7.2k citations · 24.9k across the 107 of their papers we have counts for
11 papers · 2 filters
Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm
Murphy Yuezhen Niu, Vadim Smelyanskyi, Paul Klimov +30
Two-level-system (TLS) defects in amorphous dielectrics are a major source of noise and decoherence in solid-state qubits. Gate-dependent non-Markovian errors caused by TLS-qubit c…
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…
Supplementary information for "Quantum supremacy using a programmable superconducting processor"
Frank Arute, Kunal Arya, Ryan Babbush +74
This is an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 201…
Optimal fermion-to-qubit mapping via ternary trees with applications to reduced quantum states learning
Zhang Jiang, Amir Kalev, Wojciech Mruczkiewicz +1
We introduce a fermion-to-qubit mapping defined on ternary trees, where any single Majorana operator on an -mode fermionic system is mapped to a multi-qubit Pauli operator actin…
Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon, Michael Broughton, Jarrod R. McClean +5
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.…
Diabatic gates for frequency-tunable superconducting qubits
R. Barends, C. M. Quintana, A. G. Petukhov +43
We demonstrate diabatic two-qubit gates with Pauli error rates down to in as fast as 18 ns using frequency-tunable superconducting qubits. This is achieved by…