86 citations · 408 across the 11 of their papers we have counts for
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quant-ph2023★ 85 cited
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
quant-ph2023★ 1 cited
A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes
Travis L. Scholten, Derrick Perry, Joseph Washington +2
Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a simi…