantumBoltzmannMachine { /* private fields */ }
Expand description

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 40-45)
38fn basic_qbm_demo() -> Result<()> {
39    // Create a small QBM
40    let mut qbm = QuantumBoltzmannMachine::new(
41        4,    // visible units
42        2,    // hidden units
43        1.0,  // temperature
44        0.01, // learning rate
45    )?;
46
47    println!("   Created QBM with 4 visible and 2 hidden units");
48
49    // Generate synthetic binary data
50    let data = generate_binary_patterns(100, 4);
51
52    // Train the QBM
53    println!("   Training on binary patterns...");
54    let losses = qbm.train(&data, 50, 10)?;
55
56    println!("   Training complete:");
57    println!("   - Initial loss: {:.4}", losses[0]);
58    println!("   - Final loss: {:.4}", losses.last().unwrap());
59
60    // Sample from trained model
61    let samples = qbm.sample(5)?;
62    println!("\n   Generated samples:");
63    for (i, sample) in samples.outer_iter().enumerate() {
64        print!("   Sample {}: [", i + 1);
65        for val in sample.iter() {
66            print!("{:.0} ", val);
67        }
68        println!("]");
69    }
70
71    Ok(())
72}
73
74/// RBM demonstration with persistent contrastive divergence
75fn rbm_demo() -> Result<()> {
76    // Create RBM with annealing
77    let annealing = AnnealingSchedule::new(2.0, 0.5, 100);
78
79    let mut rbm = QuantumRBM::new(
80        6,    // visible units
81        3,    // hidden units
82        2.0,  // initial temperature
83        0.01, // learning rate
84    )?
85    .with_annealing(annealing);
86
87    println!("   Created Quantum RBM with annealing schedule");
88
89    // Generate correlated binary data
90    let data = generate_correlated_data(200, 6);
91
92    // Train with PCD
93    println!("   Training with Persistent Contrastive Divergence...");
94    let losses = rbm.train_pcd(
95        &data, 100, // epochs
96        20,  // batch size
97        50,  // persistent chains
98    )?;
99
100    // Analyze training
101    let improvement = (losses[0] - losses.last().unwrap()) / losses[0] * 100.0;
102    println!("   Training statistics:");
103    println!("   - Loss reduction: {:.1}%", improvement);
104    println!("   - Final temperature: 0.5");
105
106    // Test reconstruction
107    let test_data = data.slice(s![0..5, ..]).to_owned();
108    let reconstructed = rbm.qbm().reconstruct(&test_data)?;
109
110    println!("\n   Reconstruction quality:");
111    for i in 0..3 {
112        print!("   Original:      [");
113        for val in test_data.row(i).iter() {
114            print!("{:.0} ", val);
115        }
116        print!("]  →  Reconstructed: [");
117        for val in reconstructed.row(i).iter() {
118            print!("{:.0} ", val);
119        }
120        println!("]");
121    }
122
123    Ok(())
124}
125
126/// Deep Boltzmann Machine demonstration
127fn deep_boltzmann_demo() -> Result<()> {
128    // Create a 3-layer DBM
129    let layer_sizes = vec![8, 4, 2];
130    let mut dbm = DeepBoltzmannMachine::new(
131        layer_sizes.clone(),
132        1.0,  // temperature
133        0.01, // learning rate
134    )?;
135
136    println!("   Created Deep Boltzmann Machine:");
137    println!("   - Architecture: {:?}", layer_sizes);
138    println!("   - Total layers: {}", dbm.rbms().len());
139
140    // Generate hierarchical data
141    let data = generate_hierarchical_data(300, 8);
142
143    // Layer-wise pretraining
144    println!("\n   Performing layer-wise pretraining...");
145    dbm.pretrain(
146        &data, 50, // epochs per layer
147        30, // batch size
148    )?;
149
150    println!("\n   Pretraining complete!");
151    println!("   Each layer learned increasingly abstract features");
152
153    Ok(())
154}
155
156/// Energy landscape visualization
157fn energy_landscape_demo() -> Result<()> {
158    // Create small QBM for visualization
159    let qbm = QuantumBoltzmannMachine::new(
160        2,    // visible units (for 2D visualization)
161        1,    // hidden unit
162        0.5,  // temperature
163        0.01, // learning rate
164    )?;
165
166    println!("   Analyzing energy landscape of 2-unit system");
167
168    // Compute energy for all 4 possible states
169    let states = vec![
170        Array1::from_vec(vec![0.0, 0.0]),
171        Array1::from_vec(vec![0.0, 1.0]),
172        Array1::from_vec(vec![1.0, 0.0]),
173        Array1::from_vec(vec![1.0, 1.0]),
174    ];
175
176    println!("\n   State energies:");
177    for (i, state) in states.iter().enumerate() {
178        let energy = qbm.energy(state);
179        let prob = (-energy / qbm.temperature()).exp();
180        println!(
181            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
182            state[0], state[1], energy, prob
183        );
184    }
185
186    // Show coupling matrix
187    println!("\n   Coupling matrix:");
188    for i in 0..3 {
189        print!("   [");
190        for j in 0..3 {
191            print!("{:6.3} ", qbm.couplings()[[i, j]]);
192        }
193        println!("]");
194    }
195
196    Ok(())
197}
Source

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

Compute energy of a configuration

Examples found in repository?
examples/quantum_boltzmann.rs (line 178)
157fn energy_landscape_demo() -> Result<()> {
158    // Create small QBM for visualization
159    let qbm = QuantumBoltzmannMachine::new(
160        2,    // visible units (for 2D visualization)
161        1,    // hidden unit
162        0.5,  // temperature
163        0.01, // learning rate
164    )?;
165
166    println!("   Analyzing energy landscape of 2-unit system");
167
168    // Compute energy for all 4 possible states
169    let states = vec![
170        Array1::from_vec(vec![0.0, 0.0]),
171        Array1::from_vec(vec![0.0, 1.0]),
172        Array1::from_vec(vec![1.0, 0.0]),
173        Array1::from_vec(vec![1.0, 1.0]),
174    ];
175
176    println!("\n   State energies:");
177    for (i, state) in states.iter().enumerate() {
178        let energy = qbm.energy(state);
179        let prob = (-energy / qbm.temperature()).exp();
180        println!(
181            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
182            state[0], state[1], energy, prob
183        );
184    }
185
186    // Show coupling matrix
187    println!("\n   Coupling matrix:");
188    for i in 0..3 {
189        print!("   [");
190        for j in 0..3 {
191            print!("{:6.3} ", qbm.couplings()[[i, j]]);
192        }
193        println!("]");
194    }
195
196    Ok(())
197}
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 61)
38fn basic_qbm_demo() -> Result<()> {
39    // Create a small QBM
40    let mut qbm = QuantumBoltzmannMachine::new(
41        4,    // visible units
42        2,    // hidden units
43        1.0,  // temperature
44        0.01, // learning rate
45    )?;
46
47    println!("   Created QBM with 4 visible and 2 hidden units");
48
49    // Generate synthetic binary data
50    let data = generate_binary_patterns(100, 4);
51
52    // Train the QBM
53    println!("   Training on binary patterns...");
54    let losses = qbm.train(&data, 50, 10)?;
55
56    println!("   Training complete:");
57    println!("   - Initial loss: {:.4}", losses[0]);
58    println!("   - Final loss: {:.4}", losses.last().unwrap());
59
60    // Sample from trained model
61    let samples = qbm.sample(5)?;
62    println!("\n   Generated samples:");
63    for (i, sample) in samples.outer_iter().enumerate() {
64        print!("   Sample {}: [", i + 1);
65        for val in sample.iter() {
66            print!("{:.0} ", val);
67        }
68        println!("]");
69    }
70
71    Ok(())
72}
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 360)
355fn complete_pattern(rbm: &QuantumRBM, partial: &Array1<f64>) -> Result<Array1<f64>> {
356    // Use Gibbs sampling to complete pattern
357    let mut current = partial.clone();
358
359    for _ in 0..10 {
360        let hidden = rbm.qbm().sample_hidden_given_visible(&current.view())?;
361        current = rbm.qbm().sample_visible_given_hidden(&hidden)?;
362    }
363
364    Ok(current)
365}
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 54)
38fn basic_qbm_demo() -> Result<()> {
39    // Create a small QBM
40    let mut qbm = QuantumBoltzmannMachine::new(
41        4,    // visible units
42        2,    // hidden units
43        1.0,  // temperature
44        0.01, // learning rate
45    )?;
46
47    println!("   Created QBM with 4 visible and 2 hidden units");
48
49    // Generate synthetic binary data
50    let data = generate_binary_patterns(100, 4);
51
52    // Train the QBM
53    println!("   Training on binary patterns...");
54    let losses = qbm.train(&data, 50, 10)?;
55
56    println!("   Training complete:");
57    println!("   - Initial loss: {:.4}", losses[0]);
58    println!("   - Final loss: {:.4}", losses.last().unwrap());
59
60    // Sample from trained model
61    let samples = qbm.sample(5)?;
62    println!("\n   Generated samples:");
63    for (i, sample) in samples.outer_iter().enumerate() {
64        print!("   Sample {}: [", i + 1);
65        for val in sample.iter() {
66            print!("{:.0} ", val);
67        }
68        println!("]");
69    }
70
71    Ok(())
72}
Source

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

Reconstruct visible units

Examples found in repository?
examples/quantum_boltzmann.rs (line 108)
75fn rbm_demo() -> Result<()> {
76    // Create RBM with annealing
77    let annealing = AnnealingSchedule::new(2.0, 0.5, 100);
78
79    let mut rbm = QuantumRBM::new(
80        6,    // visible units
81        3,    // hidden units
82        2.0,  // initial temperature
83        0.01, // learning rate
84    )?
85    .with_annealing(annealing);
86
87    println!("   Created Quantum RBM with annealing schedule");
88
89    // Generate correlated binary data
90    let data = generate_correlated_data(200, 6);
91
92    // Train with PCD
93    println!("   Training with Persistent Contrastive Divergence...");
94    let losses = rbm.train_pcd(
95        &data, 100, // epochs
96        20,  // batch size
97        50,  // persistent chains
98    )?;
99
100    // Analyze training
101    let improvement = (losses[0] - losses.last().unwrap()) / losses[0] * 100.0;
102    println!("   Training statistics:");
103    println!("   - Loss reduction: {:.1}%", improvement);
104    println!("   - Final temperature: 0.5");
105
106    // Test reconstruction
107    let test_data = data.slice(s![0..5, ..]).to_owned();
108    let reconstructed = rbm.qbm().reconstruct(&test_data)?;
109
110    println!("\n   Reconstruction quality:");
111    for i in 0..3 {
112        print!("   Original:      [");
113        for val in test_data.row(i).iter() {
114            print!("{:.0} ", val);
115        }
116        print!("]  →  Reconstructed: [");
117        for val in reconstructed.row(i).iter() {
118            print!("{:.0} ", val);
119        }
120        println!("]");
121    }
122
123    Ok(())
124}
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 361)
355fn complete_pattern(rbm: &QuantumRBM, partial: &Array1<f64>) -> Result<Array1<f64>> {
356    // Use Gibbs sampling to complete pattern
357    let mut current = partial.clone();
358
359    for _ in 0..10 {
360        let hidden = rbm.qbm().sample_hidden_given_visible(&current.view())?;
361        current = rbm.qbm().sample_visible_given_hidden(&hidden)?;
362    }
363
364    Ok(current)
365}
Source

pub fn temperature(&self) -> f64

Get temperature

Examples found in repository?
examples/quantum_boltzmann.rs (line 179)
157fn energy_landscape_demo() -> Result<()> {
158    // Create small QBM for visualization
159    let qbm = QuantumBoltzmannMachine::new(
160        2,    // visible units (for 2D visualization)
161        1,    // hidden unit
162        0.5,  // temperature
163        0.01, // learning rate
164    )?;
165
166    println!("   Analyzing energy landscape of 2-unit system");
167
168    // Compute energy for all 4 possible states
169    let states = vec![
170        Array1::from_vec(vec![0.0, 0.0]),
171        Array1::from_vec(vec![0.0, 1.0]),
172        Array1::from_vec(vec![1.0, 0.0]),
173        Array1::from_vec(vec![1.0, 1.0]),
174    ];
175
176    println!("\n   State energies:");
177    for (i, state) in states.iter().enumerate() {
178        let energy = qbm.energy(state);
179        let prob = (-energy / qbm.temperature()).exp();
180        println!(
181            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
182            state[0], state[1], energy, prob
183        );
184    }
185
186    // Show coupling matrix
187    println!("\n   Coupling matrix:");
188    for i in 0..3 {
189        print!("   [");
190        for j in 0..3 {
191            print!("{:6.3} ", qbm.couplings()[[i, j]]);
192        }
193        println!("]");
194    }
195
196    Ok(())
197}
Source

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

Get couplings matrix

Examples found in repository?
examples/quantum_boltzmann.rs (line 191)
157fn energy_landscape_demo() -> Result<()> {
158    // Create small QBM for visualization
159    let qbm = QuantumBoltzmannMachine::new(
160        2,    // visible units (for 2D visualization)
161        1,    // hidden unit
162        0.5,  // temperature
163        0.01, // learning rate
164    )?;
165
166    println!("   Analyzing energy landscape of 2-unit system");
167
168    // Compute energy for all 4 possible states
169    let states = vec![
170        Array1::from_vec(vec![0.0, 0.0]),
171        Array1::from_vec(vec![0.0, 1.0]),
172        Array1::from_vec(vec![1.0, 0.0]),
173        Array1::from_vec(vec![1.0, 1.0]),
174    ];
175
176    println!("\n   State energies:");
177    for (i, state) in states.iter().enumerate() {
178        let energy = qbm.energy(state);
179        let prob = (-energy / qbm.temperature()).exp();
180        println!(
181            "   State [{:.0}, {:.0}]: E = {:.3}, P ∝ {:.3}",
182            state[0], state[1], energy, prob
183        );
184    }
185
186    // Show coupling matrix
187    println!("\n   Coupling matrix:");
188    for i in 0..3 {
189        print!("   [");
190        for j in 0..3 {
191            print!("{:6.3} ", qbm.couplings()[[i, j]]);
192        }
193        println!("]");
194    }
195
196    Ok(())
197}

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