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

Informally: We research methods to make quantum computers more reliable. Specifically, we focus on improving the robustness of quantum AI systems against errors and noise arising from inherent quantum phenomena, allowing them to learn more accurately in real-world conditions. Our work integrate simulations and experiments on real quantum hardware to develop more practical and scalable quantum technologies.

Formally: Our research focuses on embedding Quantum Convolutional Neural Networks (QCNNs) within state-of-the-art Quantum Error Correction (QEC) codes to evaluate their performance on noisy intermediate-scale quantum (NISQ) devices. The goal is to leverage modern QEC techniques to correct for quantum error and therefore improving QCNN learning convergence and reliability under realistic hardware conditions. We test the effects of various QEC codes through a combination of large-scale simulations on university high-performance computing resources and experimental runs on IBM Quantum hardware. From the experimentation, we are working towards publishing our results, and I hope to share the them 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: Quantum Enhanced Feature Space [PDF]
Abstract: Current problem: limitations on successful solution for problems when feature space becomes large high-dimensional
Suggested solution: utilize controlled entanglement and interference to exploit exponentially growing quantum state space
Goal: present new class of tools for exploring the applications of noisy intermediate scale quantum computers to machine learning for improved computational power and efficacy

Name: Quantum Error Code Correction Background [PDF]
Abstract: This presentation introduces the essential codes of quantum error correction, like the 3-qubit repetition code and 9-qubit Shor code.

Name: Quantum Information Background [PDF]
Abstract: This presentation introduces the essential preliminaries of quantum information processing, from the basics of qubits and quantum gates to the critical role of quantum error correction in making quantum computing practical.

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 [Python Code] [Website]
Description: Machine learning autonomous agents exploring and learning in a dynamically generated virtual ecosystem.