Dr. Jennifer Sleeman Profile

Dr. Jennifer Sleeman

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Publications (2)

Proceedings Article | 12 September 2021 Presentation + Paper

Physics-aware deep edge detection network

Kinjal Patel, Jennifer Sleeman, Milton Halem

Proceedings Volume 11859, 1185908 (2021) https://doi.org/10.1117/12.2600327

KEYWORDS: Edge detection, Backscatter, Clouds, Satellites, Satellite imaging, Network architectures, Meteorological satellites, Information theory, Atmospheric sensing

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Proceedings Article | 20 May 2020 Presentation + Paper

A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning

Jennifer Sleeman, John Dorband, Milton Halem

Proceedings Volume 11391, 113910B (2020) https://doi.org/10.1117/12.2558832

KEYWORDS: Binary data, Computer programming, Image compression, Quantum communications, Data modeling, Quantum computing, Image restoration, Machine learning, Annealing, Convolutional neural networks

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Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.

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