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A Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantum devices. However, standard estimation methods become computationally prohibitive as the system size increases due to the exponential growth of the density matrix, describing a quantum state, with the number of qubits. We propose a low-rank tensor-network framework for mixed-state quantum state tomography based on a block tensor train (Block-TT) factorization. Specifically, the density matrix is represented as the c

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