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Quantum state reconstruction via disentanglement with sequential optimization algorithm
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DOI：10.1088/2632-2153/ad88d6.png)
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In this work, we report a novel quantum state reconstruction process based on the disentanglement algorithm. We propose a sequential disentanglement scheme, which can transform an unknown quantum state into a product of computational zero states. The inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubits one by one, we reduce the required measurements with only individual qubit measurement and identify the transformation unitary efficiently. Variational quantum circuit and reinforcement learning methods are used for the quantum circuit design for continuous and discrete quantum gates implementation. Demonstrations with our proposal for the reconstruction of the random states are presented. Our method is universal and imposes no specific ansatz or constraint on the quantum state.
Keyword:
reinforcement learning
sequential disentanglement
quantum state reconstruction
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4.6
论文数:
1.1K
被引数:
3.4K
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