activity
20072023
most citedA variational eigenvalue solver on a quantum processor

4.6k citations · 31.4k across the 155 of their papers we have counts for

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Showing 2015Show all

14 papers · 1 filter

quant-ph2015★ 806 cited

Scalable Quantum Simulation of Molecular Energies

P. J. J. O'Malley, R. Babbush, I. D. Kivlichan +27

We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qub…

quant-ph2015★ 23 cited

Faster than Classical Quantum Algorithm for dense Formulas of Exact Satisfiability and Occupation Problems

Salvatore Mandrà, Gian Giacomo Guerreschi, Alán Aspuru-Guzik

We present an exact quantum algorithm for solving the Exact Satisfiability (XSAT) problem, which belongs to the important NP-complete complexity class. The algorithm is based on an…

quant-ph2015★ 54 cited

Microwave Boson Sampling

Borja Peropadre, Gian Giacomo Guerreschi, Joonsuk Huh +1

The first post-classical computation will most probably be performed not on a universal quantum computer, but rather on a dedicated quantum hardware. A strong candidate for achievi…

quant-ph2015★ 3 cited

The role of interparticle interaction and environmental coupling in a two-particle open quantum system

Humberto G. Laguna, Robin P. Sagar, David D. Tempel +1

The effects of bath coupling on an interacting two-particle quantum system are studied using tools from information theory. Shannon entropies of the one (reduced) and two-particle…

quant-ph2015★ 2.2k cited

The theory of variational hybrid quantum-classical algorithms

Jarrod R. McClean, Jonathan Romero, Ryan Babbush +1

Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme kn…

cs.LG2015★ 897 cited

Convolutional Networks on Graphs for Learning Molecular Fingerprints

David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre +4

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary…