quantrs2-core 0.1.3

//! Machine learning decoder for quantum error correction

use super::pauli::{Pauli, PauliString};
use super::stabilizer::StabilizerCode;
use crate::error::QuantRS2Result;

/// Machine learning decoder for quantum error correction
pub struct MLDecoder {
    /// The code being decoded
    code: StabilizerCode,
    /// Neural network weights (simplified representation)
    weights: Vec<Vec<f64>>,
}

impl MLDecoder {
    /// Create a new ML decoder
    pub fn new(code: StabilizerCode) -> Self {
        // Initialize random weights for a simple neural network
        let input_size = code.stabilizers.len();
        let hidden_size = 2 * input_size;
        let output_size = code.n * 3; // 3 Pauli operators per qubit

        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();
        let mut weights = Vec::new();

        // Input to hidden layer
        let mut w1 = Vec::new();
        for _ in 0..hidden_size {
            let mut row = Vec::new();
            for _ in 0..input_size {
                row.push((rng.random::<f64>() - 0.5) * 0.1);
            }
            w1.push(row);
        }
        weights.push(w1.into_iter().flatten().collect());

        // Hidden to output layer
        let mut w2 = Vec::new();
        for _ in 0..output_size {
            let mut row = Vec::new();
            for _ in 0..hidden_size {
                row.push((rng.random::<f64>() - 0.5) * 0.1);
            }
            w2.push(row);
        }
        weights.push(w2.into_iter().flatten().collect());

        Self { code, weights }
    }

    /// Simple feedforward prediction
    fn predict(&self, syndrome: &[bool]) -> Vec<f64> {
        let input: Vec<f64> = syndrome
            .iter()
            .map(|&b| if b { 1.0 } else { 0.0 })
            .collect();

        // This is a greatly simplified neural network
        // In practice, would use proper ML framework
        let hidden_size = 2 * input.len();
        let mut hidden = vec![0.0; hidden_size];

        // Input to hidden
        for i in 0..hidden_size {
            for j in 0..input.len() {
                if i * input.len() + j < self.weights[0].len() {
                    hidden[i] += input[j] * self.weights[0][i * input.len() + j];
                }
            }
            hidden[i] = hidden[i].tanh(); // Activation function
        }

        // Hidden to output
        let output_size = self.code.n * 3;
        let mut output = vec![0.0; output_size];

        for i in 0..output_size {
            for j in 0..hidden_size {
                if i * hidden_size + j < self.weights[1].len() {
                    output[i] += hidden[j] * self.weights[1][i * hidden_size + j];
                }
            }
        }

        output
    }
}

impl super::SyndromeDecoder for MLDecoder {
    fn decode(&self, syndrome: &[bool]) -> QuantRS2Result<PauliString> {
        let prediction = self.predict(syndrome);

        // Convert prediction to Pauli string
        let mut paulis = Vec::with_capacity(self.code.n);

        for qubit in 0..self.code.n {
            let base_idx = qubit * 3;
            if base_idx + 2 < prediction.len() {
                let x_prob = prediction[base_idx];
                let y_prob = prediction[base_idx + 1];
                let z_prob = prediction[base_idx + 2];

                // Choose Pauli with highest probability
                if x_prob > y_prob && x_prob > z_prob && x_prob > 0.5 {
                    paulis.push(Pauli::X);
                } else if y_prob > z_prob && y_prob > 0.5 {
                    paulis.push(Pauli::Y);
                } else if z_prob > 0.5 {
                    paulis.push(Pauli::Z);
                } else {
                    paulis.push(Pauli::I);
                }
            } else {
                paulis.push(Pauli::I);
            }
        }

        Ok(PauliString::new(paulis))
    }
}