145 citations · 992 across the 45 of their papers we have counts for
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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…
On the experimental feasibility of quantum state reconstruction via machine learning
Sanjaya Lohani, Thomas A. Searles, Brian T. Kirby +1
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 constrai…
Experimental Violation of the Leggett-Garg Inequality Using the Polarization of Classical Light
Wenlei Zhang, Ravi K. Saripalli, Jacob M. Leamer +2
In contrast to Bell's inequalities which test the correlations between multiple spatially separated systems, the Leggett-Garg inequalities test the temporal correlations between me…
Robust polarimetry via convex optimization
Jacob M. Leamer, Wenlei Zhang, Ravi K. Saripalli +2
We present mathematical methods, based on convex optimization, for correcting non-physical coherency matrices measured in polarimetry. We also develop the method for recovering the…
Spatial Mode Correction of Single Photons using Machine Learning
Narayan Bhusal, Sanjaya Lohani, Chenglong You +6
Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, t…
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…