Dimension-adaptive machine learning-based quantum state reconstruction

Lohani, Sanjaya; Regmi, Sangita; Lukens, Joseph M.; Glasser, Ryan T.; Searles, Thomas A.; Kirby, Brian T.

doi:10.1007/s42484-022-00088-8

Dimension-adaptive machine learning-based quantum state reconstruction

Journal Article · Sun Dec 25 23:00:00 EST 2022 · Quantum Machine Intelligence

DOI:https://doi.org/10.1007/s42484-022-00088-8· OSTI ID:1928949

^[1]; ^[1]; ^[2]; Glasser, Ryan T. ^[3]; ^[1]; ^[4]

Univ. of Illinois, Chicago, IL (United States)
Arizona State Univ., Tempe, AZ (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Tulane Univ., New Orleans, LA (United States)
Tulane Univ., New Orleans, LA (United States); US Army Research Laboratory (USARL), Adelphi, MD (United States)

Here, we introduce an approach for performing quantum state reconstruction on systems of n qubits using a machine learning-based reconstruction system trained exclusively on m qubits, where m ≥ n. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall saving of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine learning systems for each Hilbert space.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); US Army Research Office (ARO); US Army Research Laboratory (USARL)

Grant/Contract Number:: AC05-00OR22725; SC0012704

OSTI ID:: 1928949

Journal Information:: Quantum Machine Intelligence, Journal Name: Quantum Machine Intelligence Journal Issue: 1 Vol. 5; ISSN 2524-4906

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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