205 citations · 344 across the 8 of their papers we have counts for
8 papers
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…
Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning
Annie Naveh, Imogen Fitzgerald, Anna Phan +2
Enhancing classical machine learning (ML) algorithms through quantum kernels is a rapidly growing research topic in quantum machine learning (QML). A key challenge in using kernels…
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…
Gate Set Tomography
Erik Nielsen, John King Gamble, Kenneth Rudinger +3
Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around…
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…
Behavior of the maximum likelihood in quantum state tomography
Travis L Scholten, Robin Blume-Kohout
Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in th…