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Quantum state reconstruction via sequential optimization algorithm reducing optimizable parameters
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AI
DOI：10.1088/1402-4896/adfb12.png)
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Abstract
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Quantum state reconstruction is an important tool in quantum information science. Based on the idea of sequential disentanglement, we propose a quantum state reconstruction algorithm that reduces the number of parameters to be optimized. For transforming the arbitrary quantum state into a tensor product of states in the computational basis, we give the conclusion that it is not necessary to apply a global unitary transformation in the optimization of sequential disentanglement. In detail, for disentangling arbitrary n-qubit pure state into , we simply apply the quantum operator U to qubits. This means that the number of parameters that need to be optimized has been further reduced, thereby saving quantum resources. Finally, numerical simulations verify the feasibility of the proposed algorithm.
Keywords:
quantum state reconstruction
sequential disentanglement
parameter optimization
quantum resources
n-qubit pure state
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