Best-Practice Aspects of Quantum-Computer Calculations: A Case Study of the Hydrogen Molecule

. 2022 Jan 18 ; 27 (3) : . [epub] 20220118

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid35163858

Grantová podpora
2/0136/19 VEGA, Slovakia
MUNI/G/1596/2019 Masaryk University

Quantum computers are reaching one crucial milestone after another. Motivated by their progress in quantum chemistry, we performed an extensive series of simulations of quantum-computer runs that were aimed at inspecting the best-practice aspects of these calculations. In order to compare the performance of different setups, the ground-state energy of the hydrogen molecule was chosen as a benchmark for which the exact solution exists in the literature. Applying the variational quantum eigensolver (VQE) to a qubit Hamiltonian obtained by the Bravyi-Kitaev transformation, we analyzed the impact of various computational technicalities. These included (i) the choice of the optimization methods, (ii) the architecture of the quantum circuits, as well as (iii) the different types of noise when simulating real quantum processors. On these, we eventually performed a series of experimental runs as a complement to our simulations. The simultaneous perturbation stochastic approximation (SPSA) and constrained optimization by linear approximation (COBYLA) optimization methods clearly outperformed the Nelder-Mead and Powell methods. The results obtained when using the Ry variational form were better than those obtained when the RyRz form was used. The choice of an optimum entangling layer was sensitively interlinked with the choice of the optimization method. The circular entangling layer was found to worsen the performance of the COBYLA method, while the full-entangling layer improved it. All four optimization methods sometimes led to an energy that corresponded to an excited state rather than the ground state. We also show that a similarity analysis of measured probabilities can provide a useful insight.

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Cao Y., Romero J., Aspuru-Guzik A. Potential of quantum computing for drug discovery. IBM J. Res. Dev. 2018;62:6:1–6:20. doi: 10.1147/JRD.2018.2888987. DOI

Polsterová S., Friák M., Všianská M., Šob M. Quantum-Mechanical Assessment of the Energetics of Silver Decahedron Nanoparticles. Nanomaterials. 2020;10:767. doi: 10.3390/nano10040767. PubMed DOI PMC

Lordi V., Nichol J. dvances and opportunities in materials science for scalable quantum computing. MRS Bull. 2021;46:589–595. doi: 10.1557/s43577-021-00133-0. DOI

Miceli R., McGuigan M. Effective matrix model for nuclear physics on a quantum computer; Proceedings of the New York Scientific Data Summit (NYSDS); New York, NY, USA. 12–14 June 2019; pp. 1–4. DOI

Di Matteo O., McCoy A., Gysbers P., Miyagi T., Woloshyn R.M., Navrátil P. Improving Hamiltonian encodings with the Gray code. Phys. Rev. A. 2021;103:042405. doi: 10.1103/PhysRevA.103.042405. DOI

Feynman R. Simulating physics with computers. Int. J. Theor. Phys. 1982;21:467–488. doi: 10.1007/BF02650179. DOI

Nielsen M.A., Chuang I.L. Quantum Computation and Quantum Information: 10th Anniversary Edition. 10th ed. Cambridge University Press; Cambridge, MA, USA: 2011.

Wu Y., Bao W.S., Cao S., Chen F., Chen M.C., Chen X., Chung T.H., Deng H., Du Y., Fan D., et al. Strong Quantum Computational Advantage Using a Superconducting Quantum Processor. Phys. Rev. Lett. 2021;127:180501. doi: 10.1103/PhysRevLett.127.180501. PubMed DOI

Arute F., Arya K., Babbush R., Bacon D., Bardin J.C., Barends R., Biswas R., Boixo S., Brandao F.G.S.L., Buell D.A., et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574:505–510. doi: 10.1038/s41586-019-1666-5. PubMed DOI

Zhong H.S., Deng Y.H., Qin J., Wang H., Chen M.C., Peng L.C., Luo Y.H., Wu D., Gong S.Q., Su H., et al. Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light. Phys. Rev. Lett. 2021;127:180502. doi: 10.1103/PhysRevLett.127.180502. PubMed DOI

Friesner R.A. Ab initio quantum chemistry: Methodology and applications. Proc. Natl. Acad. Sci. USA. 2005;102:6648–6653. doi: 10.1073/pnas.0408036102. PubMed DOI PMC

Helgaker T., Klopper W., Tew D.P. Quantitative quantum chemistry. Mol. Phys. 2008;106:2107–2143. doi: 10.1080/00268970802258591. DOI

Cremer D. Møller–Plesset perturbation theory: From small molecule methods to methods for thousands of atoms. WIREs Comput. Mol. Sci. 2011;1:509–530. doi: 10.1002/wcms.58. DOI

Lyakh D.I., Musiał M., Lotrich V.F., Bartlett R.J. Multireference Nature of Chemistry: The Coupled-Cluster View. Chem. Rev. 2012;112:182–243. doi: 10.1021/cr2001417. PubMed DOI

Mardirossian N., Head-Gordon M. Thirty years of density functional theory in computational chemistry: An overview and extensive assessment of 200 density functionals. Mol. Phys. 2017;115:2315–2372. doi: 10.1080/00268976.2017.1333644. DOI

Preskill J. Quantum Computing in the NISQ era and beyond. Quantum. 2018;2:79. doi: 10.22331/q-2018-08-06-79. DOI

O’Malley P.J.J., Babbush R., Kivlichan I.D., Romero J., McClean J.R., Barends R., Kelly J., Roushan P., Tranter A., Ding N., et al. Scalable Quantum Simulation of Molecular Energies. Phys. Rev. X. 2016;6:031007. doi: 10.1103/PhysRevX.6.031007. DOI

McArdle S., Endo S., Aspuru-Guzik A., Benjamin S., Yuan X. Quantum computational chemistry. Rev. Mod. Phys. 2020;92:15003. doi: 10.1103/RevModPhys.92.015003. DOI

Cao Y., Romero J., Olson J.P., Degroote M., Johnson P.D., Kieferová M., Kivlichan I.D., Menke T., Peropadre B., Sawaya N.P.D., et al. Quantum Chemistry in the Age of Quantum Computing. Chem. Rev. 2019;119:10856–10915. doi: 10.1021/acs.chemrev.8b00803. PubMed DOI

Kandala A., Mezzacapo A., Temme K., Takita M., Brink M., Chow J.M., Gambetta J.M. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature. 2017;549:242–246. doi: 10.1038/nature23879. PubMed DOI

Powell M.J.D. A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation. In: Gomez S., Hennart J.P., editors. Advances in Optimization and Numerical Analysis. Springer; Dordrecht, The Netherlands: 1994. pp. 51–67. DOI

Spall J.C. Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation. IEEE Trans. Autom. Control. 1992;37:332–341. doi: 10.1109/9.119632. DOI

Spall J. Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerosp. Electron. Syst. 1998;34:817–823. doi: 10.1109/7.705889. DOI

Spall J.C. An Overview of the Simultaneous Perturbation Method for Efficient Optimization. Johns Hopkins Apl Tech. Dig. 1998;19:482–492.

Nelder J.A., Mead R. A simplex method for function minimization. Comput. J. 1965;7:308–313. doi: 10.1093/comjnl/7.4.308. DOI

Powell M.J.D. The NEWUOA software for unconstrained optimization without derivatives. In: Di Pillo G., Roma M., editors. Large-Scale Nonlinear Optimization. Springer; Boston, MA, USA: 2006. pp. 255–297. DOI

Sachdeva V., Freimuth D.M., Mueller C. International Conference on Computational Science, Proceedings of the 9th International Conference Baton Rouge, LA, USA, 25–27 May 2009. Springer; Berlin/Heidelberg, Germany: 2009. Evaluating the Jaccard–Tanimoto Index on Multi-core Architectures; pp. 944–953.

Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today. 2006;11:1046–1053. doi: 10.1016/j.drudis.2006.10.005. PubMed DOI

Murata T. Machine discovery based on the co-occurence of references in search engine. In: Arikawa S., Furukawa K., editors. International Conference on Discovery Science, Proceedings of the Second International Conference, DS’99, Tokyo, Japan, 6–8 December 1999. Volume 1721. Springer; Berlin/Heidelberg, Germany: 1999. pp. 220–229.

Mild A., Reutterer T. Collaborative Filtering Methods for Binary Market Basket Data Analysis; Proceedings of the 6th International Computer Science Conference, AMT 2001; Hong Kong, China. 18–20 December 2001; pp. 302–313.

Liben-Nowell D., Kleinberg J. The link prediction problem for social networks; Proceedings of the Twelfth International Conference on Information and Knowledge Management; New Orleans, LA, USA. 3–8 November 2003; pp. 302–313.

Jaccard P. The Distribution of the Flora in the Alpine Zone.1. New Phytol. 1912;11:37–50. doi: 10.1111/j.1469-8137.1912.tb05611.x. DOI

Tanimoto T.T. An Elementary Mathematical Theory of Classification and Prediction. McGraw-Hill; New York, NY, USA: 1958. p. 8. Internal IBM Technical Report.

Bloch F. Generalized theory of relaxation. Phys. Rev. 1957;105:1206–1222. doi: 10.1103/PhysRev.105.1206. DOI

Krantz P., Kjaergaard M., Yan F., Orlando T.P., Gustavsson S., Oliver W.D. A quantum engineer’s guide to superconducting qubits. Appl. Phys. Rev. 2019;6:021318. doi: 10.1063/1.5089550. DOI

Kandala A., Temme K., Córcoles A.D., Mezzacapo A., Chow J.M., Gambetta J.M. Error mitigation extends the computational reach of a noisy quantum processor. Nature. 2019;567:491–495. doi: 10.1038/s41586-019-1040-7. PubMed DOI

Endo S., Benjamin S.C., Li Y. Practical Quantum Error Mitigation for Near-Future Applications. Phys. Rev. X. 2018;8:031027. doi: 10.1103/PhysRevX.8.031027. DOI

Cai Z. Quantum Error Mitigation using Symmetry Expansion. Quantum. 2021;5:548. doi: 10.22331/q-2021-09-21-548. DOI

Suchsland P., Tacchino F., Fischer M.H., Neupert T., Barkoutsos P.K., Tavernelli I. Algorithmic Error Mitigation Scheme for Current Quantum Processors. Quantum. 2021;5:492. doi: 10.22331/q-2021-07-01-492. DOI

Bravyi S., Sheldon S., Kandala A., Mckay D.C., Gambetta J.M. Mitigating measurement errors in multiqubit experiments. Phys. Rev. A. 2021;103 doi: 10.1103/PhysRevA.103.042605. DOI

Geller M.R., Sun M. Toward efficient correction of multiqubit measurement errors: Pair correlation method. Quantum Sci. Technol. 2021;6:025009. doi: 10.1088/2058-9565/abd5c9. DOI

Bravyi S., Gambetta J.M., Mezzacapo A., Temme K. Tapering off qubits to simulate fermionic Hamiltonians. arXiv. 20171701.08213

Bravyi S., Kitaev A. Fermionic quantum computation. Ann. Phys. 2002;298:210–226. doi: 10.1006/aphy.2002.6254. DOI

Jordan P., Wigner E. Über das Paulische Äquivalenzverbot. Z. Phys. 1928;47:631–651. doi: 10.1007/BF01331938. DOI

VQE Tutorial. [(accessed on 1 November 2021)]. Available online: https://pennylane.ai/qml/demos/tutorial_vqe.html.

Miháliková I. Implementation of the Variational Quantum Eigensolver. 2021. [(accessed on 4 January 2021)]. Available online: https://github.com/imihalik/VQE_H2.

Qiskit’s Chemistry Module. [(accessed on 4 January 2021)]. Available online: https://qiskit.org/documentation/apidoc/qiskit_chemistry.html.

Peruzzo A., McClean J., Shadbolt P., Yung M.H., Zhou X.Q., Love P., Aspuru-Guzik A., O’Brien J. A variational eigenvalue solver on a photonic quantum processor. Nat. Commun. 2014;5:4213. doi: 10.1038/ncomms5213. PubMed DOI PMC

Tilly J., Chen H., Cao S., Picozzi D., Setia K., Li Y., Grant E., Wossnig L., Rungger I., Booth G.H., et al. The Variational Quantum Eigensolver: A review of methods and best practices. arXiv. 20212111.05176

Rayleigh J. In finding the correction for the open end of an organ-pipe. Phil. Trans. 1870;161:77.

Ritz W. Über eine neue Methode zur Lösung gewisser Variationsprobleme der mathematischen Physik. J. Reine Angew. Math. 1909;135:1–61. doi: 10.1515/crll.1909.135.1. DOI

Arfken G., Weber H. Mathematical Methods for Physicists. 3rd ed. Academic Press; Orlando, FL, USA: 1985. Rayleigh-Ritz variational technique; pp. 957–961.

Miháliková I., Pivoluska M., Plesch M., Friák M., Nagaj D., Šob M. The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry. arXiv. 2021 doi: 10.3390/nano12020243.2111.04965 PubMed DOI PMC

IBM Quantum. [(accessed on 4 January 2021)]. Available online: https://quantum-computing.ibm.com/

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