quantrs2_ml/
qnn.rs

1use crate::error::{MLError, Result};
2use crate::optimization::Optimizer;
3use quantrs2_circuit::builder::Simulator;
4use quantrs2_circuit::prelude::Circuit;
5use quantrs2_sim::statevector::StateVectorSimulator;
6use scirs2_core::ndarray::{Array1, Array2};
7use scirs2_core::random::prelude::*;
8use std::fmt;
9
10/// Activation function types for quantum layers
11#[derive(Debug, Clone, Copy, PartialEq)]
12pub enum ActivationType {
13    /// Linear activation (identity)
14    Linear,
15    /// ReLU activation
16    ReLU,
17    /// Sigmoid activation
18    Sigmoid,
19    /// Tanh activation
20    Tanh,
21}
22
23/// Represents a layer type in a quantum neural network
24#[derive(Debug, Clone)]
25pub enum QNNLayerType {
26    /// Encoding layer for converting classical data to quantum states
27    EncodingLayer {
28        /// Number of classical features to encode
29        num_features: usize,
30    },
31
32    /// Variational layer with trainable parameters
33    VariationalLayer {
34        /// Number of trainable parameters
35        num_params: usize,
36    },
37
38    /// Entanglement layer to create entanglement between qubits
39    EntanglementLayer {
40        /// Connectivity pattern, e.g., "full", "linear", "circular"
41        connectivity: String,
42    },
43
44    /// Measurement layer to extract classical information
45    MeasurementLayer {
46        /// Measurement basis, e.g., "computational", "Pauli-X", "Pauli-Y", "Pauli-Z"
47        measurement_basis: String,
48    },
49}
50
51/// Results from training a quantum neural network
52#[derive(Debug, Clone)]
53pub struct TrainingResult {
54    /// Final loss value after training
55    pub final_loss: f64,
56
57    /// Training accuracy (for classification tasks)
58    pub accuracy: f64,
59
60    /// Loss history during training
61    pub loss_history: Vec<f64>,
62
63    /// Optimal parameters found during training
64    pub optimal_parameters: Array1<f64>,
65}
66
67/// Represents a quantum neural network
68#[derive(Debug, Clone)]
69pub struct QuantumNeuralNetwork {
70    /// The layers that make up the network
71    pub layers: Vec<QNNLayerType>,
72
73    /// The number of qubits used in the network
74    pub num_qubits: usize,
75
76    /// The dimension of the input data
77    pub input_dim: usize,
78
79    /// The dimension of the output data
80    pub output_dim: usize,
81
82    /// Network parameters (weights)
83    pub parameters: Array1<f64>,
84}
85
86impl QuantumNeuralNetwork {
87    /// Creates a new quantum neural network
88    pub fn new(
89        layers: Vec<QNNLayerType>,
90        num_qubits: usize,
91        input_dim: usize,
92        output_dim: usize,
93    ) -> Result<Self> {
94        // Validate the layers and structure
95        if layers.is_empty() {
96            return Err(MLError::ModelCreationError(
97                "QNN must have at least one layer".to_string(),
98            ));
99        }
100
101        // Determine parameter count from variational layers
102        let num_params = layers
103            .iter()
104            .filter_map(|layer| match layer {
105                QNNLayerType::VariationalLayer { num_params } => Some(num_params),
106                _ => None,
107            })
108            .sum::<usize>();
109
110        // Create random initial parameters
111        let parameters = Array1::from_vec(
112            (0..num_params)
113                .map(|_| thread_rng().gen::<f64>() * 2.0 * std::f64::consts::PI)
114                .collect(),
115        );
116
117        Ok(QuantumNeuralNetwork {
118            layers,
119            num_qubits,
120            input_dim,
121            output_dim,
122            parameters,
123        })
124    }
125
126    /// Creates a quantum circuit representation of the network for a given input
127    fn create_circuit(&self, input: &Array1<f64>) -> Result<Circuit<4>> {
128        // In a real implementation, this would create a proper circuit based on the layers
129        // For now, we'll create a dummy circuit with maximum 4 qubits to avoid memory issues
130        let mut circuit = Circuit::<4>::new();
131
132        // Apply dummy gates to demonstrate the concept
133        for i in 0..self.num_qubits.min(4) {
134            circuit.h(i)?;
135        }
136
137        Ok(circuit)
138    }
139
140    /// Runs the network on a given input
141    pub fn forward(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
142        // For now, this is a dummy implementation
143        let circuit = self.create_circuit(input)?;
144
145        // Run the circuit
146        let simulator = StateVectorSimulator::new();
147        let _result = simulator.run(&circuit)?;
148
149        // Process the result to get the output
150        let output = Array1::zeros(self.output_dim);
151
152        Ok(output)
153    }
154
155    /// Trains the network on a dataset
156    pub fn train(
157        &mut self,
158        x_train: &Array2<f64>,
159        y_train: &Array2<f64>,
160        epochs: usize,
161        learning_rate: f64,
162    ) -> Result<TrainingResult> {
163        // This is a dummy implementation
164        let loss_history = vec![0.5, 0.4, 0.3, 0.25, 0.2];
165
166        Ok(TrainingResult {
167            final_loss: 0.2,
168            accuracy: 0.85,
169            loss_history,
170            optimal_parameters: self.parameters.clone(),
171        })
172    }
173
174    /// Trains the network on a dataset with 1D labels (compatibility method)
175    pub fn train_1d(
176        &mut self,
177        x_train: &Array2<f64>,
178        y_train: &Array1<f64>,
179        epochs: usize,
180        learning_rate: f64,
181    ) -> Result<TrainingResult> {
182        // Convert 1D labels to 2D
183        let y_2d = y_train.clone().into_shape((y_train.len(), 1)).unwrap();
184        self.train(x_train, &y_2d, epochs, learning_rate)
185    }
186
187    /// Predicts the output for a given input
188    pub fn predict(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
189        self.forward(input)
190    }
191
192    /// Predicts the output for a batch of inputs
193    pub fn predict_batch(&self, inputs: &Array2<f64>) -> Result<Array2<f64>> {
194        let batch_size = inputs.nrows();
195        let mut outputs = Array2::zeros((batch_size, self.output_dim));
196
197        for (i, row) in inputs.axis_iter(scirs2_core::ndarray::Axis(0)).enumerate() {
198            let input = row.to_owned();
199            let output = self.predict(&input)?;
200            outputs.row_mut(i).assign(&output);
201        }
202
203        Ok(outputs)
204    }
205}
206
207/// Builder for quantum neural networks
208#[derive(Debug, Clone)]
209pub struct QNNBuilder {
210    layers: Vec<QNNLayerType>,
211    num_qubits: usize,
212    input_dim: usize,
213    output_dim: usize,
214}
215
216impl QNNBuilder {
217    /// Creates a new QNN builder
218    pub fn new() -> Self {
219        QNNBuilder {
220            layers: Vec::new(),
221            num_qubits: 0,
222            input_dim: 0,
223            output_dim: 0,
224        }
225    }
226
227    /// Sets the number of qubits
228    pub fn with_qubits(mut self, num_qubits: usize) -> Self {
229        self.num_qubits = num_qubits;
230        self
231    }
232
233    /// Sets the input dimension
234    pub fn with_input_dim(mut self, input_dim: usize) -> Self {
235        self.input_dim = input_dim;
236        self
237    }
238
239    /// Sets the output dimension
240    pub fn with_output_dim(mut self, output_dim: usize) -> Self {
241        self.output_dim = output_dim;
242        self
243    }
244
245    /// Adds an encoding layer
246    pub fn add_encoding_layer(mut self, num_features: usize) -> Self {
247        self.layers
248            .push(QNNLayerType::EncodingLayer { num_features });
249        self
250    }
251
252    /// Adds a layer (alias for add_encoding_layer for compatibility)
253    pub fn add_layer(self, size: usize) -> Self {
254        self.add_encoding_layer(size)
255    }
256
257    /// Adds a variational layer
258    pub fn add_variational_layer(mut self, num_params: usize) -> Self {
259        self.layers
260            .push(QNNLayerType::VariationalLayer { num_params });
261        self
262    }
263
264    /// Adds an entanglement layer
265    pub fn add_entanglement_layer(mut self, connectivity: &str) -> Self {
266        self.layers.push(QNNLayerType::EntanglementLayer {
267            connectivity: connectivity.to_string(),
268        });
269        self
270    }
271
272    /// Adds a measurement layer
273    pub fn add_measurement_layer(mut self, measurement_basis: &str) -> Self {
274        self.layers.push(QNNLayerType::MeasurementLayer {
275            measurement_basis: measurement_basis.to_string(),
276        });
277        self
278    }
279
280    /// Builds the quantum neural network
281    pub fn build(self) -> Result<QuantumNeuralNetwork> {
282        if self.num_qubits == 0 {
283            return Err(MLError::ModelCreationError(
284                "Number of qubits must be greater than 0".to_string(),
285            ));
286        }
287
288        if self.input_dim == 0 {
289            return Err(MLError::ModelCreationError(
290                "Input dimension must be greater than 0".to_string(),
291            ));
292        }
293
294        if self.output_dim == 0 {
295            return Err(MLError::ModelCreationError(
296                "Output dimension must be greater than 0".to_string(),
297            ));
298        }
299
300        if self.layers.is_empty() {
301            return Err(MLError::ModelCreationError(
302                "QNN must have at least one layer".to_string(),
303            ));
304        }
305
306        QuantumNeuralNetwork::new(
307            self.layers,
308            self.num_qubits,
309            self.input_dim,
310            self.output_dim,
311        )
312    }
313}
314
315impl fmt::Display for QNNLayerType {
316    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
317        match self {
318            QNNLayerType::EncodingLayer { num_features } => {
319                write!(f, "Encoding Layer (features: {})", num_features)
320            }
321            QNNLayerType::VariationalLayer { num_params } => {
322                write!(f, "Variational Layer (parameters: {})", num_params)
323            }
324            QNNLayerType::EntanglementLayer { connectivity } => {
325                write!(f, "Entanglement Layer (connectivity: {})", connectivity)
326            }
327            QNNLayerType::MeasurementLayer { measurement_basis } => {
328                write!(f, "Measurement Layer (basis: {})", measurement_basis)
329            }
330        }
331    }
332}
333
334/// Quantum neural network layer for use in other modules
335#[derive(Debug, Clone)]
336pub struct QNNLayer {
337    /// Input dimension
338    pub input_dim: usize,
339    /// Output dimension
340    pub output_dim: usize,
341    /// Activation function
342    pub activation: ActivationType,
343}
344
345impl QNNLayer {
346    /// Create a new QNN layer
347    pub fn new(input_dim: usize, output_dim: usize, activation: ActivationType) -> Self {
348        Self {
349            input_dim,
350            output_dim,
351            activation,
352        }
353    }
354}
quantrs2_ml/qnn.rs

quantrs2_ml/
qnn.rs
