Quantum computing in pharma: A multilayer embedding approach for near future applications

Abstract

Quantum computers are special purpose machines that are expected to be particularly useful in simulating strongly correlated chemical systems. The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F2 in a (2,2) active space on Rigetti's Aspen‐11 QPU. While this is a promising start, it also underlines the need for carefully selecting the orbital spaces treated by the quantum computer. In this work, a scheme for selecting such an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment. The active occupied space is selected from orbitals localized on the chemically relevant fragment of the molecule, while the corresponding virtual space is chosen based on the magnitude of interactions with the occupied space calculated from perturbation theory. This protocol is then applied to two chemical systems of pharmaceutical relevance: the enzyme [Fe] hydrogenase and the photosenzitizer temoporfin. While the sizes of the active spaces currently amenable to a quantum computational treatment are not enough to demonstrate quantum advantage, the procedure outlined here is applicable to any active space size, including those that are outside the reach of classical computation.

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

“…Energy-weighted DMET and Gutzwiller variational embedding approaches have been tested on current quantum processors. Pharmaceutical model systems have also been studied, including a study of protein–ligand interactions using DMET and our own work on a multilayer embedding approach …”

“…To construct the molecular Hamiltonian within this region, an active space of orbitals must be selected in a manner reminiscent of frozen core , and complete active space approaches. In our previous work, we suggested a way for how the active orbitals may be selected using local fragment occupied orbitals and a corresponding set of natural orbitals obtained from perturbation theory . We also outlined a secondary subtractive embedding process to take care of correlation effects outside the active space.…”

“…The def2-TZVP basis set was used . The occupied orbitals were localized using the Pipek–Mezey approach and were mapped to fragments using intrinsic atomic orbitals and the criterion that the orbital charge on the fragment be larger than 0.95 . The same number of active virtuals were selected using perturbation theory as the number of active occupied orbitals obtained in the previous step …”

“…Pharmaceutical model systems have also been studied, including a study of protein–ligand interactions using DMET 134 and our own work on a multilayer embedding approach. 135 …”

“…In our previous work, we suggested a way for how the active orbitals may be selected using local fragment occupied orbitals and a corresponding set of natural orbitals obtained from perturbation theory. 135 We also outlined a secondary subtractive embedding process to take care of correlation effects outside the active space. For the purposes of resource estimation, this second step is not necessary.…”