quantrs2_device/process_tomography/reconstruction/
mod.rs

1//! Process reconstruction methods
2
3pub mod bayesian;
4pub mod compressed_sensing;
5pub mod ensemble;
6pub mod linear_inversion;
7pub mod machine_learning;
8pub mod maximum_likelihood;
9
10use scirs2_core::ndarray::{Array1, Array2, Array4};
11use scirs2_core::Complex64;
12
13use super::core::SciRS2ProcessTomographer;
14use super::results::{ExperimentalData, PhysicalValidityMetrics, ReconstructionQuality};
15use crate::DeviceResult;
16
17pub use bayesian::*;
18pub use compressed_sensing::*;
19pub use ensemble::*;
20pub use linear_inversion::*;
21pub use machine_learning::*;
22pub use maximum_likelihood::*;
23
24/// Reconstruction methods implementation for SciRS2ProcessTomographer
25impl SciRS2ProcessTomographer {
26    /// Linear inversion reconstruction
27    pub fn linear_inversion_reconstruction(
28        &self,
29        experimental_data: &ExperimentalData,
30    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
31        linear_inversion::reconstruct_linear_inversion(self, experimental_data)
32    }
33
34    /// Maximum likelihood estimation reconstruction
35    pub fn maximum_likelihood_reconstruction(
36        &self,
37        experimental_data: &ExperimentalData,
38    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
39        maximum_likelihood::reconstruct_maximum_likelihood(self, experimental_data)
40    }
41
42    /// Compressed sensing reconstruction
43    pub fn compressed_sensing_reconstruction(
44        &self,
45        experimental_data: &ExperimentalData,
46    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
47        compressed_sensing::reconstruct_compressed_sensing(self, experimental_data)
48    }
49
50    /// Bayesian inference reconstruction
51    pub fn bayesian_reconstruction(
52        &self,
53        experimental_data: &ExperimentalData,
54    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
55        bayesian::reconstruct_bayesian(self, experimental_data)
56    }
57
58    /// Ensemble methods reconstruction
59    pub fn ensemble_reconstruction(
60        &self,
61        experimental_data: &ExperimentalData,
62    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
63        ensemble::reconstruct_ensemble(self, experimental_data)
64    }
65
66    /// Machine learning reconstruction
67    pub fn ml_reconstruction(
68        &self,
69        experimental_data: &ExperimentalData,
70    ) -> DeviceResult<(Array4<Complex64>, ReconstructionQuality)> {
71        machine_learning::reconstruct_machine_learning(self, experimental_data)
72    }
73
74    /// Build measurement matrix for linear reconstruction
75    pub fn build_measurement_matrix(
76        &self,
77        experimental_data: &ExperimentalData,
78    ) -> DeviceResult<Array2<f64>> {
79        let num_measurements = experimental_data.measurement_results.len();
80        let num_qubits = (experimental_data.input_states[0].dim().0 as f64).log2() as usize;
81        let process_dim = (1_usize << num_qubits).pow(2); // d^2 for d-dimensional system
82
83        let mut measurement_matrix = Array2::zeros((num_measurements, process_dim));
84
85        for (m_idx, (&result, &uncertainty)) in experimental_data
86            .measurement_results
87            .iter()
88            .zip(experimental_data.measurement_uncertainties.iter())
89            .enumerate()
90        {
91            let input_idx = m_idx / experimental_data.measurement_operators.len();
92            let meas_idx = m_idx % experimental_data.measurement_operators.len();
93
94            if input_idx < experimental_data.input_states.len()
95                && meas_idx < experimental_data.measurement_operators.len()
96            {
97                let input_state = &experimental_data.input_states[input_idx];
98                let measurement = &experimental_data.measurement_operators[meas_idx];
99
100                // Compute coefficients for the process matrix elements
101                for i in 0..(1 << num_qubits) {
102                    for j in 0..(1 << num_qubits) {
103                        for k in 0..(1 << num_qubits) {
104                            for l in 0..(1 << num_qubits) {
105                                let process_idx = i * (1_usize << num_qubits).pow(3)
106                                    + j * (1_usize << num_qubits).pow(2)
107                                    + k * (1_usize << num_qubits)
108                                    + l;
109
110                                if process_idx < process_dim {
111                                    let coefficient = self.compute_process_coefficient(
112                                        input_state,
113                                        measurement,
114                                        i,
115                                        j,
116                                        k,
117                                        l,
118                                    )?;
119                                    measurement_matrix[[m_idx, process_idx]] = coefficient;
120                                }
121                            }
122                        }
123                    }
124                }
125            }
126        }
127
128        Ok(measurement_matrix)
129    }
130}
131
132/// Common utilities for reconstruction methods
133pub mod utils {
134    use super::*;
135    use crate::DeviceResult;
136
137    /// Check physical validity of reconstructed process
138    pub fn check_physical_validity(process_matrix: &Array4<Complex64>) -> PhysicalValidityMetrics {
139        let dim = process_matrix.dim().0;
140
141        // Check complete positivity (simplified)
142        let mut is_cp = true;
143        let mut positivity_measure = 1.0;
144
145        // Check trace preservation (simplified)
146        let mut trace_sum = 0.0;
147        for i in 0..dim {
148            for j in 0..dim {
149                trace_sum += process_matrix[[i, j, i, j]].re;
150            }
151        }
152        let is_tp = (trace_sum - 1.0).abs() < 1e-6;
153        let tp_measure = 1.0 - (trace_sum - 1.0).abs();
154
155        PhysicalValidityMetrics {
156            is_completely_positive: is_cp,
157            is_trace_preserving: is_tp,
158            positivity_measure,
159            trace_preservation_measure: tp_measure.max(0.0),
160        }
161    }
162
163    /// Calculate reconstruction quality metrics
164    pub fn calculate_reconstruction_quality(
165        process_matrix: &Array4<Complex64>,
166        experimental_data: &ExperimentalData,
167        log_likelihood: f64,
168    ) -> ReconstructionQuality {
169        let physical_validity = check_physical_validity(process_matrix);
170
171        // Calculate condition number (simplified)
172        let condition_number = 10.0; // Placeholder
173
174        ReconstructionQuality {
175            log_likelihood,
176            physical_validity,
177            condition_number,
178        }
179    }
180}
quantrs2_device/process_tomography/reconstruction/mod.rs

quantrs2_device/process_tomography/reconstruction/
mod.rs
