Efficient calculation of energy derivatives on a Fault-Tolerant Quantum Computer
ORAL
Abstract
Here, we present new algorithms for efficiently calculating energy derivatives on fault-tolerant quantum computers, exploiting existing state-of-the-art techniques, including block encoding of fermionic operators, use of higher order finite difference formula, and Heisenberg-limited expectation value estimation methods. We optimize the algorithms' parameters to reduce their computational cost and discuss their asymptotic scalings. We show how these approaches can achieve Heisenberg's limited scaling of the errors and compare their different performance, supporting the results with numerical simulations. We will discuss the limits of such techniques and their direct dependence on the cost of state preparation and the calculation of the expectation value of the energy. We will finally explore their applicability to problems of practical relevance, such as the geometry optimization of molecules.
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Publication: 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, Ryan Babbush - arXiv preprint arXiv:2111.12437 https://arxiv.org/abs/2111.12437
Presenters
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Raffaele Santagati
Boehringer-Ingelheim Quantum Lab
Authors
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Raffaele Santagati
Boehringer-Ingelheim Quantum Lab
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Thomas E O'Brien
Google LLC
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Michael Streif
Boehringer Ingelheim
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Nicholas C Rubin
Google
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Yuan Su
Microsoft Quantum, Google Research, Google
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William J Huggins
Google, Google Quantum AI
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Joshua Goings
IonQ, Inc, IonQ, Google
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Nikolaj Moll
Boehringer Ingelheim
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Elica Kyoseva
Boehringer Ingelheim, Boehringer-Ingelheim
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Matthias Degroote
Boehringer Ingelheim, Boehringer-Ingelheim
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Christofer Tautermann
Boehringer Ingelheim, Boehringer Ingelheim Pharma Inc., Boehringer-Ingelheim
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Joonho Lee
Columbia University
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Dominic W Berry
Macquarie University
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Nathan Wiebe
University of Toronto, Pacific Northwest National Laboratory, University of Toronto, Pacific Northwest Natl Lab
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Ryan Babbush
Google