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This paper offers a theoretical framework for sorting multiqubit entangled states into SLOCC-convertibility classes using conserved operators, with explicit classification counts for up to six qubits. For quantum information researchers, the practical value is a more refined catalogue of entanglement types and a link between state-convertibility constraints and parent Hamiltonians. The results are mathematical and do not yet show direct implications for hardware or applications.
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Classification of quantum states through conserved operators

arXiv3 min read

arXiv:2610.20988v1 Announce Type: new Abstract: Determining whether quantum states can be interconverted by a prescribed set of transformations is a fundamental problem in quantum information. Here we develop a general classification framework based on conserved operators (COs), i.e., not necessarily hermitian matrices having the state of interest as an eigenstate. We first show that COs belonging to the algebra generating the allowed transformations can be obtained from the kernel of a quantum covariance matrix. Their number determines the dimension of the accessible orbit, while their algebraic structure and eigenvalues provide further constraints on state convertibility. We establish general bounds on the number of COs and prove that highest-weight states, like separable states in the case of $n$ qudits, attain the maximum. We also show how these operators generate parent Hamiltonians having the characterized state as an exact ground state. We then also consider positive zero-eigenvalue COs, which encode forbidden directions of reduced density matrices and incorporate rank-based constraints into the same framework. Applying this approach to multiqubit SLOCC, we obtain complementary classifications from algebraic and rank COs. While for $n\leq 3$ qubits both lead to the usual classes, for $n=4$ qubits they distinguish 17 and 20 types separately, and 29 when combined. The combined classification yields 287 fixed types and $4$ continuous families for $n=5$ qubits, and 4926 fixed types and 147 continuous families for $n=6$ qubits, providing, to our knowledge, the most refined exhaustive classification of SLOCC-convertibility classes based on such data.

Abstract:Determining whether quantum states can be interconverted by a prescribed set of transformations is a fundamental problem in quantum information. Here we develop a general classification framework based on conserved operators (COs), i.e., not necessarily hermitian matrices having the state of interest as an eigenstate. We first show that COs belonging to the algebra generating the allowed transformations can be obtained from the kernel of a quantum covariance matrix. Their number determines the dimension of the accessible orbit, while their algebraic structure and eigenvalues provide further constraints on state convertibility. We establish general bounds on the number of COs and prove that highest-weight states, like separable states in the case of $n$ qudits, attain the maximum. We also show how these operators generate parent Hamiltonians having the characterized state as an exact ground state. We then also consider positive zero-eigenvalue COs, which encode forbidden directions of reduced density matrices and incorporate rank-based constraints into the same framework. Applying this approach to multiqubit SLOCC, we obtain complementary classifications from algebraic and rank COs. While for $n\leq 3$ qubits both lead to the usual classes, for $n=4$ qubits they distinguish 17 and 20 types separately, and 29 when combined. The combined classification yields 287 fixed types and $4$ continuous families for $n=5$ qubits, and 4926 fixed types and 147 continuous families for $n=6$ qubits, providing, to our knowledge, the most refined exhaustive classification of SLOCC-convertibility classes based on such data.

arXiv:2610.20988 [quant-ph]

(or arXiv:2610.20988v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.20988 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

supplementary_data.pdf

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