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20182024
most citedTequila: A platform for rapid development of quantum algorithms

68 citations · 115 across the 4 of their papers we have counts for

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quant-ph20242 cited

Enhancing initial state overlap through orbital optimization for faster molecular electronic ground-state energy estimation

Pauline J. Ollitrault, Cristian L. Cortes, Jerome F. Gonthier +7

The quantum phase estimation algorithm stands as the primary method for determining the ground state energy of a molecular electronic Hamiltonian on a quantum computer. In this con…

quant-ph20211 cited

Towards the Simulation of Large Scale Protein-Ligand Interactions on NISQ-era Quantum Computers

Fionn D. Malone, Robert M. Parrish, Alicia R. Welden +6

We explore the use of symmetry-adapted perturbation theory (SAPT) as a simple and efficient means to compute interaction energies between large molecular systems with a hybrid meth…

quant-ph202044 cited

Natural Evolutionary Strategies for Variational Quantum Computation

Abhinav Anand, Matthias Degroote, Alán Aspuru-Guzik

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized…

quant-ph202068 cited

Tequila: A platform for rapid development of quantum algorithms

Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza +15

Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are al…

quant-ph2020

Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions

Abhinav Anand, Jonathan Romero, Matthias Degroote +1

The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical and experimental explorations will most…

quant-ph2019

An Artificial Spiking Quantum Neuron

Lasse Bjørn Kristensen, Matthias Degroote, Peter Wittek +2

Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processin…