62 citations · 156 across the 17 of their papers we have counts for
20 papers
Identifying overparameterization in Quantum Circuit Born Machines
Andrea Delgado, Francisco Rios, Kathleen E. Hamilton
In machine learning, overparameterization is associated with qualitative changes in the empirical risk landscape, which can lead to more efficient training dynamics. For many param…
An entanglement-based volumetric benchmark for near-term quantum hardware
Kathleen E. Hamilton, Nouamane Laanait, Akhil Francis +8
We introduce a volumetric benchmark for near-term quantum platforms based on the generation and verification of genuine entanglement across n-qubits using graph states and direct s…
Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
Travis S. Humble, Andrea Delgado, Raphael Pooser +23
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phe…
Unsupervised Quantum Circuit Learning in High Energy Physics
Andrea Delgado, Kathleen E. Hamilton
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum cir…
Quantum computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
Mode connectivity in the QCBM loss landscape
Kathleen E. Hamilton, Emily Lynn, Vicente Leyton-Ortega +2
Quantum circuit Born machines (QCBMs) and training via variational quantum algorithms (VQAs) are key applications for near-term quantum hardware. QCBM ansätze designs are unique in…