On the experimental feasibility of quantum state reconstruction via machine learning
This work addresses the practical feasibility and resource requirements of quantum state reconstruction for researchers working with quantum computing platforms, particularly in the context of high-dimensional systems.
This paper investigates the resource scaling of machine learning-based quantum state reconstruction for up to four qubits, specifically for pure states, and evaluates its performance in low-count scenarios. The method was implemented on an IBM Q quantum computer and compared against Maximum Likelihood Estimation.
We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction.