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Quantum state estimation based on deep learning
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Quantum state estimation based on deep learning
We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements.Through simulations,we have demonstrated that our approach generates functionally equiva-lent reconstructed states for a wide range of pure and mixed input states.Compared with traditional methods,our system offers the advantage of faster speed.Additionally,by training our system with measurement results containing simulated noise sources,the system shows a significant improvement in average fidelity compared with typical reconstruction meth-ods.We also found that constraining the variational manifold to physical states,i.e.,positive semi-definite density matrices,greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise.Finally,we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform,a real quantum computer.
deep learningquantum state estimationIBM quantum processor
肖皓文、韩枝光
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School of Information and Communication Engineering,Hainan University,Haikou 570228,China
deep learning quantum state estimation IBM quantum processor
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