Structured Factorization Approaches for Quantum State Tomography

• URL: http://arxiv.org/abs/2607.01608v1
• Date: Thu, 02 Jul 2026 02:23:58 GMT
• Title: Structured Factorization Approaches for Quantum State Tomography
• Abstract summary: We introduce a unified framework, termed structured factorization, that builds on BurerMonteiro-type factorization.<n>We formulate quantum state tomography as an optimization problem over the factor space from measurement data.
• Score: 18.729698238758264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the complexity of quantum state tomography (QST) scales exponentially with system size, exploiting priors such as low-rankness, tensor-network structures, and neural-network representations is essential for scalable QST in terms of sample complexity and parameter complexity. In this paper, we introduce a unified framework, termed structured factorization, that builds on BurerMonteiro-type factorization by parametrizing the density matrix as $FF^\dagger$, where the factor $F$ is constrained to belong to a structured model class. This factorization guarantees physical validity by construction while allowing a broad range of structural priors to be incorporated directly through the choice of the factor space, ranging from the generic Cholesky decomposition to low-rank matrices, matrix product operators, and neural density operators based on multilayer perceptron and transformer architectures. Building on this structured factorization framework, we formulate QST as an optimization problem over the factor space from measurement data. We first develop a unified statistical analysis of the sample complexity of least-squares estimation for a broad class of structured quantum states. We then propose a projected gradient descent method that operates directly on the factor space and accommodates a wide range of structural parametrizations and reconstruction objectives. To further exploit the geometry of the maximum-likelihood estimation formulation and the constraints on the factors, we derive a power method that yields a step-size-free algorithm with fast convergence, recovering Covers method as a special case when the factor is unconstrained.

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