Quantum approximate bayesian computation for nmr model inference

ABSTRACT Recent technological advances may lead to the development of small-scale quantum computers that are capable of solving problems that cannot be tackled with classical computers. A

limited number of algorithms have been proposed and their relevance to real-world problems is a subject of active investigation. Analysis of many-body quantum systems is particularly

challenging for classical computers due to the exponential scaling of the Hilbert space dimension with the number of particles. Hence, solving the problems relevant to chemistry and

condensed-matter physics is expected to be the first successful application of quantum computers. In this Article, we propose another class of problems from the quantum realm that can be

solved efficiently on quantum computers: model inference for nuclear magnetic resonance (NMR) spectroscopy, which is important for biological and medical research. Our results are based on

three interconnected studies. First, we use methods from classical machine learning to analyse a dataset of NMR spectra of small molecules. We perform stochastic neighbourhood embedding and

identify clusters of spectra, and demonstrate that these clusters are correlated with the covalent structure of the molecules. Second, we propose a simple and efficient method, aided by a

quantum simulator, to extract the NMR spectrum of any hypothetical molecule described by a parametric Heisenberg model. Third, we propose a simple variational Bayesian inference procedure

for estimating the Hamiltonian parameters of experimentally relevant NMR spectra. Access through your institution Buy or subscribe This is a preview of subscription content, access via your

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institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS ROBUST AUTOMATED BACKBONE TRIPLE RESONANCE NMR ASSIGNMENTS OF PROTEINS USING

BAYESIAN-BASED SIMULATED ANNEALING Article Open access 21 March 2023 OPTIMAL 13C NMR INVESTIGATION OF INTRINSICALLY DISORDERED PROTEINS AT 1.2 GHZ Article 12 December 2023 COMPUTATIONAL

MOLECULAR SPECTROSCOPY Article 27 May 2021 DATA AVAILABILITY The data and code to numerically generate the NMR data sets used in this manuscript can be found at

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strongly correlated lattice fermions. _EPJ Quantum Technol._ 3, 11 (2016). Google Scholar  Download references ACKNOWLEDGEMENTS D.S. acknowledges support from the FWO as post-doctoral fellow

of the Research Foundation—Flanders and from a 2019 grant from the Harvard Quantum Initiative Seed Funding programme. S.M. is supported by a research grant from the National Heart, Lung,

and Blood Institute (K24 HL136852). O.D. and H.D. are supported by a research award from the National Heart, Lung, and Blood Institute, (5K01HL135342) and (T32 HL007575) respectively. E.D.

acknowledges support from the Harvard–MIT CUA, ARO grant number W911NF-20-1-0163, the National Science Foundation through grant number OAC-1934714, AFOSR Quantum Simulation MURI.The authors

acknowledge useful discussions with P. Mehta and M. Lukin. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Physics, Harvard University, Cambridge, MA, USA Dries Sels & Eugene

Demler * Theory of Quantum and Complex Systems, Universiteit Antwerpen, Antwerpen, Belgium Dries Sels * Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical

School, Boston, MA, USA Hesam Dashti, Samia Mora & Olga Demler * Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Samia Mora

Authors * Dries Sels View author publications You can also search for this author inPubMed Google Scholar * Hesam Dashti View author publications You can also search for this author inPubMed

 Google Scholar * Samia Mora View author publications You can also search for this author inPubMed Google Scholar * Olga Demler View author publications You can also search for this author

inPubMed Google Scholar * Eugene Demler View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS D.S. and E.D. conceived the presented idea in

consultation with H.D., S.M. and O.D. D.S. developed the theoretical formalism, performed the analytic calculations and performed the numerical simulations. H.D. compiled the NMR data used

in the manuscript. All authors provided critical feedback and helped shape the manuscript. CORRESPONDING AUTHOR Correspondence to Dries Sels. ETHICS DECLARATIONS COMPETING INTERESTS The

authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional

affiliations. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Discussion and Figs. 1–5. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE

Sels, D., Dashti, H., Mora, S. _et al._ Quantum approximate Bayesian computation for NMR model inference. _Nat Mach Intell_ 2, 396–402 (2020). https://doi.org/10.1038/s42256-020-0198-x

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