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

• URL: http://arxiv.org/abs/2408.11091v1
• Date: Tue, 20 Aug 2024 18:00:01 GMT
• Title: Calculating the energy profile of an enzymatic reaction on a quantum computer
• Authors: Patrick Ettenhuber, Mads Bøttger Hansen, Irfansha Shaik, Stig Elkjær Rasmussen, Pier Paolo Poier, Niels Kristian Madsen, Marco Majland, Frank Jensen, Lars Olsen, Nikolaj Thomas Zinner,
• Abstract summary: Quantum computing provides a promising avenue toward enabling quantum chemistry calculations. Recent research efforts are dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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