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20212026
most citedFully scalable randomized benchmarking without motion reversal

12 citations · 14 across the 7 of their papers we have counts for

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8 papers · 1 filter

quant-ph2026

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…

quant-ph2026

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…

quant-ph2025

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…

quant-ph2024

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…

quant-ph2024

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…

quant-ph2023★ 12 cited

Fully scalable randomized benchmarking without motion reversal

Jordan Hines, Daniel Hothem, Robin Blume-Kohout +2

We introduce binary randomized benchmarking (BiRB), a protocol that streamlines traditional RB by using circuits consisting almost entirely of i.i.d. layers of gates. BiRB reliably…

quant-ph2023

Predictive Models from Quantum Computer Benchmarks

Daniel Hothem, Jordan Hines, Karthik Nataraj +2

Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks -- such as algorithmic or rand…

quant-ph2023★ 2 cited

Learning a quantum computer's capability

Daniel Hothem, Kevin Young, Tommie Catanach +1

Accurately predicting a quantum computer's capability -- which circuits it can run and how well it can run them -- is a foundational goal of quantum characterization and benchmarki…