Calculating the energy profile of an enzymatic reaction on a quantum computer

TL;DR

This paper demonstrates the first quantum computing simulation of an enzymatic reaction, combining classical and quantum methods to improve molecular modeling accuracy on NISQ devices.

Contribution

It introduces a hybrid classical-quantum approach using FAST-VQE and a novel gate reduction strategy for enzymatic reactions on NISQ hardware, advancing practical quantum chemistry applications.

Findings

Successful simulation of an enzymatic proton transfer reaction

Enhanced accuracy of quantum chemistry calculations on NISQ devices

Framework applicable to broader computational enzymology

Abstract

Quantum computing (QC) provides a promising avenue toward enabling quantum chemistry calculations, which are classically impossible due to a computational complexity that increases exponentially with system size. As fully fault-tolerant algorithms and hardware, for which an exponential speedup is predicted, are currently out of reach, recent research efforts are dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices to showcase the practical utility of such machines. To demonstrate the utility of NISQ devices in the field of chemistry, we apply our recently developed FAST-VQE algorithm and a novel quantum gate reduction strategy based on propositional satisfiability together with standard optimization tools for the simulation of the rate-determining proton transfer step for CO2 hydration catalysed by carbonic anhydrase resulting in the first application of a quantum computing device for the simulation of an enzymatic reaction. To this end, we have combined classical force field simulations with quantum mechanical methods on classical and quantum computers in a hybrid calculation approach. The presented technique significantly enhances the accuracy and capabilities of QC-based molecular modeling and finally pushes it into compelling and realistic applications. The framework is general and can be applied beyond the case of computational enzymology.