142 citations · 284 across the 19 of their papers we have counts for
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A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification
Davis Arthur, Prasanna Date
Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited b…
Towards a comprehensive visualization of structure in data
Joan Garriga, Frederic Bartumeus
Dimensional data reduction methods are fundamental to explore and visualize large data sets. Basic requirements for unsupervised data exploration are simplicity, flexibility and sc…
Training Deep Neural Networks with Constrained Learning Parameters
Prasanna Date, Christopher D. Carothers, John E. Mitchell +2
Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing…
Balanced k-Means Clustering on an Adiabatic Quantum Computer
Davis Arthur, Prasanna Date
Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced -means cl…
QUBO Formulations for Training Machine Learning Models
Prasanna Date, Davis Arthur, Lauren Pusey-Nazzaro
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scal…
Adiabatic Quantum Linear Regression
Prasanna Date, Thomas Potok
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning m…