13 citations · 15 across the 3 of their papers we have counts for
3 papers
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning
Jennifer Sleeman, John Dorband, Milton Halem
Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sample…
SAT-based Compressive Sensing
Ramin Ayanzadeh, Milton Halem, Tim Finin
We propose to reduce the original well-posed problem of compressive sensing to weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition approach that linearly s…
Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty
Ramin Ayanzadeh, Seyedahmad Mousavi, Milton Halem +1
Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional sign…