用于连续扭转角空间中非格点蛋白质结构预测的对数尺度变分量子本征求解器
Logarithmic-scale variational quantum eigensolver for off-lattice protein structure prediction in continuous torsional angle space
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中文总结 AI 辅助
该研究提出对数尺度变分量子本征求解器，将蛋白质结构预测的量子比特需求降至O(log₂N)，实现非格点全原子模拟，在chignolin等蛋白上获类天然构象，为混合量子生物物理奠定可扩展基础。
中文摘要 AI 辅助
经典方法和当前用于蛋白质结构预测的量子方法（QPSP）均存在局限性，尤其是量子比特需求巨大，限制了近期模型只能开展简单的格点模拟。我们提出一种对数尺度变分量子本征求解器（VQE），可将N个扭转自由度的量子比特需求降至O(log₂N)，从而实现非格点全原子模拟。该架构从状态矢量模拟的相对相位中提取分子扭转角；在量子硬件上，解码器将基态概率的经验累积分布函数（CDF）映射为有界扭转变量，这些变量输入经典算法以构建重原子坐标。我们采用EfficientSU2拟设和多阶段弛豫来缓解贫瘠高原问题。结构通过定制的量子-经典混合哈密顿量，结合Rosetta和OpenMM基准进行评估；对chignolin和Trp-cage的评估得到了类天然构象：chignolin在保留的快照中达到0.623 Å的Cα原子均方根偏差（RMSD），最终模型为1.199 Å；Trp-cage的快照RMSD为2.501 Å，最终模型为3.512 Å。在IBM处理器（ibm_cleveland、ibm_miami）上执行时，成功恢复了类天然结构，最佳RMSD为1.758 Å。定制能量函数整体表现最佳，但所有函数在采样空间中均存在能量排序失衡问题。这是首个用于QPSP的全原子连续空间量子算法，通过将物理量子比特约束转化为电路深度约束，证明了仅用指数级更少的量子比特即可实现高分辨率预测；尽管当前存在计算开销、能量函数敏感性等限制，但它为混合量子生物物理学建立了可扩展的基础。
英文摘要
Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-lattice simulations. We propose a logarithmic-scale variational quantum eigensolver (VQE) that reduces qubit requirements for N torsional degrees of freedom to O(log2N), enabling off-lattice, all-atom simulations. Our architecture extracts molecular torsions from relative phases in statevector simulations. On quantum hardware, a decoder maps the empirical cumulative distribution function (CDF) from basis-state probabilities to bounded torsional variables. These feed a classical algorithm to build heavy-atom coordinates. We use an EfficientSU2 ansatz and multi-stage relaxation to mitigate barren plateaus. Structures are evaluated via a custom hybrid quantum-classical Hamiltonian, alongside Rosetta and OpenMM benchmarks. Evaluation on chignolin and Trp-cage yielded native-like conformations. Chignolin reached a 0.623 Å Cα RMSD in retained snapshots and 1.199 Å in final models; Trp-cage achieved a 2.501 Å RMSD among snapshots (3.512 Å in final models). Execution on IBM processors (ibm_cleveland, ibm_miami) successfully recovered native-like structures with a best RMSD of 1.758 Å. The custom energy function performed best overall, though energy-ranking imbalances persisted across sampled landscapes for all functions. This introduces the first all-atom, continuous-space quantum algorithm for QPSP. By converting physical qubit constraints into circuit depth constraints, it proves high-resolution prediction is feasible with exponentially fewer qubits. Despite current limits like computational overhead and energy function sensitivity, it establishes a scalable foundation for hybrid quantum biophysics.
发表机构
- Cleveland Clinic（克利夫兰诊所）
- Case Western Reserve University（凯斯西储大学）
机构由 AI 辅助整理，请以论文原文为准。
