Data-Centric Machine Learning in Quantum Information Science

Abstract

has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Dimension-adaptive machine-learning-based quantum state reconstruction

“…While we limited our discussion to systems based on qubits and collections of qubits, restricting the possible Hilbert space dimensions to powers of two, extensions to arbitrary dimensions are straightforward. Further, based on previous results showing the impact of engineering training sets to emphasize specific system features [31,32], further improvements could potentially be found by developing training sets that explicitly consider the distribution of their reduced density matrices.…”

“…In that case, we can sample this distribution using any pre-trained network and extract the lower bound specifically for this use case. The development of custom distributions of random quantum states that mimic various general features of quantum systems could potentially limit the impact of mismatched training and test distributions in practice [32].…”

“…In general, QST consists of the preparation and experimental collection of measurement results and the purely classical and computational step of recovering the density matrix most consistent with the measurement results, known as quantum state reconstruction [7][8][9]. Various methods exist for performing quantum state reconstruction, including maximum likelihood estimation [8][9][10][11][12][13][14], Bayesian inference [6,[15][16][17][18][19][20][21][22][23][24][25], and machine-learning-based techniques -supervised learning [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], semi-supervised learning [43][44][45], and reinforcement learning [46].…”