65 citations · 105 across the 10 of their papers we have counts for
13 papers
The Impact of Markovian Errors on Random Circuits
Anthony M. Polloreno, Timothy Proctor, Robin Blume-Kohout
Randomized benchmarking (RB) returns a single decay parameter or gate error rate, but when the noise is gate-dependent that parameter is notoriously difficult to interpret. We stud…
Logical gate set tomography
Piper C. Wysocki, Juan Gonzalez De Mendoza, Karl Mayer +11
Quantum error correction (QEC) can strongly suppress noise, but predicting whether a computation succeeds requires understanding not only how much effective noise persists after QE…
Benchmarking the computational power of quantum computers
Timothy Proctor, Oliver Hart, Oliver Widzowski Maupin +27
Quantum computing hardware is advancing rapidly toward utility-scale machines that will enable scientific breakthroughs. Many teams are pursuing distinct and difficult-to-compare r…
Helios: A 98-qubit trapped-ion quantum computer
Anthony Ransford, M. S. Allman, Jake Arkinstall +183
We report on Quantinuum Helios, a 98-qubit trapped-ion quantum processor based on the quantum charge-coupled device (QCCD) architecture. Helios features Ba hyperfine…
Efficient simulation of Clifford circuits with small Markovian errors
Ashe Miller, Corey Ostrove, Jordan Hines +3
Classical simulation of noisy quantum circuits is essential for understanding quantum computing experiments. It enables scalable error characterization, analysis of how noise impac…
When Clifford benchmarks are sufficient; estimating application performance with scalable proxy circuits
Seth Merkel, Timothy Proctor, Samuele Ferracin +4
The goal of benchmarking is to determine how far the output of a noisy system is from its ideal behavior; this becomes exceedingly difficult for large quantum systems where classic…
Measuring error rates of mid-circuit measurements
Daniel Hothem, Jordan Hines, Charles Baldwin +3
High-fidelity mid-circuit measurements, which read out the state of specific qubits in a multiqubit processor without destroying them or disrupting their neighbors, are a critical…
A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations
Avimita Chatterjee, Sonny Rappaport, Anish Giri +5
Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gau…
A Practical Introduction to Benchmarking and Characterization of Quantum Computers
Akel Hashim, Long B. Nguyen, Noah Goss +16
Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthr…
Benchmarking quantum computers
Timothy Proctor, Kevin Young, Andrew D. Baczewski +1
The rapid pace of development in quantum computing technology has sparked a proliferation of benchmarks for assessing the performance of quantum computing hardware and software. Go…
Pauli Noise Learning for Mid-Circuit Measurements
Jordan Hines, Timothy Proctor
Current benchmarks for mid-circuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying their perfor…
What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
Daniel Hothem, Ashe Miller, Timothy Proctor
Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers…
Scalable Full-Stack Benchmarks for Quantum Computers
Jordan Hines, Timothy Proctor
Quantum processors are now able to run quantum circuits that are infeasible to simulate classically, creating a need for benchmarks that assess a quantum processor's rate of errors…