10 papers · 1 filter
Hardware-Aware Compilation and Execution of Bivariate Bicycle Codes on Neutral-Atom Systems
Jason Ludmir, Aditya Ranjan, Nicholas S. DiBrita +2
Quantum computers are noisy; without quantum error correction (QEC), deep programs fail as qubits lose information due to decoherence. Among QEC approaches, bivariate bicycle (BB)…
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,…
Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers
Yuqian Huo, Daniel Leeds, Jason Ludmir +2
Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale qu…
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…