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20182026
most citedTurbulence correction with artificial neural networks

145 citations · 501 across the 21 of their papers we have counts for

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14 papers · 1 filter

quant-ph2026

Structured leakage in OAM-encoded qubits revealed by distributed quantum feature extraction

Yayu Mo, Nazifa Rumman, Sanjeev Shapkota +2

Orbital angular momentum (OAM) modes have emerged as an important platform for high-dimensional quantum communication but remain vulnerable to environmental noise and channel imper…

quant-ph2026

Induced random mixed states are symmetric Dirichlet mixtures

Brian T. Kirby, Alexander C. B. Greenwood, Sanjaya Lohani +1

We establish an exact equivalence in distribution between -dimensional random mixed states induced by partial traces over -dimensional environments and the Mai-Alquier distri…

quant-ph2026

Adaptive AI for Pulse-Level Quantum Control

Sanjeev Shapkota, Yayu Mo, Sanjaya Lohani

The Control Variational Quantum Eigensolver (ctrl-VQE) directly optimizes microwave pulses to enable faster and lower-error quantum-state preparation, but its continuous control la…

quant-ph2026

Quantum Erasure Imaging: Complementary Modalities from Delayed-Choice Erasure

Sean D Huver, Sanjaya Lohani

Quantum Erasure Imaging (QEI) turns delayed-choice erasure into a practical imaging protocol. Entangled photon pairs encode two classical modalities, absorption and a phas…

quant-ph2022★ 8 cited

Demonstration of machine-learning-enhanced Bayesian quantum state estimation

Sanjaya Lohani, Joseph M. Lukens, Atiyya A. Davis +6

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum fo…

quant-ph2022

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…

quant-ph2022★ 3 cited

Classical Optical Analogue of Quantum Discord

Jacob M. Leamer, Wenlei Zhang, Nicholas J. Savino +4

Quantum discord has been shown to be a resource for quantum advantage in addition to quantum entanglement. While many experiments have demonstrated classical analogies of entanglem…

quant-ph2022★ 13 cited

Data-Centric Machine Learning in Quantum Information Science

Sanjaya Lohani, Joseph M. Lukens, Ryan T. Glasser +2

We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we…

quant-ph2021★ 15 cited

Improving application performance with biased distributions of quantum states

Sanjaya Lohani, Joseph M. Lukens, Daniel E. Jones +3

We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mi…

quant-ph2020

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…

quant-ph2020★ 29 cited

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…

quant-ph2020★ 29 cited

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…

quant-ph2020★ 86 cited

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…

quant-ph2019★ 9 cited

Coherent Optical Communications Enhanced by Machine Intelligence

Sanjaya Lohani, Ryan T. Glasser

Uncertainty in discriminating between different received coherent signals is integral to the operation of many free-space optical communications protocols, and is often difficult w…