Formally: My research focuses on integrating quantum low-density parity-check (qLDPC) error-correcting codes into quantum convolutional neural networks (QCNNs) to improve their stability under quantum error and noise. I aim to use these codes as state stabilizers and correcting for quantum gate errors, enhancing training and testing accuracy on quantum hardware with finite sampling constraints. I have developed and validated a QCNN baseline through statevector simulations and extended it with a novel QCNN+qLDPC architecture. By leveraging IBM Quantum's platform, I systematically tune hyperparameters and benchmark performance under realistic quantum noise conditions. Ultimately, my work explores practical strategies to make quantum machine learning models more robust and scalable for near-term quantum devices. I am working towards publication and hope to share it here soon!
More informally: I study how to make quantum computers work better and more reliably. I focus on helping quantum AI handle mistakes and noise so it can learn more accurately. My work combines simulations and real quantum computers to build smarter, more practical quantum technologies.
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: 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 Based Artifical Intelligence with Robot Interface [Website]
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.