211 citations · 264 across the 9 of their papers we have counts for
9 papers
Quantum-HPC Software Stacks and the openQSE Reference Architecture: A Survey
Amir Shehata, Brian Austin, Tom Beck +22
Quantum resources are increasingly integrated into high-performance computing (HPC) and cloud environments, but quantum high-performance computing (QHPC) software stacks remain iso…
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…
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
Alberto Di Meglio, Karl Jansen, Ivano Tavernelli +43
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage…
Quantum Information Science and Technology for Nuclear Physics. Input into U.S. Long-Range Planning, 2023
Douglas Beck, Joseph Carlson, Zohreh Davoudi +98
In preparation for the 2023 NSAC Long Range Plan (LRP), members of the Nuclear Science community gathered to discuss the current state of, and plans for further leveraging opportun…
Quantum Annealing for Jet Clustering with Thrust
Andrea Delgado, Jesse Thaler
Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-ene…
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…
Particle Track Classification Using Quantum Associative Memory
Gregory Quiroz, Lauren Ice, Andrea Delgado +1
Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrim…