activity
19992022
most citedObservation of topological phenomena in a programmable lattice of 1,800 qubits

331 citations · 1.8k across the 21 of their papers we have counts for

collaborators

21 papers

quant-ph2022★ 5 cited

Probing flux and charge noise with macroscopic resonant tunneling

Alexander M. Whiticar, Anatoly Y. Smirnov, Trevor Lanting +8

We report on measurements of flux and charge noise in an rf-SQUID flux qubit using macroscopic resonant tunneling (MRT). We measure rates of incoherent tunneling from the lowest en…

quant-ph2022★ 249 cited

Quantum critical dynamics in a 5000-qubit programmable spin glass

Andrew D. King, Jack Raymond, Trevor Lanting +24

Experiments on disordered alloys suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Du…

quant-ph2022★ 221 cited

Coherent quantum annealing in a programmable 2000-qubit Ising chain

Andrew D. King, Sei Suzuki, Jack Raymond +21

Quantum simulation has emerged as a valuable arena for demonstrating and understanding the capabilities of near-term quantum computers. Quantum annealing has been used successfully…

quant-ph2021★ 11 cited

Architectural considerations in the design of a third-generation superconducting quantum annealing processor

Kelly Boothby, Colin Enderud, Trevor Lanting +46

Early generations of superconducting quantum annealing processors have provided a valuable platform for studying the performance of a scalable quantum computing technology. These s…

quant-ph2019★ 153 cited

Scaling advantage in quantum simulation of geometrically frustrated magnets

Andrew D. King, Jack Raymond, Trevor Lanting +51

The promise of quantum computing lies in harnessing programmable quantum devices for practical applications such as efficient simulation of quantum materials and condensed matter s…

quant-ph2019★ 66 cited

Demonstration of nonstoquastic Hamiltonian in coupled superconducting flux qubits

I. Ozfidan, C. Deng, A. Y. Smirnov +45

Quantum annealing (QA) is a heuristic algorithm for finding low-energy configurations of a system, with applications in optimization, machine learning, and quantum simulation. Up t…