Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing
Abstract
Sidechain packing is a critical stage in protein folding, with direct implications for structure-based drug discovery. Google DeepMind's tool, AlphaFold2, predicts protein backbone reliably but sidechain positioning less accurately. Recovering the lowest-energy rotamer assignment over a fixed backbone is NP-hard. We present a hybrid quantum-classical pipeline that repacks sidechains on the AlphaFold backbone using the Quantum Approximate Optimisation Algorithm (QAOA), encoding the one- and two-body energies as a quadratic unconstrained binary optimisation (QUBO) problem. We introduce a constraint-preserving ansatz, pairing a W-state initialisation with a cyclic XY ring mixer, that enforces one-hot rotamer validity without penalty terms while keeping two-qubit gate scaling linear in the rotamer count. We also define an asymptotic shot-scaling metric, measured against the experimentally resolved conformation, that fixes optimiser quality independently of the baseline; its fitted growth stays below the classical exhaustive-search rate at moderate rotamer flexibility. Evaluated on bovine pancreatic trypsin inhibitor (5PTI) across high- and moderate AlphaFold-confidence regions, the pipeline lowers conformational energy against the AlphaFold baseline.
Related
- Quantum approximate stochastic combinatorial optimisation
- Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer
- Effective Noise Thresholds for Reliable Quantum Approximation Optimisation Algorithm under Noisy Environment
- κ-Parametrized Boltzmann Distribution Sampling on Quantum Approximate Optimization Algorithm
- Parameter-Dependent Noise Resilience in the Quantum Approximate Optimization Algorithm
BibTeX
@misc{stewart2026quantum,
title = {{Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing}},
author = {Sebastian O. M. Stewart and Nick Chancellor and Jonte R Hance and Ittoop Vergheese Puthoor},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31077},
eprint = {2609.31077},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31077},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing
AU - Sebastian O. M. Stewart
AU - Nick Chancellor
AU - Jonte R Hance
AU - Ittoop Vergheese Puthoor
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.31077
UR - https://arxiv.org/abs/2609.31077
AB - Sidechain packing is a critical stage in protein folding, with direct implications for structure-based drug discovery. Google DeepMind's tool, AlphaFold2, predicts protein backbone reliably but sidechain positioning less accurately. Recovering the lowest-energy rotamer assignment over a fixed backbone is NP-hard. We present a hybrid quantum-classical pipeline that repacks sidechains on the AlphaFold backbone using the Quantum Approximate Optimisation Algorithm (QAOA), encoding the one- and two-body energies as a quadratic unconstrained binary optimisation (QUBO) problem. We introduce a constraint-preserving ansatz, pairing a W-state initialisation with a cyclic XY ring mixer, that enforces one-hot rotamer validity without penalty terms while keeping two-qubit gate scaling linear in the rotamer count. We also define an asymptotic shot-scaling metric, measured against the experimentally resolved conformation, that fixes optimiser quality independently of the baseline; its fitted growth stays below the classical exhaustive-search rate at moderate rotamer flexibility. Evaluated on bovine pancreatic trypsin inhibitor (5PTI) across high- and moderate AlphaFold-confidence regions, the pipeline lowers conformational energy against the AlphaFold baseline.
ER -