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An Ensemble Approach for Compressive Sensing with Quantum
We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing.
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning
We formulated an MNIST classification problem using a deep convolutional neural network that used samples from a quantum RBM to train the MNIST classifier and compared the results with an MNIST classifier trained with the original MNIST training data set, as well as an MNIST classifier trained using classical RBM samples.
Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach
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 automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest.
Ontology-Grounded Topic Modeling for Climate Science Research
In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable.
