quantrs2_core/

ml_error_mitigation.rs

//! Machine Learning-Based Quantum Error Mitigation
//!
//! This module implements advanced error mitigation techniques that use machine learning
//! to adaptively learn and correct quantum errors. This goes beyond traditional error
//! mitigation by learning error patterns from quantum hardware and optimizing mitigation
//! strategies in real-time.

use crate::error::{QuantRS2Error, QuantRS2Result};
use crate::gate::GateOp;
use crate::qubit::QubitId;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::{thread_rng, Rng};
use scirs2_core::{Complex32, Complex64};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};

/// Neural network-based error predictor
///
/// Uses a simple feedforward neural network to predict error probabilities
/// based on circuit characteristics and historical error data.
#[derive(Debug, Clone)]
pub struct NeuralErrorPredictor {
    /// Input layer weights (circuit features -> hidden layer)
    input_weights: Array2<f64>,
    /// Hidden layer weights (hidden -> output)
    hidden_weights: Array2<f64>,
    /// Input layer bias
    input_bias: Array1<f64>,
    /// Hidden layer bias
    hidden_bias: Array1<f64>,
    /// Learning rate for gradient descent
    learning_rate: f64,
    /// Training history for adaptive learning
    training_history: Arc<RwLock<Vec<TrainingExample>>>,
}

/// Training example for the neural error predictor
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Circuit features (depth, gate counts, connectivity, etc.)
    pub features: Vec<f64>,
    /// Observed error rate
    pub error_rate: f64,
    /// Timestamp of observation
    pub timestamp: std::time::Instant,
}

/// Circuit features for error prediction
#[derive(Debug, Clone)]
pub struct CircuitFeatures {
    /// Circuit depth (number of layers)
    pub depth: usize,
    /// Number of single-qubit gates
    pub single_qubit_gates: usize,
    /// Number of two-qubit gates
    pub two_qubit_gates: usize,
    /// Circuit connectivity (average qubits per gate)
    pub connectivity: f64,
    /// Estimated gate fidelity
    pub average_gate_fidelity: f64,
    /// Number of measurement operations
    pub measurement_count: usize,
    /// Circuit width (number of qubits)
    pub width: usize,
    /// Entanglement entropy estimate
    pub entanglement_entropy: f64,
}

impl NeuralErrorPredictor {
    /// Create a new neural error predictor
    ///
    /// # Arguments
    /// * `input_size` - Number of input features
    /// * `hidden_size` - Number of hidden neurons
    /// * `learning_rate` - Learning rate for training
    pub fn new(input_size: usize, hidden_size: usize, learning_rate: f64) -> Self {
        let mut rng = thread_rng();

        // Xavier initialization for better convergence
        let xavier_input = (6.0 / (input_size + hidden_size) as f64).sqrt();
        let xavier_hidden = (6.0 / (hidden_size + 1) as f64).sqrt();

        let input_weights = Array2::from_shape_fn((hidden_size, input_size), |_| {
            rng.gen_range(-xavier_input..xavier_input)
        });

        let hidden_weights = Array2::from_shape_fn((1, hidden_size), |_| {
            rng.gen_range(-xavier_hidden..xavier_hidden)
        });

        let input_bias = Array1::zeros(hidden_size);
        let hidden_bias = Array1::zeros(1);

        Self {
            input_weights,
            hidden_weights,
            input_bias,
            hidden_bias,
            learning_rate,
            training_history: Arc::new(RwLock::new(Vec::new())),
        }
    }

    /// Predict error rate for given circuit features
    ///
    /// # Arguments
    /// * `features` - Circuit features to analyze
    ///
    /// # Returns
    /// Predicted error rate (0.0 to 1.0)
    pub fn predict(&self, features: &[f64]) -> QuantRS2Result<f64> {
        if features.len() != self.input_weights.ncols() {
            return Err(QuantRS2Error::InvalidInput(format!(
                "Expected {} features, got {}",
                self.input_weights.ncols(),
                features.len()
            )));
        }

        // Forward pass
        let input = Array1::from_vec(features.to_vec());

        // Hidden layer: ReLU(W1 * x + b1)
        let hidden_pre = self.input_weights.dot(&input) + &self.input_bias;
        let hidden = hidden_pre.mapv(|x| x.max(0.0)); // ReLU activation

        // Output layer: sigmoid(W2 * h + b2)
        let output_pre = self.hidden_weights.dot(&hidden) + &self.hidden_bias;
        let output = 1.0 / (1.0 + (-output_pre[0]).exp()); // Sigmoid activation

        Ok(output.clamp(0.0, 1.0))
    }

    /// Train the predictor with a new example
    ///
    /// # Arguments
    /// * `features` - Circuit features
    /// * `observed_error_rate` - Observed error rate from execution
    pub fn train(&mut self, features: &[f64], observed_error_rate: f64) -> QuantRS2Result<()> {
        if features.len() != self.input_weights.ncols() {
            return Err(QuantRS2Error::InvalidInput(
                "Feature size mismatch".to_string(),
            ));
        }

        // Store training example
        {
            let mut history = self
                .training_history
                .write()
                .unwrap_or_else(|e| e.into_inner());
            history.push(TrainingExample {
                features: features.to_vec(),
                error_rate: observed_error_rate,
                timestamp: std::time::Instant::now(),
            });

            // Keep only recent history (last 1000 examples)
            let len = history.len();
            if len > 1000 {
                history.drain(0..len - 1000);
            }
        }

        // Backpropagation
        let input = Array1::from_vec(features.to_vec());

        // Forward pass
        let hidden_pre = self.input_weights.dot(&input) + &self.input_bias;
        let hidden = hidden_pre.mapv(|x| x.max(0.0));

        let output_pre = self.hidden_weights.dot(&hidden) + &self.hidden_bias;
        let predicted = 1.0 / (1.0 + (-output_pre[0]).exp());

        // Backward pass
        // Output layer gradient
        let output_error = predicted - observed_error_rate;
        let output_delta = output_error * predicted * (1.0 - predicted); // Sigmoid derivative

        // Hidden layer gradient
        let hidden_error = output_delta * self.hidden_weights.row(0).to_owned();
        let hidden_delta = hidden_error.mapv(|x| if x > 0.0 { x } else { 0.0 }); // ReLU derivative

        // Update weights
        for i in 0..self.hidden_weights.ncols() {
            self.hidden_weights[[0, i]] -= self.learning_rate * output_delta * hidden[i];
        }
        self.hidden_bias[0] -= self.learning_rate * output_delta;

        for i in 0..self.input_weights.nrows() {
            for j in 0..self.input_weights.ncols() {
                self.input_weights[[i, j]] -= self.learning_rate * hidden_delta[i] * input[j];
            }
            self.input_bias[i] -= self.learning_rate * hidden_delta[i];
        }

        Ok(())
    }

    /// Get training history
    pub fn get_training_history(&self) -> Vec<TrainingExample> {
        self.training_history
            .read()
            .unwrap_or_else(|e| e.into_inner())
            .clone()
    }

    /// Calculate prediction accuracy on historical data
    pub fn calculate_accuracy(&self) -> f64 {
        let history = self
            .training_history
            .read()
            .unwrap_or_else(|e| e.into_inner());
        if history.is_empty() {
            return 0.0;
        }

        let mut total_error = 0.0;
        for example in history.iter() {
            if let Ok(predicted) = self.predict(&example.features) {
                total_error += (predicted - example.error_rate).abs();
            }
        }

        1.0 - (total_error / history.len() as f64)
    }
}

impl CircuitFeatures {
    /// Extract features from a quantum circuit
    ///
    /// # Arguments
    /// * `gates` - List of quantum gates in the circuit
    /// * `num_qubits` - Total number of qubits
    pub fn extract_from_circuit(gates: &[Box<dyn GateOp>], num_qubits: usize) -> Self {
        let mut single_qubit_gates = 0;
        let mut two_qubit_gates = 0;
        let mut measurement_count = 0;
        let mut max_depth = 0;
        let mut qubit_depths: HashMap<QubitId, usize> = HashMap::new();

        // Analyze gate structure
        for gate in gates {
            let qubits = gate.qubits();

            match qubits.len() {
                1 => single_qubit_gates += 1,
                2 => two_qubit_gates += 1,
                _ => {}
            }

            // Track depth per qubit
            let current_depth = qubits
                .iter()
                .map(|q| *qubit_depths.get(q).unwrap_or(&0))
                .max()
                .unwrap_or(0)
                + 1;

            for qubit in qubits {
                qubit_depths.insert(qubit, current_depth);
            }

            max_depth = max_depth.max(current_depth);

            // Count measurements (check gate name)
            if gate.name().to_lowercase().contains("measure") {
                measurement_count += 1;
            }
        }

        let total_gates = single_qubit_gates + two_qubit_gates;
        let connectivity = if total_gates > 0 {
            (single_qubit_gates + 2 * two_qubit_gates) as f64 / total_gates as f64
        } else {
            0.0
        };

        // Estimate entanglement entropy (simplified)
        // More two-qubit gates relative to qubits = higher entanglement
        let entanglement_entropy = if num_qubits > 0 {
            (two_qubit_gates as f64 / num_qubits as f64).min(num_qubits as f64)
        } else {
            0.0
        };

        // Estimate average gate fidelity (simplified, would be calibrated)
        let average_gate_fidelity = (two_qubit_gates as f64).mul_add(-0.005, 0.99);

        Self {
            depth: max_depth,
            single_qubit_gates,
            two_qubit_gates,
            connectivity,
            average_gate_fidelity: average_gate_fidelity.max(0.90),
            measurement_count,
            width: num_qubits,
            entanglement_entropy,
        }
    }

    /// Convert features to vector for ML input
    pub fn to_vector(&self) -> Vec<f64> {
        vec![
            self.depth as f64,
            self.single_qubit_gates as f64,
            self.two_qubit_gates as f64,
            self.connectivity,
            self.average_gate_fidelity,
            self.measurement_count as f64,
            self.width as f64,
            self.entanglement_entropy,
        ]
    }
}

/// Adaptive error mitigation strategy
///
/// Dynamically adjusts mitigation parameters based on learned error patterns
pub struct AdaptiveErrorMitigation {
    /// Neural predictor for error rates
    predictor: NeuralErrorPredictor,
    /// Mitigation strength multiplier
    mitigation_strength: f64,
    /// Minimum shots for statistical significance
    min_shots: usize,
    /// Performance metrics
    metrics: Arc<RwLock<MitigationMetrics>>,
}

/// Metrics tracking mitigation performance
#[derive(Debug, Clone)]
pub struct MitigationMetrics {
    pub total_circuits: usize,
    pub average_improvement: f64,
    pub prediction_accuracy: f64,
    pub adaptive_adjustments: usize,
}

impl AdaptiveErrorMitigation {
    /// Create new adaptive error mitigation system
    pub fn new() -> Self {
        Self {
            predictor: NeuralErrorPredictor::new(8, 16, 0.01),
            mitigation_strength: 1.0,
            min_shots: 1024,
            metrics: Arc::new(RwLock::new(MitigationMetrics {
                total_circuits: 0,
                average_improvement: 0.0,
                prediction_accuracy: 0.0,
                adaptive_adjustments: 0,
            })),
        }
    }

    /// Predict optimal mitigation parameters for a circuit
    ///
    /// # Arguments
    /// * `features` - Circuit features
    ///
    /// # Returns
    /// Recommended number of shots and mitigation strength
    pub fn recommend_mitigation(&self, features: &CircuitFeatures) -> QuantRS2Result<(usize, f64)> {
        let predicted_error = self.predictor.predict(&features.to_vector())?;

        // Adaptive shot allocation: more errors = more shots needed
        let recommended_shots =
            (self.min_shots as f64 * predicted_error.mul_add(10.0, 1.0)) as usize;

        // Adaptive mitigation strength
        let strength = self.mitigation_strength * predicted_error.mul_add(2.0, 1.0);

        Ok((recommended_shots, strength))
    }

    /// Update predictor with observed results
    pub fn update_from_results(
        &mut self,
        features: &CircuitFeatures,
        observed_error: f64,
    ) -> QuantRS2Result<()> {
        self.predictor
            .train(&features.to_vector(), observed_error)?;

        // Update metrics
        {
            let mut metrics = self.metrics.write().unwrap_or_else(|e| e.into_inner());
            metrics.total_circuits += 1;
            metrics.prediction_accuracy = self.predictor.calculate_accuracy();
        }

        Ok(())
    }

    /// Get current metrics
    pub fn get_metrics(&self) -> MitigationMetrics {
        self.metrics
            .read()
            .unwrap_or_else(|e| e.into_inner())
            .clone()
    }
}

impl Default for AdaptiveErrorMitigation {
    fn default() -> Self {
        Self::new()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_neural_predictor_creation() {
        let predictor = NeuralErrorPredictor::new(8, 16, 0.01);
        assert_eq!(predictor.input_weights.ncols(), 8);
        assert_eq!(predictor.input_weights.nrows(), 16);
    }

    #[test]
    fn test_prediction() {
        let predictor = NeuralErrorPredictor::new(8, 16, 0.01);
        let features = vec![10.0, 5.0, 3.0, 1.5, 0.99, 2.0, 4.0, 1.2];

        let result = predictor.predict(&features);
        assert!(result.is_ok());

        let error_rate = result.expect("Failed to predict error rate");
        assert!(error_rate >= 0.0 && error_rate <= 1.0);
    }

    #[test]
    fn test_training() {
        let mut predictor = NeuralErrorPredictor::new(8, 16, 0.01);
        let features = vec![10.0, 5.0, 3.0, 1.5, 0.99, 2.0, 4.0, 1.2];

        // Train multiple times
        for _ in 0..100 {
            let result = predictor.train(&features, 0.05);
            assert!(result.is_ok());
        }

        // Check that training history is updated
        let history = predictor.get_training_history();
        assert_eq!(history.len(), 100);
    }

    #[test]
    fn test_adaptive_mitigation() {
        let mitigation = AdaptiveErrorMitigation::new();

        let features = CircuitFeatures {
            depth: 10,
            single_qubit_gates: 20,
            two_qubit_gates: 8,
            connectivity: 1.4,
            average_gate_fidelity: 0.99,
            measurement_count: 4,
            width: 4,
            entanglement_entropy: 2.0,
        };

        let result = mitigation.recommend_mitigation(&features);
        assert!(result.is_ok());

        let (shots, strength) = result.expect("Failed to recommend mitigation");
        assert!(shots >= 1024);
        assert!(strength > 0.0);
    }
}