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19992026
most citedSupplementary information for "Quantum supremacy using a programmable superconducting processor"

7.2k citations · 10.4k across the 152 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

quant-ph2019

Benchmarking Supercomputers with the Jülich Universal Quantum Computer Simulator

Dennis Willsch, Hannes Lagemann, Madita Willsch +3

We use a massively parallel simulator of a universal quantum computer to benchmark some of the most powerful supercomputers in the world. We find nearly ideal scaling behavior on t…

quant-ph2019★ 7.2k cited

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…

quant-ph2019★ 124 cited

Benchmarking the Quantum Approximate Optimization Algorithm

Madita Willsch, Dennis Willsch, Fengping Jin +2

The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state, the energy expectatio…

cond-mat.stat-mech2019★ 25 cited

Exponential damping induced by random and realistic perturbations

Jonas Richter, Fengping Jin, Lars Knipschild +4

Given a quantum many-body system and the expectation-value dynamics of some operator, we study how this reference dynamics is altered due to a perturbation of the system's Hamilton…

quant-ph2019★ 11 cited

Real-time simulation of flux qubits used for quantum annealing

Madita Willsch, Dennis Willsch, Fengping Jin +2

The real-time flux dynamics of up to three superconducting quantum interference devices (SQUIDs) are studied by numerically solving the time-dependent Schrödinger equation. The num…

cs.LG2019★ 186 cited

Support vector machines on the D-Wave quantum annealer

Dennis Willsch, Madita Willsch, Hans De Raedt +1

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2…