• Open Access

Efficient quantum computation of molecular forces and other energy gradients

Thomas E. O'Brien, Michael Streif, Nicholas C. Rubin, Raffaele Santagati, Yuan Su, William J. Huggins, Joshua J. Goings, Nikolaj Moll, Elica Kyoseva, Matthias Degroote, Christofer S. Tautermann, Joonho Lee, Dominic W. Berry, Nathan Wiebe, and Ryan Babbush
Phys. Rev. Research 4, 043210 – Published 26 December 2022

Abstract

While most work on the quantum simulation of chemistry has focused on computing energy surfaces, a similarly important application requiring subtly different algorithms is the computation of energy derivatives. Almost all molecular properties can be expressed an energy derivative, including molecular forces, which are essential for applications such as molecular dynamics simulations. Here, we introduce new quantum algorithms for computing molecular energy derivatives with significantly lower complexity than prior methods. Under cost models appropriate for noisy-intermediate scale quantum devices, we demonstrate how low-rank factorization and other tomography schemes can be optimized for energy derivative calculations. We numerically demonstrate that our techniques reduce the number of circuit repetitions required by many orders of magnitude for even modest systems, and that the cost of estimating an entire force vector may in some systems be lower than the cost of estimating the energy. In the context of fault-tolerant algorithms, we develop new methods of estimating energy derivatives with Heisenberg limited scaling, incorporating state-of-the-art techniques for block encoding fermionic operators. In contrast to our near-term results, we find that the cost of estimating forces with any of our Heisenberg-limited methods is bounded by the cost of estimating energies, due to inner loops requiring either energy estimation or reflections around the ground state. This implies that applications such as geometry optimization, coupling parameter estimation, and spectral prediction may be practical on fault-tolerant quantum devices, but tractable molecular dynamics simulations of large-scale systems requires further algorithmic advances.

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  • Received 21 December 2021
  • Accepted 13 October 2022

DOI:https://doi.org/10.1103/PhysRevResearch.4.043210

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyPhysics of Living Systems

Authors & Affiliations

Thomas E. O'Brien1,*, Michael Streif2,†, Nicholas C. Rubin1,‡, Raffaele Santagati2,§, Yuan Su1, William J. Huggins1, Joshua J. Goings1, Nikolaj Moll2, Elica Kyoseva2, Matthias Degroote2, Christofer S. Tautermann3, Joonho Lee1,4, Dominic W. Berry5, Nathan Wiebe6,7,∥, and Ryan Babbush1,¶

  • 1Google Research, Venice, California 90291, USA
  • 2Quantum Lab, Boehringer Ingelheim, 55218 Ingelheim am Rhein, Germany
  • 3Boehringer Ingelheim Pharma GmbH & Co KG, Birkendorfer Strasse 65, 88397 Biberach, Germany
  • 4Department of Chemistry, Columbia University, New York, USA
  • 5School of Mathematical and Physical Sciences, Macquarie University, New South Wales 2109, Australia
  • 6Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 1A4
  • 7Pacific Northwest National Laboratory, Richland, Washington 99354, USA

  • *teobrien@google.com
  • michael.streif@boehringer-ingelheim.com
  • nickrubin@google.com
  • §raffaele.santagati@boehringer-ingelheim.com
  • nawiebe@cs.toronto.edu
  • babbush@google.com

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Vol. 4, Iss. 4 — December - December 2022

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