The FIRE Summit 2024

Permanent URI for this collectionhttp://hdl.handle.net/1903/33513

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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