Title: Error-Corrected Quantum Convolutional Neural Networks via Quantum Low-Density Parity-Check Codes
Advisor: Dr. Yadav Animesh, Assistant Professor, Department of Electrical Engineering and Computer Science, Ohio University
Duration: August 2024 - Present

Informally: I research how to make quantum computers work better and more reliably. Specifically, I focus on helping quantum AI handle mistakes and noise due to inherit quantum mechanic properties so it can learn more accurately. My work combines simulations and real quantum computers to build smarter, more practical quantum technologies.

Formally: My research focuses on integrating quantum low-density parity-check (qLDPC) codes into quantum convolutional neural networks (QCNNs) to improve their stability under quantum error and noise. I aim to use these codes to correct for quantum errors and decoherence to improve training accuracy on quantum hardware under finite sampling constraints. I leverage IBM Quantum's platform and my university's high-performance computers to experiment with realistic quantum conditions. Ultimately, this allows me to explore and apply procedures to make quantum machine learning models more robust and scalable for near-term quantum devices. My advisor and I are working towards publication of the results from experimentation and I hope to share it here soon!

Name: Final Paper for Hardware for Deep Learning: Quantum Algorithms for Deep Convolutional Neural Networks [PDF]
Preview: Computer scientists utilize principles of quantum mechanics, mathematics, and computer science in quantum computing. By borrowing concepts from each field scientists can rigorously define both a broad and narrow theoretical model of a quantum computer and later apply it to the real world. These theoretical models, such as the result...

Name: Quantum Algorithms for Deep Convolutional Neural Networks [PDF]
Abstract: Current problem: it is difficult to implement non linearities with quantum unitaries
Suggested solution: a new quantum tomography algorithm with norm guarantees, and new applications of probabilistic sampling in the context of information processing
Goal: The QCNN is particularly interesting for deep networks and could allow new frontiers in image recognition, by using more or larger convolution kernels, larger or deeper inputs

Name: Computational Neuroscience
Description: Working on building a modular neural network structure to mimic brain lobes (prefrontal cortex, motor cortex, occipital lobe, thalamus, brain stem, temporal, and parietal lobes), with each lobe specialized for a task, but connected to other lobes for more complex dynamics. I plan to use these models for project for autonomous agent exploration and learning using neural networks and reinforcement learning

Name: Credit Card Fraud Detection using a Quantum Support Vector Machine [Code]
Description: Applying a Quantum Support Vector Machine for credit card fraud detection in Qiskit. The applied QSVM makes use of quantum enhanced feature space optimization based on the research paper, Supervised Learning with Quantum Enhanced Feature Spaces. The classifier used is a Variational Quantum Classifier with 29-dimensional ZZ feature mapping for a 2-qubit quantum kernel.

Name: My Cloud A.I. Controlling a Robot Body [AI Website Version]
Description: A robot I built that communicates with a remote server running artifical intelligence software I designed and programmed from scratch. Meaning, you can talk to the exact same Jarcey that is running in this robot, either on this website context switching, on jarcey's own website, or any device with an internet connection. This is because all communication, data, and programs are transimitted to, from, and processed on the same one remote server.

Name: Autonomous AI Agents Ecosystem Simulator [Code]
Description: Machine learning autonomous agents exploring and learning in a dynamically generated virtual ecosystem.