145 citations · 501 across the 21 of their papers we have counts for
22 papers
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…
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…
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…
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…
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work introduces a new method designed for Bayesian deep learning called scalable Bayesian Monte Carlo (SBMC). The method is comprised of a model and an algorithm. The model in…
Classification of Single Photons in Higher-Order Spatial Modes via Convolutional Neural Networks
Manon P. Bart, Sita Dawanse, Nicholas J. Savino +7
Spatial modes are a promising candidate for encoding information for classical and quantum optical communication due to their potential high information capacity. Unfortunately, co…
Comparison of parallel SMC and MCMC for Bayesian deep learning
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work systematically compares parallel implementations of consistent (asymptotically unbiased) Bayesian deep learning algorithms: sequential Monte Carlo sampler (SMC$_\parallel…
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…
Deep learning for enhanced free-space optical communications
Manon P. Bart, Nicholas J. Savino, Paras Regmi +8
Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of coherent light used in ON-OFF keying free-space optical communication. Here we pres…
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…
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…
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…
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…
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…
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…
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…
Dispersion Characterization and Pulse Prediction with Machine Learning
Sanjaya Lohani, Erin M. Knutson, Wenlei Zhang +1
In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses…
Generative Machine Learning for Robust Free-Space Communication
Sanjaya Lohani, Ryan T. Glasser
Realistic free-space optical communications systems suffer from turbulent propagation of light through the atmosphere and detector noise at the receiver, which can significantly de…
Turbulence correction with artificial neural networks
Sanjaya Lohani, Ryan T. Glasser
We design an optical feedback network making use of machine learning techniques and demonstrate via simulations its ability to correct for the effects of turbulent propagation on o…
On the use of deep neural networks in optical communications
Sanjaya Lohani, Erin M. Knutson, Matthew O'Donnell +2
Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep…