Abstract:
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 whe...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Quantum Engineering ( Volume: 2)
Funding Agency:
IBM-HBCU Quantum Center, Howard University, Washington, DC, USA
Tulane University, New Orleans, LA, USA
IBM-HBCU Quantum Center, Howard University, Washington, DC, USA
Massachusetts Institute of Technology, Cambridge, MA, USA
Tulane University, New Orleans, LA, USA
United States Army Research Laboratory, Adelphi, MD, USA
Tulane University, New Orleans, LA, USA
IBM-HBCU Quantum Center, Howard University, Washington, DC, USA
Tulane University, New Orleans, LA, USA
IBM-HBCU Quantum Center, Howard University, Washington, DC, USA
Massachusetts Institute of Technology, Cambridge, MA, USA
Tulane University, New Orleans, LA, USA
United States Army Research Laboratory, Adelphi, MD, USA
Tulane University, New Orleans, LA, USA