Abstract
Abstract We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements. Through simulations, we demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states. Compared to traditional methods, our system offers the advantage of faster speed. Additionally, by training our system with measurement results containing simulated noise sources, we could show a significant improvement in average fidelity compared to typical reconstruction methods. 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 IBMQ, a real quantum computer.
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