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qdk/source/noisy_simulator/src/operation

source/noisy_simulator/src/operation/tests.rs

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1// Copyright (c) Microsoft Corporation.
2// Licensed under the MIT License.
3
4use nalgebra::dmatrix;
5use num_complex::{Complex, ComplexFloat};
6
7use crate::{
8 SquareMatrix,
9 operation::{Operation, operation},
10 tests::assert_approx_eq,
11};
12
13/// Constructs an operation using dense kraus matrices, to allow an exhaustive
14/// testing of the inner linear operations used to construct the `Operator`.
15fn dense_operation() -> Operation {
16 const I: Complex<f64> = Complex::I;
17
18 // Construct an operation from two kraus matrices.
19 operation!(
20 [
21 0., 1., 2., 4.;
22 1., 2., 3., 4.;
23 1., 1., 3., 5.;
24 3., 1., 1., 2.;
25 ],
26 [
27 0.1 * I, 1. + I, 2., 4.;
28 1., 2. - I, 3., 4.;
29 1. + 0.50 * I, 1., 3., 5.;
30 3. + 0.25 * I, 1., 1., 2.;
31 ]
32 )
33 .expect("operation should be valid")
34}
35
36#[test]
37fn check_operation_number_of_qubits_is_computed_correctly() {
38 let op = operation!(
39 [
40 0., 0.;
41 0., 0.;
42 ]
43 )
44 .expect("operation should be valid");
45
46 assert_eq!(1, op.number_of_qubits());
47}
48
49#[test]
50fn check_non_square_kraus_operator_does_not_panic() {
51 let op = operation!(
52 [
53 0., 0.;
54 ]
55 );
56
57 assert!(matches!(
58 op,
59 Err(crate::Error::FailedToConstructOperation(_))
60 ));
61}
62
63/// Check that the inner matrices of the instrument are constructed correctly.
64#[test]
65fn check_effect_matrix_is_computed_correctly() {
66 const I: Complex<f64> = Complex::I;
67 let op = dense_operation();
68
69 // Check that the effect matrix is the sum of the individual effect matrices
70 // of each kraus operator.
71 let eff0 = dmatrix![
72 11., 6., 9., 15.;
73 6., 7., 12., 19.;
74 9., 12., 23., 37.;
75 15., 19., 37., 61.;
76 ]
77 .map(std::convert::Into::into);
78
79 let eff1 = dmatrix! [
80 11.3225 + 0. * I, 6.1 - 1.85 * I, 9. - 1.95 * I, 15. - 3.4 * I;
81 6.1 + 1.85 * I, 9. + 0. * I, 12. + 1. * I, 19. + 0. * I;
82 9. + 1.95 * I, 12. - 1. * I, 23. + 0. * I, 37. + 0. * I;
83 15. + 3.4 * I, 19. + 0. * I, 37. + 0. * I, 61. + 0. * I;
84 ];
85
86 let eff = eff0 + eff1;
87
88 for (x0, x1) in eff.iter().zip(op.effect_matrix().iter()) {
89 assert_approx_eq(0., (x0 - x1.conj()).abs());
90 }
91}
92
93#[test]
94fn check_operation_matrix_is_computed_correctly() {
95 const I: Complex<f64> = Complex::I;
96 let op = dense_operation();
97
98 // Check that the operation matrix is the sum of the individual operation matrices
99 // of each kraus operator.
100 let op0: SquareMatrix = dmatrix![
101 0., 0., 0., 0. , 0., 1., 2., 4. , 0., 2., 4., 8. , 0. , 4. , 8. , 16.;
102 0., 0., 0., 0. , 1., 2., 3., 4. , 2., 4., 6., 8. , 4. , 8. , 12., 16.;
103 0., 0., 0., 0. , 1., 1., 3., 5. , 2., 2., 6., 10., 4. , 4. , 12., 20.;
104 0., 0., 0., 0. , 3., 1., 1., 2. , 6., 2., 2., 4. , 12., 4. , 4. , 8. ;
105 0., 1., 2., 4. , 0., 2., 4., 8. , 0., 3., 6., 12., 0. , 4. , 8. , 16.;
106 1., 2., 3., 4. , 2., 4., 6., 8. , 3., 6., 9., 12., 4. , 8. , 12., 16.;
107 1., 1., 3., 5. , 2., 2., 6., 10., 3., 3., 9., 15., 4. , 4. , 12., 20.;
108 3., 1., 1., 2. , 6., 2., 2., 4. , 9., 3., 3., 6. , 12., 4. , 4. , 8. ;
109 0., 1., 2., 4. , 0., 1., 2., 4. , 0., 3., 6., 12., 0. , 5. , 10., 20.;
110 1., 2., 3., 4. , 1., 2., 3., 4. , 3., 6., 9., 12., 5. , 10., 15., 20.;
111 1., 1., 3., 5. , 1., 1., 3., 5. , 3., 3., 9., 15., 5. , 5. , 15., 25.;
112 3., 1., 1., 2. , 3., 1., 1., 2. , 9., 3., 3., 6. , 15., 5. , 5. , 10.;
113 0., 3., 6., 12., 0., 1., 2., 4. , 0., 1., 2., 4. , 0. , 2. , 4. , 8. ;
114 3., 6., 9., 12., 1., 2., 3., 4. , 1., 2., 3., 4. , 2. , 4. , 6. , 8. ;
115 3., 3., 9., 15., 1., 1., 3., 5. , 1., 1., 3., 5. , 2. , 2. , 6. , 10.;
116 9., 3., 3., 6. , 3., 1., 1., 2. , 3., 1., 1., 2. , 6. , 2. , 2. , 4. ;
117 ]
118 .map(std::convert::Into::into);
119
120 let op1 = dmatrix![
121 0.01 + 0. * I, 0.1 + 0.1 * I, 0. + 0.2 * I, 0. + 0.4 * I, 0.1 - 0.1 * I, 2. + 0. * I, 2. + 2. * I, 4. + 4. * I, 0. - 0.2 * I, 2. - 2. * I, 4. + 0. * I, 8. + 0. * I, 0. - 0.4 * I, 4. - 4. * I, 8. + 0. * I, 16. + 0. * I;
122 0. + 0.1 * I, -0.1 + 0.2 * I, 0. + 0.3 * I, 0. + 0.4 * I, 1. + 1. * I, 1. + 3. * I, 3. + 3. * I, 4. + 4. * I, 2. + 0. * I, 4. + 2. * I, 6. + 0. * I, 8. + 0. * I, 4. + 0. * I, 8. + 4. * I, 12. + 0. * I, 16. + 0. * I;
123 0.05 + 0.1 * I, 0. + 0.1 * I, 0. + 0.3 * I, 0. + 0.5 * I, 1.5 + 0.5 * I, 1. + 1. * I, 3. + 3. * I, 5. + 5. * I, 2. - 1. * I, 2. + 0. * I, 6. + 0. * I, 10. + 0. * I, 4. - 2. * I, 4. + 0. * I, 12. + 0. * I, 20. + 0. * I;
124 0.025 + 0.3 * I, 0. + 0.1 * I, 0. + 0.1 * I, 0. + 0.2 * I, 3.25 + 2.75 * I, 1. + 1. * I, 1. + 1. * I, 2. + 2. * I, 6. - 0.5 * I, 2. + 0. * I, 2. + 0. * I, 4. + 0. * I, 12. - 1. * I, 4. + 0. * I, 4. + 0. * I, 8. + 0. * I;
125 0. - 0.1 * I, 1. - 1. * I, 2. + 0. * I, 4. + 0. * I, -0.1 - 0.2 * I, 1. - 3. * I, 4. - 2. * I, 8. - 4. * I, 0. - 0.3 * I, 3. - 3. * I, 6. + 0. * I, 12. + 0. * I, 0. - 0.4 * I, 4. - 4. * I, 8. + 0. * I, 16. + 0. * I;
126 1. + 0. * I, 2. + 1. * I, 3. + 0. * I, 4. + 0. * I, 2. - 1. * I, 5. + 0. * I, 6. - 3. * I, 8. - 4. * I, 3. + 0. * I, 6. + 3. * I, 9. + 0. * I, 12. + 0. * I, 4. + 0. * I, 8. + 4. * I, 12. + 0. * I, 16. + 0. * I;
127 1. - 0.5 * I, 1. + 0. * I, 3. + 0. * I, 5. + 0. * I, 1.5 - 2. * I, 2. - 1. * I, 6. - 3. * I, 10. - 5. * I, 3. - 1.5 * I, 3. + 0. * I, 9. + 0. * I, 15. + 0. * I, 4. - 2. * I, 4. + 0. * I, 12. + 0. * I, 20. + 0. * I;
128 3. - 0.25 * I, 1. + 0. * I, 1. + 0. * I, 2. + 0. * I, 5.75 - 3.5 * I, 2. - 1. * I, 2. - 1. * I, 4. - 2. * I, 9. - 0.75 * I, 3. + 0. * I, 3. + 0. * I, 6. + 0. * I, 12. - 1. * I, 4. + 0. * I, 4. + 0. * I, 8. + 0. * I;
129 0.05 - 0.1 * I, 1.5 - 0.5 * I, 2. + 1. * I, 4. + 2. * I, 0. - 0.1 * I, 1. - 1. * I, 2. + 0. * I, 4. + 0. * I, 0. - 0.3 * I, 3. - 3. * I, 6. + 0. * I, 12. + 0. * I, 0. - 0.5 * I, 5. - 5. * I, 10. + 0. * I, 20. + 0. * I;
130 1. + 0.5 * I, 1.5 + 2. * I, 3. + 1.5 * I, 4. + 2. * I, 1. + 0. * I, 2. + 1. * I, 3. + 0. * I, 4. + 0. * I, 3. + 0. * I, 6. + 3. * I, 9. + 0. * I, 12. + 0. * I, 5. + 0. * I, 10. + 5. * I, 15. + 0. * I, 20. + 0. * I;
131 1.25 + 0. * I, 1. + 0.5 * I, 3. + 1.5 * I, 5. + 2.5 * I, 1. - 0.5 * I, 1. + 0. * I, 3. + 0. * I, 5. + 0. * I, 3. - 1.5 * I, 3. + 0. * I, 9. + 0. * I, 15. + 0. * I, 5. - 2.5 * I, 5. + 0. * I, 15. + 0. * I, 25. + 0. * I;
132 3.125 + 1.25 * I, 1. + 0.5 * I, 1. + 0.5 * I, 2. + 1. * I, 3. - 0.25 * I, 1. + 0. * I, 1. + 0. * I, 2. + 0. * I, 9. - 0.75 * I, 3. + 0. * I, 3. + 0. * I, 6. + 0. * I, 15. - 1.25 * I, 5. + 0. * I, 5. + 0. * I, 10. + 0. * I;
133 0.025 - 0.3 * I, 3.25 - 2.75 * I, 6. + 0.5 * I, 12. + 1. * I, 0. - 0.1 * I, 1. - 1. * I, 2. + 0. * I, 4. + 0. * I, 0. - 0.1 * I, 1. - 1. * I, 2. + 0. * I, 4. + 0. * I, 0. - 0.2 * I, 2. - 2. * I, 4. + 0. * I, 8. + 0. * I;
134 3. + 0.25 * I, 5.75 + 3.5 * I, 9. + 0.75 * I, 12. + 1. * I, 1. + 0. * I, 2. + 1. * I, 3. + 0. * I, 4. + 0. * I, 1. + 0. * I, 2. + 1. * I, 3. + 0. * I, 4. + 0. * I, 2. + 0. * I, 4. + 2. * I, 6. + 0. * I, 8. + 0. * I;
135 3.125 - 1.25 * I, 3. + 0.25 * I, 9. + 0.75 * I, 15. + 1.25 * I, 1. - 0.5 * I, 1. + 0. * I, 3. + 0. * I, 5. + 0. * I, 1. - 0.5 * I, 1. + 0. * I, 3. + 0. * I, 5. + 0. * I, 2. - 1. * I, 2. + 0. * I, 6. + 0. * I, 10. + 0. * I;
136 9.0625 + 0. * I, 3. + 0.25 * I, 3. + 0.25 * I, 6. + 0.5 * I, 3. - 0.25 * I, 1. + 0. * I, 1. + 0. * I, 2. + 0. * I, 3. - 0.25 * I, 1. + 0. * I, 1. + 0. * I, 2. + 0. * I, 6. - 0.5 * I, 2. + 0. * I, 2. + 0. * I, 4. + 0. * I;
137 ];
138
139 let op_matrix = op0 + op1;
140
141 for (x0, x1) in op.matrix().transpose().iter().zip(op_matrix.iter()) {
142 assert_approx_eq(0., (x0 - x1).abs());
143 }
144}
145