230 citations · 426 across the 25 of their papers we have counts for
9 papers · 1 filter
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…
ManQala: Game-Inspired Strategies for Quantum State Engineering
Onur Danaci, Wenlei Zhang, Robert Coleman +6
The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here,…
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…
Accelerating Progress Towards Practical Quantum Advantage: The Quantum Technology Demonstration Project Roadmap
Paul Alsing, Phil Battle, Joshua C. Bienfang +32
Quantum information science and technology (QIST) is a critical and emerging technology with the potential for enormous world impact and is currently invested in by over 40 nations…
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…
Generation of polarization entanglement via the quantum Zeno effect
Ian C. Nodurft, Harry C. Shaw, Ryan T. Glasser +2
The quantum Zeno effect reveals that the continuous observation of a quantum system can result in significant alterations to its evolution. Here, we present a method for establishi…
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…
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…