quantrs2_device/process_tomography/reconstruction/
bayesian.rs

1//! Bayesian inference reconstruction method
2
3use scirs2_core::ndarray::{Array1, Array2, Array4};
4use scirs2_core::Complex64;
5
6use super::super::core::SciRS2ProcessTomographer;
7use super::super::results::{ExperimentalData, ReconstructionQuality};
8use super::utils::calculate_reconstruction_quality;
9use crate::DeviceResult;
10
11/// Bayesian inference reconstruction implementation
12pub fn reconstruct_bayesian(
13    tomographer: &SciRS2ProcessTomographer,
14    experimental_data: &ExperimentalData,
15) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
16    let num_qubits = (experimental_data.input_states[0].dim().0 as f64).log2() as usize;
17    let dim = 1 << num_qubits;
18
19    // Initialize with prior (identity process)
20    let mut process_matrix = Array4::zeros((dim, dim, dim, dim));
21    for i in 0..dim {
22        process_matrix[[i, i, i, i]] = Complex64::new(1.0, 0.0);
23    }
24
25    // Simplified Bayesian update
26    // In practice, this would involve MCMC or variational inference
27
28    let log_likelihood = calculate_bayesian_log_likelihood(&process_matrix, experimental_data)?;
29    let reconstruction_quality =
30        calculate_reconstruction_quality(&process_matrix, experimental_data, log_likelihood);
31
32    Ok((process_matrix, reconstruction_quality))
33}
34
35/// Calculate Bayesian log-likelihood including prior
36fn calculate_bayesian_log_likelihood(
37    process_matrix: &Array4<Complex64>,
38    experimental_data: &ExperimentalData,
39) -> DeviceResult<f64> {
40    let mut log_likelihood = 0.0;
41
42    // Likelihood term
43    for (observed, &uncertainty) in experimental_data
44        .measurement_results
45        .iter()
46        .zip(experimental_data.measurement_uncertainties.iter())
47    {
48        let predicted = 0.5; // Placeholder
49        let diff = observed - predicted;
50        let variance = uncertainty * uncertainty;
51        log_likelihood -= 0.5 * (diff * diff / variance);
52    }
53
54    // Prior term (favor identity-like processes)
55    let dim = process_matrix.dim().0;
56    for i in 0..dim {
57        for j in 0..dim {
58            for k in 0..dim {
59                for l in 0..dim {
60                    if i == j && k == l && i == k {
61                        // Prior favors diagonal elements close to 1
62                        let deviation = (process_matrix[[i, j, k, l]].re - 1.0).abs();
63                        log_likelihood -= 0.5 * deviation * deviation;
64                    } else {
65                        // Prior favors off-diagonal elements close to 0
66                        let magnitude = process_matrix[[i, j, k, l]].norm();
67                        log_likelihood -= 0.5 * magnitude * magnitude;
68                    }
69                }
70            }
71        }
72    }
73
74    Ok(log_likelihood)
75}
quantrs2_device/process_tomography/reconstruction/bayesian.rs

quantrs2_device/process_tomography/reconstruction/
bayesian.rs
