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qdk/source/noisy_simulator/src

source/noisy_simulator/src/instrument.rs

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1// Copyright (c) Microsoft Corporation.
2// Licensed under the MIT License.
3
4//! This module contains the `Instrument` struct, used to make measurments
5//! in a quantum system.
6
7#[cfg(test)]
8mod tests;
9
10use crate::{operation::Operation, Error, SquareMatrix, TOLERANCE};
11use nalgebra::{DMatrix, DVector};
12
13/// An instrument is the means by which we make measurements on a quantum system.
14pub struct Instrument {
15 operations: Vec<Operation>,
16 summed_operation: SquareMatrix,
17 summed_effect: SquareMatrix,
18 summed_effect_transpose: SquareMatrix,
19 summed_kraus_operators: Vec<SquareMatrix>,
20}
21
22impl Instrument {
23 /// Creates a new instrument.
24 pub fn new(operations: Vec<Operation>) -> Result<Self, Error> {
25 if operations.is_empty() {
26 return Err(Error::FailedToConstructInstrument(
27 "there should be at least one Operation".to_string(),
28 ));
29 }
30
31 let number_of_qubits = operations
32 .first()
33 .expect("there should be at least an element")
34 .number_of_qubits();
35
36 if !operations
37 .iter()
38 .all(|op| number_of_qubits == op.number_of_qubits())
39 {
40 return Err(Error::FailedToConstructInstrument(
41 "all Operations should target the same number of qubits".to_string(),
42 ));
43 }
44
45 let summed_operation: SquareMatrix = operations.iter().map(Operation::matrix).sum();
46 let summed_effect: SquareMatrix = operations.iter().map(Operation::effect_matrix).sum();
47 let summed_effect_transpose = summed_effect.transpose();
48 let summed_kraus_operators = summed_kraus_operators(&operations)?;
49
50 Ok(Self {
51 operations,
52 summed_operation,
53 summed_effect,
54 summed_effect_transpose,
55 summed_kraus_operators,
56 })
57 }
58
59 /// Return operation corresponding to the i-th outcome.
60 #[must_use]
61 pub fn operation(&self, i: usize) -> &Operation {
62 &self.operations[i]
63 }
64
65 /// Return the matrix corresponding to the sum over all
66 /// operations in this instrument:
67 /// Σᵢ Σₖ (Kᵢₖ ⊗ Kᵢₖ*)
68 /// where ⊗ denotes the Kronecker product
69 /// and * denotes the complex conjugate (entry-wise).
70 #[must_use]
71 pub fn non_selective_operation_matrix(&self) -> &SquareMatrix {
72 &self.summed_operation
73 }
74
75 /// Return Kraus operators of the operation corresponding to non selective evolution.
76 #[must_use]
77 pub fn non_selective_kraus_operators(&self) -> &Vec<SquareMatrix> {
78 &self.summed_kraus_operators
79 }
80
81 /// Return total effect Σᵢ Σₖ (Kᵢₖ† Kᵢₖ), where † denotes the adjoint.
82 #[must_use]
83 pub fn total_effect(&self) -> &SquareMatrix {
84 &self.summed_effect
85 }
86
87 /// Return transposed total effect Σᵢ Σₖ (Kᵢₖ† Kᵢₖ)^T, where † denotes the adjoint.
88 #[must_use]
89 pub fn total_effect_transposed(&self) -> &SquareMatrix {
90 &self.summed_effect_transpose
91 }
92
93 /// Return number of operations/outcomes in this instrument.
94 #[must_use]
95 pub fn num_operations(&self) -> usize {
96 self.operations.len()
97 }
98}
99
100fn summed_kraus_operators(operations: &[Operation]) -> Result<Vec<SquareMatrix>, Error> {
101 let choi_matrix = compute_choi_matrix(operations);
102 let (choi_dim, _) = choi_matrix.shape();
103 let eigen_decomposition = choi_matrix.symmetric_eigen();
104 let eigenvectors = eigen_decomposition.eigenvectors;
105 let eigenvalues = eigen_decomposition.eigenvalues;
106 let mut summed_kraus_operators = Vec::with_capacity(eigenvalues.len());
107
108 let (krows, kcols) = operations[0].kraus_operators()[0].shape();
109 for (col, eigenvalue) in eigenvalues.iter().enumerate() {
110 if *eigenvalue > 0. {
111 let mut kraus_operator = SquareMatrix::zeros(krows, kcols);
112 let sqrt_eigenvalue = eigenvalue.sqrt();
113
114 // Performance note: for performance reasons we transpose the
115 // Kraus operators before storing them. We want to store each column
116 // of the eigenvector matrix as a Kraus matrix in row major order, and
117 // then transpose it. But because nalgebra stores and iterates over
118 // its matrices in column major order, we can skip the transposition,
119 // since it is already implicit.
120 //
121 // See noisy_simulator/src/operation.rs/Operation::new for more details.
122 for row in 0..choi_dim {
123 let idx = kraus_operator.vector_to_matrix_index(row);
124 kraus_operator[idx] = sqrt_eigenvalue * eigenvectors[(row, col)];
125 }
126
127 summed_kraus_operators.push(kraus_operator);
128 } else if *eigenvalue < -TOLERANCE {
129 return Err(Error::FailedToConstructInstrument(format!(
130 "failed to decompose Choi matrix, lambda_i = {eigenvalue}"
131 )));
132 }
133 }
134
135 Ok(summed_kraus_operators)
136}
137
138fn compute_choi_matrix(operations: &[Operation]) -> SquareMatrix {
139 operations
140 .iter()
141 .map(|op| {
142 op.kraus_operators()
143 .iter()
144 .map(|k| {
145 let vectorized_k = vectorize(k);
146 &vectorized_k * &vectorized_k.adjoint()
147 })
148 .sum::<SquareMatrix>()
149 })
150 .sum()
151}
152
153/// Stacks the columns of `matrix` into a single column vector.
154///
155/// Performance note: Typically vectorization stacks the
156/// rows of a matrix into a single column vector. But since we
157/// transposed all matrices until now, we stack the columns instead.
158///
159/// See `noisy_simulator/src/operation.rs/Operation::new` for more details.
160fn vectorize<T: nalgebra::Scalar + Copy>(matrix: &DMatrix<T>) -> DVector<T> {
161 DVector::<T>::from_iterator(matrix.len(), matrix.iter().copied())
162}
163