trustformers-models 0.1.1

Model implementations for TrustformeRS
Documentation 
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use trustformers_core::{
    errors::Result,
    layers::{LayerNorm, Linear},
    quantum::{QuantumAnsatz, QuantumNeuralLayer},
    tensor::Tensor,
    traits::Layer,
};

use super::{config::QuantumClassicalConfig, model::QuantumClassicalModelOutput};

/// Quantum convolutional neural network
#[derive(Debug)]
pub struct QuantumConvolutionalNN {
    /// Configuration
    pub config: QuantumClassicalConfig,
    /// Convolutional layers
    pub conv_layers: Vec<Linear>,
    /// Quantum pooling layers
    pub quantum_pooling_layers: Vec<QuantumNeuralLayer>,
    /// Fully connected layers
    pub fc_layers: Vec<Linear>,
    /// Layer normalization
    pub layer_norms: Vec<LayerNorm>,
    /// Output projection
    pub output_projection: Linear,
}

impl QuantumConvolutionalNN {
    /// Create a new quantum CNN
    pub fn new(config: &QuantumClassicalConfig) -> Result<Self> {
        let mut conv_layers = Vec::new();
        let mut quantum_pooling_layers = Vec::new();
        let mut fc_layers = Vec::new();
        let mut layer_norms = Vec::new();

        for _ in 0..config.n_classical_layers {
            conv_layers.push(Linear::new(config.d_model, config.d_model, config.use_bias));
            fc_layers.push(Linear::new(config.d_model, config.d_model, config.use_bias));
            layer_norms.push(LayerNorm::new(vec![config.d_model], 1e-12)?);
        }

        for _ in 0..config.n_quantum_layers {
            let ansatz = QuantumAnsatz::from(config.quantum_ansatz.clone());
            let parameters = vec![0.1; config.get_quantum_parameters_count()];
            quantum_pooling_layers.push(QuantumNeuralLayer::new(
                config.num_qubits,
                ansatz,
                &parameters,
            )?);
        }

        let output_projection = Linear::new(config.d_model, config.d_model, config.use_bias);

        Ok(Self {
            config: config.clone(),
            conv_layers,
            quantum_pooling_layers,
            fc_layers,
            layer_norms,
            output_projection,
        })
    }

    /// Forward pass through quantum CNN
    pub fn forward(&mut self, input: &Tensor) -> Result<QuantumClassicalModelOutput> {
        let mut hidden_states = input.clone();
        let mut quantum_measurements = Vec::new();

        // Convolutional layers with quantum pooling
        for (i, (conv_layer, layer_norm)) in
            self.conv_layers.iter().zip(self.layer_norms.iter()).enumerate()
        {
            // Convolution (simplified as linear transformation)
            let conv_output = conv_layer.forward(hidden_states.clone())?;
            hidden_states = layer_norm.forward(conv_output)?;

            // Quantum pooling
            if i < self.quantum_pooling_layers.len() {
                let quantum_output = self.quantum_pooling_layers[i].forward(&hidden_states)?;
                quantum_measurements.push(quantum_output.clone());
                hidden_states = quantum_output;
            }
        }

        // Fully connected layers
        for fc_layer in &self.fc_layers {
            hidden_states = fc_layer.forward(hidden_states)?;
            hidden_states = hidden_states.relu()?;
        }

        let output = self.output_projection.forward(hidden_states.clone())?;

        let combined_measurements = if !quantum_measurements.is_empty() {
            let mut combined = quantum_measurements[0].clone();
            for measurement in quantum_measurements.iter().skip(1) {
                combined = combined.add(measurement)?;
            }
            Some(combined)
        } else {
            None
        };

        Ok(QuantumClassicalModelOutput {
            hidden_states: output,
            quantum_measurements: combined_measurements,
            classical_activations: Some(hidden_states),
            quantum_attention_weights: None,
            quantum_fidelity_scores: None,
            quantum_entanglement_measures: None,
            quantum_error_mitigation: None,
        })
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        self.conv_layers.iter().map(|l| l.parameter_count()).sum::<usize>()
            + self.fc_layers.iter().map(|l| l.parameter_count()).sum::<usize>()
            + self.layer_norms.iter().map(|l| l.parameter_count()).sum::<usize>()
            + self.output_projection.parameter_count()
            + self.quantum_pooling_layers.len() * self.config.get_quantum_parameters_count()
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        self.parameter_count() as f32 * 4.0 / 1_000_000.0
    }
}