The Variational Quantum Eigensolver: A review of methods and best practices

Tilly, Jules; Chen, Hongxiang; Cao, Shuxiang; Picozzi, Dario; Setia, Kanav; Liu, Ying; Grant, Edward; Wossnig, Leonard; Rungger, Ivan; Booth, George H.; Tennyson, Jonathan

doi:10.1016/j.physrep.2022.08.003

Cited by 461 publications

(273 citation statements)

References 516 publications

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“…As such, they are not guaranteed to yield better approximations with respect to classical methods. Moreover, as the molecule size increases, VQE requires a larger number of sub cycles for Hamiltonian averaging (because of more terms in the Hamiltonian), projecting to unfeasibly long runtime for systems of practical importance [ 90 , 102 , 106 , 107 ]. In addition, the training/optimization of the ansatz parameters (the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol{\theta}$\end{document} in Figure 3 ) has already been proven to be nondetermistic polynomial-time hard (NP-hard) [ 108 ].…”

Section: Quantum Algorithms For the Molecular Modelling Of Biomoleculesmentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

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Section: Quantum Algorithms For the Molecular Modelling Of Biomoleculesmentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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“…VQE attempts to find the minimum eigenvalue of a wave function encoded in parameterized rotation gates and entangled qubits [26]. It is then run several times while tuning the rotation gates.…”

Section: B Benchmarksmentioning

confidence: 99%

Reducing Runtime Overhead via Use-Based Migration in Neutral Atom Quantum Architectures

Litteken

Baker

Chong

2022

2022 IEEE International Conference on Quantum Computing and Engineering (QCE)

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Neutral atoms are a promising choice for scalable quantum computing architectures. Features such as long distance interactions and native multiqubit gates offer reductions in communication costs and operation count. However, the trapped atoms used as qubits can be lost over the course of computation and due to adverse environmental factors. The value of a lost computation qubit cannot be recovered and requires the reloading of the array and rerunning of the computation, greatly increasing the number of runs of a circuit. Software mitigation strategies exist [1] but exhaust the original mapped locations of the circuit slowly and create more spread out clusters of qubits across the architecture decreasing the probability of success. We increase flexibility by developing strategies that find all reachable qubits, rather only adjacent hardware qubits. Second, we divide the architecture into separate sections, and run the circuit in each section, free of lost atoms. Provided the architecture is large enough, this resets the circuit without having to reload the entire architecture. This increases the number of effective shots before reloading by a factor of two for a circuit that utilizes 30% of the architecture. We also explore using these sections to parallelize execution of circuits, reducing the overall runtime by a total 50% for 30 qubit circuit. These techniques contribute to a dynamic new set of strategies to combat the detrimental effects of lost computational space.

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“…In this contribution, we leverage the standard formulation of the IEF-PCM to include solvation effects in the flagship algorithm of quantum simulation for noisy intermediate scale quantum (NISQ) devices: the variational quantum eigensolver (VQE). − The choice of this method has been dictated by the recent literature that has showed its successful applications on near-term quantum processors to simulate molecules, condensed matter physics, and other phenomena of physico-chemical interest. − In particular, we exploit a specific flavor of VQE where the trial wavefunction is built exploiting an adaptive concept . In the following, we will refer to the new algorithm as PCM–VQE.…”

Section: Introductionmentioning

confidence: 99%

Quantum Simulation of Molecules in Solution

Castaldo

Jahangiri

Delgado

et al. 2022

J. Chem. Theory Comput.

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Quantum chemical calculations on quantum computers have been focused mostly on simulating molecules in the gas phase. Molecules in liquid solution are, however, most relevant for chemistry. Continuum solvation models represent a good compromise between computational affordability and accuracy in describing solvation effects within a quantum chemical description of solute molecules. In this work, we extend the variational quantum eigensolver to simulate solvated systems using the polarizable continuum model. To account for the state dependent solute–solvent interaction we generalize the variational quantum eigensolver algorithm to treat non-linear molecular Hamiltonians. We show that including solvation effects does not impact the algorithmic efficiency. Numerical results of noiseless simulations for molecular systems with up to 12 spin-orbitals (qubits) are presented. Furthermore, calculations performed on a simulated noisy quantum hardware (IBM Q, Mumbai) yield computed solvation free energies in fair agreement with the classical calculations.

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The Variational Quantum Eigensolver: A review of methods and best practices

Cited by 461 publications

References 516 publications

Quantum computing algorithms: getting closer to critical problems in computational biology

Quantum computing algorithms: getting closer to critical problems in computational biology

Reducing Runtime Overhead via Use-Based Migration in Neutral Atom Quantum Architectures

Quantum Simulation of Molecules in Solution

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