1 citations · 1 across the 12 of their papers we have counts for
4 papers · 1 filter
Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers
Yuqian Huo, Daniel Leeds, Jason Ludmir +2
Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale qu…
Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
Jason Han, Nicholas S. DiBrita, Daniel Leeds +3
Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for dist…
QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility
Jason Ludmir, Ian Martin, Nicholas S. DiBrita +1
Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable trainin…
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Jason Zev Ludmir, Sophia Rebello, Jacob Ruiz +1
Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently eme…