AutoSolvate: A Toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules [PDF]
, 2022 The availability of large, high-quality data sets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational data set generation of solution-phase molecular ...
Fang, Liu +3 more
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Differentiable matrix product states for simulating variational quantum computational chemistry [PDF]
Quantum, 2023 Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver (VQE) is a promising heuristic quantum algorithm to ...
Chu Guo +3 more
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On the potentially transformative role of auxiliary-field quantum Monte Carlo in quantum chemistry: A highly accurate method for transition metals and beyond [PDF]
, 2022 Approximate solutions to the ab initio electronic structure problem have been a focus of theoretical and computational chemistry research for much of the past century, with the goal of predicting relevant energy differences to within “chemical accuracy” (
Shiwei, Zhang +4 more
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Application of fermionic marginal constraints to hybrid quantum algorithms
New Journal of Physics, 2018 Many quantum algorithms, including recently proposed hybrid classical/quantum algorithms, make use of restricted tomography of the quantum state that measures the reduced density matrices, or marginals, of the full state.
Nicholas C Rubin +2 more
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∆-Machine Learning for Quantum Chemistry Prediction of Solution-phase Molecular Properties at the Ground and Excited States [PDF]
, 2023 Due to the limitation of solvent models, quantum chemistry calculated solution-phase molecular properties often deviates from experimental measurements. Recently, ∆-machine learning (∆-ML) was shown to be a promising approach to correcting errors in the ...
Pinyuan, Li +3 more
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Predicting band gaps of semiconductors with quantum chemistry [PDF]
EPJ Web of Conferences, 2020 The following article gives a brief introduction to quantum chemistry and its application to the prediction of band gaps of inorganic and organic semiconductors.
Dittmer Anneke
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Introduction to “Quantum computing for chemistry, material science and biotechnology” [PDF]
Digital DiscoveryMatthias Degroote, Joonho Lee and Pauline Ollitrault introduce the Digital Discovery themed collection on “Quantum computing for chemistry, material science and biotechnology”.Matthias Degroote, Joonho Lee and Pauline Ollitrault introduce the Digital ...
Matthias Degroote +2 more
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Hybrid Quantum-Classical Eigensolver without Variation or Parametric Gates
Quantum Reports, 2021 The use of near-term quantum devices that lack quantum error correction, for addressing quantum chemistry and physics problems, requires hybrid quantum-classical algorithms and techniques. Here, we present a process for obtaining the eigenenergy spectrum
Pejman Jouzdani, Stefan Bringuier
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2MOLCAS as a development platform for quantum chemistry software
, 2004 This work presents the quantum chemistry package MOLCAS, with emphasis on its usefulness as a platform for developing new quantum chemical codes, and the reader is assumed to be familiar with such a process.
Luis Serrano‐Andrés +11 more
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Quantum simulation of quantum field theories as quantum chemistry
Journal of High Energy Physics, 2020 Conformal truncation is a powerful numerical method for solving generic strongly-coupled quantum field theories based on purely field-theoretic technics without introducing lattice regularization.
Junyu Liu, Yuan Xin
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