Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory

• URL: http://arxiv.org/abs/2305.07009v2
• Date: Mon, 15 May 2023 17:13:57 GMT
• Title: Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory
• Authors: Cristian L. Cortes, Matthias Loipersberger, Robert M. Parrish, Sam Morley-Short, William Pol, Sukin Sim, Mark Steudtner, Christofer S. Tautermann, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif
• Abstract summary: We consider the symmetry-adapted perturbation theory components of the interaction energy as a prototypical example of such an observable. We present a quantum algorithm that estimates interaction energies at the first-order SAPT level with a Heisenberg-limited scaling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficient computation of observables beyond the total energy is a key challenge and opportunity for fault-tolerant quantum computing approaches in quantum chemistry. Here we consider the symmetry-adapted perturbation theory (SAPT) components of the interaction energy as a prototypical example of such an observable. We provide a guide for calculating this observable on a fault-tolerant quantum computer while optimizing the required computational resources. Specifically, we present a quantum algorithm that estimates interaction energies at the first-order SAPT level with a Heisenberg-limited scaling. To this end, we exploit a high-order tensor factorization and block encoding technique that efficiently represents each SAPT observable. To quantify the computational cost of our methodology, we provide resource estimates in terms of the required number of logical qubits and Toffoli gates to execute our algorithm for a range of benchmark molecules, also taking into account the cost of the eigenstate preparation and the cost of block encoding the SAPT observables. Finally, we perform the resource estimation for a heme and artemisinin complex as a representative large-scale system encountered in drug design, highlighting our algorithm's performance in this new benchmark study and discussing possible bottlenecks that may be improved in future work.

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