The cost of quantum algorithms for biochemistry: A case study in metaphosphate hydrolysis

TL;DR

This paper assesses the quantum computational resources needed for simulating a key biochemical reaction, demonstrating that variational quantum algorithms are promising for near-term quantum hardware applications.

Contribution

It compares three quantum algorithms for biochemical energy estimation and provides benchmark datasets to guide future research in quantum biochemistry.

Findings

Variational methods require fewer quantum resources and are more feasible on near-term devices.

Quantum algorithms can potentially solve important biochemical problems with current or near-future quantum hardware.

The paper offers comprehensive datasets and code for benchmarking quantum biochemical simulations.

Abstract

We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.