QAOA 用于蛋白质侧链堆积：在 AlphaFold 骨架基础上改进构象

作者设计了约束保持的 ansatz，结合 W 态初始化与循环 XY 环混合器，在不引入罚项的情况下保证 one-hot rotamer 有效性，且双比特门数量随 rotamer 数线性增长。在牛胰蛋白酶抑制剂 5PTI 的高置信和中等置信区域测试中，该流水线将构象能量降到 AlphaFold2 基线之下。

Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing

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.

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include QAOA, AlphaFold2, 蛋白质折叠, 量子计算.

Primary source verification: published directly by arXiv: Google DeepMind with first-party authority.