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

2026-01-27

AI Summary

This study evaluates the resource costs of quantum algorithms for simulating ground-state energies of key biochemical reactions, such as ATP and metaphosphate hydrolysis. By systematically comparing the variational quantum eigensolver (VQE), quantum Krylov subspace methods, and quantum phase estimation (QPE)—and integrating classical exact simulations, numerical estimates, and theoretical bound analyses—it provides the first comprehensive quantification of resource requirements for these three mainstream quantum algorithms in realistic biochemical systems. The findings reveal that VQE demands significantly fewer quantum resources than the alternatives, making it a promising candidate for simulating critical reactions on near-term quantum hardware. To support future algorithmic development and applications, the project also releases a reproducible dataset of molecular Hamiltonians and benchmarking code.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• quantum algorithms
• biochemistry
• ground state energy
• metaphosphate hydrolysis
• quantum resource estimation

Innovation

• quantum resource estimation
• variational quantum eigensolver
• quantum phase estimation
• biochemical simulation
• metaphosphate hydrolysis