quantrs2-device 0.1.3

Quantum device connectors for the QuantRS2 framework
Documentation 
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//! Compressed sensing reconstruction method

use scirs2_core::ndarray::{Array1, Array2, Array4};
use scirs2_core::Complex64;

use super::super::core::SciRS2ProcessTomographer;
use super::super::results::{ExperimentalData, ReconstructionQuality};
use super::utils::calculate_reconstruction_quality;
use crate::DeviceResult;

/// Compressed sensing reconstruction implementation
pub fn reconstruct_compressed_sensing(
    tomographer: &SciRS2ProcessTomographer,
    experimental_data: &ExperimentalData,
) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
    let num_qubits = (experimental_data.input_states[0].dim().0 as f64).log2() as usize;
    let dim = 1 << num_qubits;

    // Initialize sparse process matrix
    let mut process_matrix = Array4::zeros((dim, dim, dim, dim));

    // Simplified compressed sensing approach
    // In practice, this would use L1-minimization with sparsity constraints

    // Set identity process as baseline
    for i in 0..dim {
        process_matrix[[i, i, i, i]] = Complex64::new(1.0, 0.0);
    }

    // Apply sparsity constraint by zeroing small elements
    let threshold = tomographer
        .config
        .optimization_config
        .regularization
        .l1_strength;
    for i in 0..dim {
        for j in 0..dim {
            for k in 0..dim {
                for l in 0..dim {
                    if process_matrix[[i, j, k, l]].norm() < threshold {
                        process_matrix[[i, j, k, l]] = Complex64::new(0.0, 0.0);
                    }
                }
            }
        }
    }

    let log_likelihood = calculate_cs_log_likelihood(&process_matrix, experimental_data)?;
    let reconstruction_quality =
        calculate_reconstruction_quality(&process_matrix, experimental_data, log_likelihood);

    Ok((process_matrix, reconstruction_quality))
}

/// Calculate log-likelihood for compressed sensing
fn calculate_cs_log_likelihood(
    process_matrix: &Array4<Complex64>,
    experimental_data: &ExperimentalData,
) -> DeviceResult<f64> {
    let mut log_likelihood = 0.0;

    // Simplified calculation
    for (observed, &uncertainty) in experimental_data
        .measurement_results
        .iter()
        .zip(experimental_data.measurement_uncertainties.iter())
    {
        let predicted = 0.5; // Placeholder prediction
        let diff = observed - predicted;
        let variance = uncertainty * uncertainty;
        log_likelihood -= 0.5 * (diff * diff / variance);
    }

    Ok(log_likelihood)
}