Quantum Machine Learning for Image Classification
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Project description
From self-driving cars to medical diagnoses, machine learning (ML) has revolutionized our world in the last few decades. Additionally, quantum computing has considerable promise for the future, with superposition and entanglement making quantum algorithms much more efficient and effective than their classical counterparts. Quantum ML aims to apply these quantum principles to the groundbreaking field of ML, such as for image classification. Convolutional neural networks (CNNs) are very effective for classification, so we study a quantum convolutional neural network (QCNN) that is trained to distinguish between handwritten numbers in the MNIST dataset and compared to a similar-sized classical network. After tuning the QCNN and quantum encoding, the QCNN achieved comparable accuracy to the classical network.
Mentor review
My mentor helped me set and adjust deadlines for tasks, based on my progress and how certain topics were going. In the earlier stages of the project when my algorithm was not working and I had to make numerous significant changes, she helped me pick which steps to take in trying to make a starting viable project. She also evaluated my research on a higher level and brought her knowledge of scientific research and quantum mechanics into supporting me. I had never written a research paper but she helped walk me through each step and build an outline for my paper, which really made the task clear for me.