quantrs2-device 0.1.3

Quantum device connectors for the QuantRS2 framework
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75

//! Bayesian inference 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;

/// Bayesian inference reconstruction implementation
pub fn reconstruct_bayesian(
    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 with prior (identity process)
    let mut process_matrix = Array4::zeros((dim, dim, dim, dim));
    for i in 0..dim {
        process_matrix[[i, i, i, i]] = Complex64::new(1.0, 0.0);
    }

    // Simplified Bayesian update
    // In practice, this would involve MCMC or variational inference

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

    Ok((process_matrix, reconstruction_quality))
}

/// Calculate Bayesian log-likelihood including prior
fn calculate_bayesian_log_likelihood(
    process_matrix: &Array4<Complex64>,
    experimental_data: &ExperimentalData,
) -> DeviceResult<f64> {
    let mut log_likelihood = 0.0;

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

    // Prior term (favor identity-like processes)
    let dim = process_matrix.dim().0;
    for i in 0..dim {
        for j in 0..dim {
            for k in 0..dim {
                for l in 0..dim {
                    if i == j && k == l && i == k {
                        // Prior favors diagonal elements close to 1
                        let deviation = (process_matrix[[i, j, k, l]].re - 1.0).abs();
                        log_likelihood -= 0.5 * deviation * deviation;
                    } else {
                        // Prior favors off-diagonal elements close to 0
                        let magnitude = process_matrix[[i, j, k, l]].norm();
                        log_likelihood -= 0.5 * magnitude * magnitude;
                    }
                }
            }
        }
    }

    Ok(log_likelihood)
}