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Quantum classification, factor width, k-incoherence

This example accompanies the "The complexity of quantum state classification" paper 1.

In this tutorial, we will cover the concepts of the so-called "learnability" of quantum states along with related settings of "factor width" and the notion of \(k\)-incoherence of a matrix. More details can be found in the aforementioned paper.

Learnability of quantum states

To illustrate \(k\)-learnability, consider the following generalization of the trine states to four states in three dimensions, called the tetrahedral states:

\[ \begin{aligned} \ket{\psi_1} = \frac{1}{\sqrt{3}} (\ket{0} + \ket{1} + \ket{2}), \quad & \ket{\psi_2} = \frac{1}{\sqrt{3}} (\ket{0} - \ket{1} - \ket{2}), \\ \ket{\psi_3} = \frac{1}{\sqrt{3}} (-\ket{0} - \ket{1} + \ket{2}), \quad & \ket{\psi_4} = \frac{1}{\sqrt{3}} (-\ket{0} + \ket{1} - \ket{2}). \end{aligned} \]
import numpy as np


def tetrahedral_states() -> list[np.ndarray]:
    return [
        np.array([1, 1, 1], dtype=np.complex128) / np.sqrt(3),
        np.array([1, -1, -1], dtype=np.complex128) / np.sqrt(3),
        np.array([-1, -1, 1], dtype=np.complex128) / np.sqrt(3),
        np.array([-1, 1, -1], dtype=np.complex128) / np.sqrt(3),
    ]


print(tetrahedral_states())

Out:

[array([0.57735027+0.j, 0.57735027+0.j, 0.57735027+0.j]), array([ 0.57735027+0.j, -0.57735027+0.j, -0.57735027+0.j]), array([-0.57735027+0.j, -0.57735027+0.j,  0.57735027+0.j]), array([-0.57735027+0.j,  0.57735027+0.j, -0.57735027+0.j])]

This set of states is \(2\)-learnable, upon receiving one of them, one can always guess two states from which it was selected without error.

from toqito.state_props import learnability

states = tetrahedral_states()
learnability_result = learnability(states, k=2)
print(f"Average classification error (k=2): {learnability_result['value']}")

Out:

Average classification error (k=2): 8.148409457769262e-06

Indeed, can be accomplished using the following POVM \(M_{i,j} = \frac{1}{2} \ket{\phi_{i,j}} \bra{\phi_{i,j}}\), where

\[ \begin{aligned} \ket{\phi_{1,2}} &= \frac{1}{\sqrt{2}}(\ket{1} + \ket{2}), \quad \ket{\phi_{1,3}} = \frac{1}{\sqrt{2}}(\ket{0} + \ket{1}), \quad \ket{\phi_{1,4}} = \frac{1}{\sqrt{2}}(\ket{0} + \ket{2}), \\ \ket{\phi_{2,3}} &= \frac{1}{\sqrt{2}}(\ket{0} - \ket{2}), \quad \ket{\phi_{2,4}} &= \frac{1}{\sqrt{2}}(\ket{0} - \ket{1}), \quad \ket{\phi_{3,4}} = \frac{1}{\sqrt{2}}(\ket{1} - \ket{2}). \end{aligned} \]
def povm_residual(states: list[np.ndarray], povm: dict[tuple[int, int], np.ndarray]) -> tuple[float, float]:
    """Return the maximum POVM reconstruction and support violations."""
    dim = states[0].shape[0]
    total = sum(povm.values(), np.zeros((dim, dim), dtype=np.complex128))
    sum_residual = np.max(np.abs(total - np.eye(dim)))

    zero_residual = 0.0
    for idx, state in enumerate(states):
        for subset, operator in povm.items():
            if idx not in subset:
                zero_residual = max(zero_residual, np.abs(np.vdot(state, operator @ state)))
    return sum_residual, zero_residual


phi_vectors = {
    (0, 1): np.array([0, 1, 1], dtype=np.complex128) / np.sqrt(2),
    (0, 2): np.array([1, 1, 0], dtype=np.complex128) / np.sqrt(2),
    (0, 3): np.array([1, 0, 1], dtype=np.complex128) / np.sqrt(2),
    (1, 2): np.array([1, 0, -1], dtype=np.complex128) / np.sqrt(2),
    (1, 3): np.array([1, -1, 0], dtype=np.complex128) / np.sqrt(2),
    (2, 3): np.array([0, 1, -1], dtype=np.complex128) / np.sqrt(2),
}
povm_elements = {pair: 0.5 * np.outer(vec, vec.conj()) for pair, vec in phi_vectors.items()}

sum_res, zero_res = povm_residual(states, povm_elements)
print(f"max|Σ M_S - I|             : {sum_res:.2e}")
print(f"max|⟨ψ_i|M_S|ψ_i⟩| (i∉S)   : {zero_res:.2e}")

Out:

max|Σ M_S - I|             : 2.22e-16
max|⟨ψ_i|M_S|ψ_i⟩| (i∉S)   : 0.00e+00

By contrast however, these states are not \(k=1\)-learnable:

states = tetrahedral_states()
learnability_result = learnability(states, k=1)
print(f"Average classification error (k=1): {learnability_result['value']}")

Out:

Average classification error (k=1): 0.2500001320013096

k-Incoherence

The notion of \(k\)-incoherence comes from 2. For a positive integers, \(k\) and \(n\), the matrix \(X \in \text{Pos}(\mathbb{C}^n)\) is called \(k\)-incoherent if there exists a positive integer \(m\), a set \(S = \{|\psi_0\rangle, |\psi_1\rangle,\ldots, |\psi_{m-1}\rangle\} \subset \mathbb{C}^n\) with the property that each \(|\psi_i\rangle\) has at most \(k\) non-zero entries, and real scalars \(c_0, c_1, \ldots, c_{m-1} \geq 0\) for which

\[ X = \sum_{j=0}^{m-1} c_j |\psi_j\rangle \langle \psi_j|. \]

This function checks if the provided density matrix mat is k-incoherent. It returns True if mat is k-incoherent and False if mat is not.

For example, the following matrix is \(2\)-incoherent

\[ \begin{pmatrix} 2 & 1 & 2 \\ 1 & 2 & -1 \\ 2 & -1 & 5 \end{pmatrix} \]

Indeed, one can verify this numerically using the is_k_incoherent.

from toqito.matrix_props import is_k_incoherent

mat = np.array([[2, 1, 2], [1, 2, -1], [2, -1, 5]])
print(is_k_incoherent(mat, 2))

Out:

True

Factor width

Another closely related definition to \(k\)-incoherence is that of factor width 345 below.

Let \(k\) be a positive integer. The factor width of a positive semidefinite matrix \(X\) is the smallest \(k\) such that it is \(k\)-incoherent.

For example, the matrix \(\operatorname{diag}(1, 1, 0)\) has factor width at most \(1\).

from toqito.matrix_props import factor_width

diag_mat = np.diag([1, 1, 0])
result = factor_width(diag_mat, k=1)
print(result["feasible"])

Out:

True

Conversely, the rank-one matrix \(\frac{1}{2}\begin{pmatrix} 1 & 1 \\ 1 & 1 \end{pmatrix}\) is not \(1\)-factorable.

hadamard = np.array([[1, 1], [1, 1]], dtype=np.complex128) / 2
result = factor_width(hadamard, k=1)
print(result["feasible"])

Out:

False

This example comes directly from 4. Suppose we want to determine the factor width of the rank-\(3\) matrix

\[ M = \begin{bmatrix} 2 & 1 & 1 & -1 \\ 1 & 2 & 0 & 1 \\ 1 & 0 & 2 & -1 \\ -1 & 1 & -1 & 2 \end{bmatrix}. \]

We start by finding a basis for \(S := \text{range}(M)\), which can be done by picking a linearly independent set of \(r = 3\) columns of \(M\): \(S = \operatorname{span}\{(2,1,1,-1), (1,2,0,1), (1,0,2,-1)\}\). Then \(R_0 = \{S\}\) and we proceed recursively:

\[ \begin{aligned} R_1 = \{S_1, S_2, S_3, S_3\}, \quad \text{where} \quad S_1 & = \operatorname{span}\{(0,1,-1,1), (0,1,-3,1)\}, \\ S_2 & = \operatorname{span}\{(1,0,2,-1), (3,0,2,-3)\}, \\ S_3 & = \operatorname{span}\{(1,2,0,1), (3,2,0,-1)\}, \ \ \text{and} \\ S_4 & = \operatorname{span}\{(1,1,1,0), (3,3,1,0)\}. \end{aligned} \]

To determine whether or not \(M\) is \(3\)-incoherent, we let \(\Pi_1\), \(\Pi_2\), \(\Pi_3\), and \(\Pi_4\) be the orthogonal projections onto \(S_1\), \(S_2\), \(S_3\), and \(S_4\), respectively. We then use semidefinite programming to determine whether or not there exist matrices \(M_1, M_2, M_3, M_4 \in \text{Pos}(\mathbb{C}^4)\) for which

\[ M = M_1 + M_2 + M_3 + M_4, \quad \text{and} \quad M_j = \Pi_j M_j \Pi_j \quad \text{for all} \quad j \in \{1,2,3,4\}. \]

Indeed, such matrices do exist:

\[ M_1 = \begin{bmatrix} 0 & 0 & 0 & 0 \\ 0 & 1 & -1 & 1 \\ 0 & -1 & 1 & -1 \\ 0 & 1 & -1 & 1 \end{bmatrix}, \ M_2 = \begin{bmatrix} 1 & 0 & 0 & -1 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ -1 & 0 & 0 & 1 \end{bmatrix}, \ M_3 = \begin{bmatrix} 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{bmatrix}, \ M_4 = \begin{bmatrix} 1 & 1 & 1 & 0 \\ 1 & 1 & 1 & 0 \\ 1 & 1 & 1 & 0 \\ 0 & 0 & 0 & 0 \end{bmatrix}, \]

so \(M\) is \(3\)-incoherent. For example, we can verify this numerically using the factor_width.

mat = np.array(
    [
        [2, 1, 1, -1],
        [1, 2, 0, 1],
        [1, 0, 2, -1],
        [-1, 1, -1, 2],
    ],
    dtype=np.complex128,
)
result = factor_width(mat, k=3)
print(sum(result["factors"]))

Out:

[[ 1.99999997+0.j  0.99999998+0.j  0.99999999+0.j -0.99999998+0.j]
 [ 0.99999998+0.j  1.99999997+0.j  0.        +0.j  0.99999998+0.j]
 [ 0.99999999+0.j  0.        +0.j  2.        +0.j -0.99999999+0.j]
 [-0.99999998+0.j  0.99999998+0.j -0.99999999+0.j  1.99999997+0.j]]

To similarly determine whether or not \(M\) is \(2\)-incoherent, we proceed further with the recursive construction by computing

\[ R_2 = \{S_{\{1,2\}}, S_{\{1,3\}}, S_{\{2,3\}}, S_{\{3,4\}}\}, \quad \text{where} \quad S_{\{1,2\}} = S_{\{1,4\}} = S_{\{2,4\}} = \operatorname{span}\{(0,0,1,0)\}, \]
\[ S_{\{1,3\}} = \operatorname{span}\{(0,1,0,1)\}, \quad S_{\{2,3\}} = \operatorname{span}\{(1,0,0,-1)\}, \quad \text{and} \quad S_{\{3,4\}} = \operatorname{span}\{(1,1,0,0)\}. \]

It follows that the only vectors in \(\text{range}(M)\) with \(k = 2\) or fewer non-zero entries are the scalar multiples of \({v_1} := (0,0,1,0)\), \({v_2} := (0,1,0,1)\), \({v_3} := (1,0,0,-1)\), and \({v_4} := (1,1,0,0)\), so \(M\) is \(2\)-incoherent if and only if there exist non-negative real scalars \(c_1\), \(c_2\), \(c_3\), and \(c_4\) for which

\[ M = c_1 v_1 v_1^* + c_2 v_2 v_2^* + c_3 v_3 v_3^* + c_4 v_4 v_4^*. \]

It is straightforward to use semidefinite programming (or even just solve by hand in this small example) to see that no such scalars exist, so \(X\) is not \(2\)-incoherent. It follows that \(X\) has factor width \(3\).

result = factor_width(mat, k=2)
print(result["feasible"])
# mkdocs_gallery_thumbnail_path = 'figures/logo.png'

Out:

False

Total running time of the script: ( 0 minutes 0.223 seconds)

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  1. Nathaniel Johnston, Benjamin Lovitz, Vincent Russo, and Jamie Sikora. The complexity of quantum state classification. 2025. arXiv:X. ↩

  2. Nathaniel Johnston, Shirin Moein, Rajesh Pereira, and Sarah Plosker. Absolutely k-incoherent quantum states and spectral inequalities for the factor width of a matrix. Physical Review A, 106(5):052417, 2022. ↩

  3. Francesco Barioli and Abraham Berman. The maximal CP-rank of rank \(k\) completely positive matrices. Linear Algebra and its Applications, 363:17–33, 2003. doi:10.1016/S0024-3795(02)00250-1. ↩

  4. Nathaniel Johnston, Shirin Moein, and Sarah Plosker. The factor width rank of a matrix. Linear Algebra and its Applications, 716:32–59, 2025. doi:10.1016/j.laa.2025.03.016. ↩↩

  5. Erik G Boman, Doron Chen, Ojas Parekh, and Sivan Toledo. On factor width and symmetric H-matrices. Linear algebra and its applications, 405:239–248, 2005. doi:10.1016/j.laa.2005.03.029. ↩