Quantum computational chemistry

By Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C. Benjamin, Xiao Yuan. Reviews of Modern Physics, 2018

1,392 citations recorded by the source corpus.

With small quantum computers becoming a reality, first applications are eagerly sought. Quantum chemistry presents a spectrum of computational problems, from relatively easy to classically intractable. Algorithms for the easiest of these have been run on the first quantum computers. But an urgent question is, how well will these algorithms scale to go beyond what is possible classically? This review presents strategies employed to construct quantum algorithms for quantum chemistry, with the goal that quantum computers will eventually answer presently inaccessible questions, for example, in transition metal catalysis or important biochemical reactions.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/b49ca782a7c16ca66345ccc8c73beabb6bf2ce92

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Citations in the corpus

Variational quantum algorithms (2020, methodology): “As discussed in Section IIIA the VQE algorithm [16] (and associated architectures) have shown promising advances towards the goal of performing molecular quantum chemistry on quantum computers [226], with large scale implementation already being executed [227].”
Theory of variational quantum simulation (2018, methodology): “As the state preparation and measurement procedures may be hard to simulate by a classical computer [17–19], hybrid or variational quantum simulation can have an intrinsic quantum advantage over VCS and it has been proposed as the standard tool for studying quantum computational chemistry [46, 47].”
Fundamental limits of quantum error mitigation (2021, neutral): “~ φ ðKÞ Q :1⁄4 k1⁄41 q1⁄41 EðkÞ q ðφÞ h i : (21)”
qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (2019, background): “The energy minimization is done by a classical computer, so quantum resources are only used for the classically intractable parts (state preparation and energy evaluation) of the calculation [10].”
Error mitigation with Clifford quantum-circuit data (2020, background): “[3] Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon C Benjamin, and Xiao Yuan, “Quantum computational chemistry,” Reviews of Modern Physics 92, 015003 (2020).”
Noise-induced barren plateaus in variational quantum algorithms (2020, methodology): “Here the i and j indices range over “occupied” orbitals whereas the a and b indices range over “virtual” orbitals [38, 49].”
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model (2020, background): “Our work paves the way toward simulating strongly correlated quantum systems [19, 22–32] on existing digital quantum computers.”
An adaptive variational algorithm for exact molecular simulations on a quantum computer (2018, background): “2h, and (2) the increased number of parameters selected in the ADAPT calculations in Fig.”
VQE method: a short survey and recent developments (2021, methodology): “Several comprehensive reviews covering VQE have been published recently.[21, 22, 23, 24] The goal of this work is to present recent advances in methods that produce shorter VQE circuits or provide higher accuracy.”