13 citations · 15 across the 5 of their papers we have counts for
4 papers · 1 filter
Multi-Qubit Correction for Quantum Annealers
Ramin Ayanzadeh, John Dorband, Milton Halem +1
We present \emph{multi-qubit correction} (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a se…
An Ensemble Approach for Compressive Sensing with Quantum
Ramin Ayanzadeh, Milton Halem, Tim Finin
We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Since executing quantum machine instructions on a quant…
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…
Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach
Ramin Ayanzadeh, Milton Halem, Tim Finin
We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning…