7 papers
HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning
Nicholas S. DiBrita, Jason Han, Younghyun Cho +2
Quantum machine learning (QML) algorithms have demonstrated early promise across hardware platforms, but remain difficult to interpret due to the inherent opacity of quantum state…
Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization
Nicholas S. DiBrita, Jason Han, Krishna Bhatia +3
We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a targe…
SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
Jason Ludmir, Nicholas S. DiBrita, Jason Han +1
Emerging quantum sensors are increasingly envisioned as components of hybrid quantum-classical high-performance computing, enabling new capabilities in scientific, cyber-physical,…
Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
Jason Han, Nicholas S. DiBrita, Daniel Leeds +3
Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for dist…
ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
Nicholas S. DiBrita, Jason Han, Tirthak Patel
Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not ye…
EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
Jason Han, Nicholas S. DiBrita, Younghyun Cho +2
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variab…