14 citations · 14 across the 3 of their papers we have counts for
7 papers
Dimension-adaptive machine-learning-based quantum state reconstruction
Sanjaya Lohani, Sangita Regmi, Joseph M. Lukens +3
We introduce an approach for performing quantum state reconstruction on systems of qubits using a machine-learning-based reconstruction system trained exclusively on qubits…
Exploring the relationship between the faithfulness and entanglement of two qubits
Gabriele Riccardi, Daniel E. Jones, Xiao-Dong Yu +2
A conceptually simple and experimentally prevalent class of entanglement witnesses, known as fidelity witnesses, detect entanglement via a state's fidelity with a pure reference st…
Machine learning pipeline for quantum state estimation with incomplete measurements
Onur Danaci, Sanjaya Lohani, Brian T. Kirby +1
Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness…
Exploring classical correlations in noise to recover quantum information using local filtering
Daniel E. Jones, Brian T. Kirby, Gabriele Riccardi +2
A general quantum channel consisting of a decohering and a filtering element carries one qubit of an entangled photon pair. As we apply a local filter to the other qubit, some mutu…
Machine learning assisted quantum state estimation
Sanjaya Lohani, Brian T. Kirby, Michael Brodsky +2
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…
Compensation of polarization dependent loss using noiseless amplification and attenuation
R. A. Brewster, B. T. Kirby, J. D. Franson +1
Polarization dependent loss (PDL) is a serious problem that hinders the transfer of polarization qubits through quantum networks. Recently it has been shown that the detrimental ef…