Machine learning assisted quantum state estimation
This work addresses the problem of efficient and accurate quantum state estimation for quantum experiments, offering incremental improvements by integrating machine learning into existing tomography methods.
The authors tackled quantum state tomography by developing a machine learning framework that reconstructs quantum states from coincidence measurements, demonstrating via simulations that it achieves functionally equivalent states to traditional methods with significantly enhanced average fidelity, including in noisy and partial data scenarios.
We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.