Office of Undergraduate Research

Permanent URI for this communityhttp://hdl.handle.net/1903/20157

Emphasizing equitable and inclusive access to research opportunities, the University of Maryland's Office of Undergraduate Research (OUR) empowers undergraduates and faculty to engage and succeed in inquiry, creative activity, and scholarship. This collection includes materials shared by undergraduate researchers during OUR events. It also encompasses materials from Undergraduate Research Day 2020, Undergraduate Research Day 2021, and Undergraduate Research Day 2022, which were organized by the Maryland Center for Undergraduate Research.

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Subject: search.filters.subject.FIRE Quantum Machine Learning

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Now showing 1 - 4 of 4
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    Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions
    (2024) Bapanapalli, Abhinav; Mahmud, Ateef; Nadig, Chiraag; Thakker, Krish; Jabeen, Shabnam
    Predicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.
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    The Quantum Zeno Effect
    (2024) Haswell, Meg; Ramanathan, Nithika; Ketner, Hannah; Jabeen, Shabnam
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    Data Augmentations on Quantum Wasserstein Generative Adversarial Networks
    (2024-12-11) Lee, Joey; Lai, Devon; Banerjee, Ayan; Jabeen, Shabnam
    The goal of this project is to explore Quantum Wasserstein Generative Adversarial Networks (QWGANs) and address its limitations by incorporating data augmentation techniques such as Elastic Transforms and Gaussian/Poisson Noise to simulate real-world imperfections, such as noise and distortions. With this we test the robustness of the QWGAN framework and compare QWGAN performance with such data modification techniques against one another
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    Hybrid Quantum Vision Transformers for Particle Image Classification
    (2024) Christopher, Darwin; Mahendran, Smithi; Shah, Saloni; Tanjore, Sid; Jabeen, Shabnam