19 citations · 19 across the 1 of their papers we have counts for
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Assessing the Benefits and Risks of Quantum Computers
Travis L. Scholten, Carl J. Williams, Dustin Moody +5
Quantum computing is an emerging technology with potentially far-reaching implications for national prosperity and security. Understanding the timeframes over which economic benefi…
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…
Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor
Amir H. Karamlou, William A. Simon, Amara Katabarwa +3
In the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is…
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack
Daniel Mills, Seyon Sivarajah, Travis L. Scholten +1
Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarki…
Classifying single-qubit noise using machine learning
Travis L. Scholten, Yi-Kai Liu, Kevin Young +1
Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An i…