1 citations · 1 across the 5 of their papers we have counts for
5 papers
Balancing Expressivity and Overfitting in Quantum Gaussian Process Regression
Saasha Joshi, Udson C. Mendes, Luke C. G. Govia
Active learning is a paradigm of machine learning that can be utilized to model expensive black-box functions by training a surrogate model from actively queried training points. T…
Observable Estimation in the Absence of Classical Verification
Samantha V. Barron, Bradley Mitchell, Vinay Tripathi +45
The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of c…
Indoor Occupancy Classification using a Compact Hybrid Quantum-Classical Model Enabled by a Physics-Informed Radar Digital Twin
Sebastian Ratto, Ahmed N. Sayed, Neda Rojhani +6
Indoor occupancy classification enables privacy-preserving monitoring in settings such as remote elder care, where presence information helps triage alarms without cameras or weara…
When Clifford benchmarks are sufficient; estimating application performance with scalable proxy circuits
Seth Merkel, Timothy Proctor, Samuele Ferracin +4
The goal of benchmarking is to determine how far the output of a noisy system is from its ideal behavior; this becomes exceedingly difficult for large quantum systems where classic…
Nonlinear input transformations are ubiquitous in quantum reservoir computing
L. C. G. Govia, G. J. Ribeill, G. E. Rowlands +1
The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential…