流体力学量子计算研究进展

孟昭远; 卢臻; 熊诗颖; 赵耀民; 杨越

doi:10.6052/1000-0992-24-041

流体力学量子计算研究进展

doi: 10.6052/1000-0992-24-041 cstr: 32046.14.1000-0992-24-041

孟昭远¹,
卢臻¹,
熊诗颖²,
赵耀民^{1, 3},
杨越^{1, 3, ,}

1.
北京大学工学院湍流与复杂系统国家重点实验室, 北京 100871
2.
浙江大学航空航天学院工程力学系, 杭州 310027
3.
北京大学应用物理与技术研究中心, 北京 100871

基金项目: 感谢国家自然科学基金(12432010、11925201)和国家重点研发计划青年科学家项目(2023YF-B4502600)资助

详细信息

作者简介:
杨越, 北京大学工学院教授、副院长. 先后获浙江大学学士、中国科学院力学研究所硕士、加州理工学院博士学位, 于普林斯顿大学与康奈尔大学作博士后. 现任湍流与复杂系统国家重点实验室副主任, 中国力学学会理事, 中国空气动力学会理事, 周培源基金会理事, 空气动力学学报副主编, 力学进展、SCPMA、AMS、ARC期刊编委. 曾获国家杰出青年科学基金、科学探索奖、教育部青年科学奖、求是杰出青年学者奖. 主要研究方向为湍流、涡动力学、燃烧、量子计算

通讯作者:
yyg@pku.edu.cn

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出版历程
- 网络出版日期: 2025-03-11

Advances in quantum computing for fluid dynamics

MENG Zhaoyuan¹,
LU Zhen¹,
XIONG Shiying²,
ZHAO Yaomin^{1, 3},
YANG Yue^{1, 3
, ,}

1.
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China
2.
Department of Engineering Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
3.
HEDPS-CAPT, Peking University, Beijing 100871, China

More Information

Corresponding author: yyg@pku.edu.cn

摘要

摘要: 本文综述流体力学量子计算这一前沿交叉研究领域的进展与挑战. 作为潜在的颠覆性技术, 量子计算预期在未来能够解决部分现实世界中的难题. 流体力学是经典物理与工程应用中极具挑战的问题, 可作为展示量子计算实用性与优越性的范例, 同时量子计算也可为流体力学带来新的研究范式. 本文首先阐述量子计算在量子态叠加与纠缠等方面的特点, 指出流体力学量子计算在初态制备、量子态演化和测量方面的挑战. 随后重点介绍量子−经典混合算法、哈密顿模拟等流体力学量子算法, 以及综述它们在真实量子计算机上的硬件实现进展. 总之, 目前流体力学量子计算仍处于萌芽阶段, 未来在量子计算硬件与算法方面均面临诸多挑战. 与传统方法相比, 尽管量子计算尚未在模拟强非线性的流体力学问题上展示出优越性, 但近期进展显示其有潜力来高效模拟湍流等复杂流动.
- 流体力学 /
- 量子计算 /
- 湍流 /
- 涡动力学 /
- 计算流体力学
Abstract: We review progress and challenges in the emerging field of quantum computing for fluid dynamics (QCFD). Quantum computing, a potentially disruptive technology, is expected to tackle pressing problems in the real world. Fluid dynamics, a complex problem in classical physics and engineering, can serve as an example to demonstrate quantum utility and advantage. Conversely, quantum computing can introduce new paradigms in fluid dynamics research. In this review, we first introduce quantum computing features, such as superposition and entanglement, and highlight the challenges of QCFD in initial state preparation, quantum state evolution, and measurement. We then focus on hybrid quantum-classical algorithms and Hamiltonian simulation for fluid dynamics, reviewing their hardware implementation on current quantum computers. In conclusion, QCFD is in its infancy, facing both challenges in quantum devices and algorithms. Although quantum computing has not yet shown an advantage in simulating strongly nonlinear fluid dynamics over classical methods, recent progress suggests its potential in enhancing simulations of complex flows, including turbulence.
- fluid mechanics /
- quantum computing /
- turbulence /
- vortex dynamics /
- computational fluid dynamics

HTML全文

图 1 量子态和量子计算过程示意图. (a)单量子比特状态的Bloch球表示, 可用三维笛卡尔坐标$ x=\sin\theta\cos\varphi $, $ y=\sin\theta\sin\varphi $, $ z=\cos\theta $描述, 为量子态提供几何图像. 任意单量子比特状态$ | {\psi} \rangle=\cos\dfrac{\theta}{2}| {0} \rangle+ {\rm{e}}^{ {\rm{i}}\varphi}\sin\dfrac{\theta}{2}| {1} \rangle $可用球上的一个点来表示, $ | {0} \rangle $态位于北极, $ | {1} \rangle $态位于南极. (b)量子计算机的计算过程分为初态制备、量子态演化和统计测量三步, 图中量子门$ U_1,U_2,\cdots,U_k $均为酉变换

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图 2 量子计算基本特性总结. (a)在经典计算机中, n位寄存器中保存 0 到 $ 2^n - 1 $中的一个数; 而在量子计算机中, 处于叠加态的n位量子寄存器中同时保存 0 到 $ 2^n - 1 $ 所有的数, 它们各以一定的概率同时存在. (b)制备两比特纠缠态的最简量子线路: 输出态$ (| {00} \rangle+| {11} \rangle)/\sqrt{2} $不能写成两个量子态的直积, 即两个量子比特处于纠缠态; 测量纠缠中一个量子比特的态, 将使另一个量子比特的态与之相同. (c) 量子计算具有类似于并行的特性, 对叠加的量子态进行一次酉变换, 可产生从0到$ 2^n-1 $所有取值各自所对应的函数值. 其中不同或相同颜色的量子比特分别代表它们之间存在或不存在纠缠

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图 3 流场的量子初态制备示意图. 将流场信息编码为量子态, 并将该量子初态制备过程分解为多项式复杂度的基本门操作, 是QCFD面临的难题之一

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图 4 量子−经典混合算法与哈密顿模拟算法计算流程图对比. (a) 利用量子线性求解器的量子−经典混合计算流程图. 在经典计算机上, 首先计算离散微分方程在当前时刻的矩阵和非齐次列向量, 结果用于设定量子线性求解器的参数. 随后进行初态制备, 并利用量子线性求解器求解线性方程组, 获得归一化的解向量. 通过量子态层析将提取出的解向量输入到经典计算机中, 计算下一个时刻的矩阵元素. 推进时间步需重复这一循环过程, 任一时刻的数据处理均在经典计算机上完成. (b) 基于NS方程的自旋量子表示, 将流体动力学模拟转化为求解双分量薛定谔方程 (Meng & Yang 2024b). 在量子计算机上, 对该薛定谔方程进行时间积分, 从而基于该哈密顿模拟流程实现端到端的量子计算

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图 5 求解线性方程组Ax = b的HHL算法的量子线路. 首先在量子寄存器$ | {0} \rangle^{\otimes n_b} $中制备列向量b初态, 然后利用量子相位估计得到矩阵A特征值的二进制表示, 对初态为$ | {0} \rangle $的辅助量子比特施加受控旋转, 随后进行量子相位估计的状态反演, 最后对辅助量子比特进行量子测量. 若测量结果为$ | {1} \rangle $, 则量子寄存器中编码的是归一化的解向量$ | {x} \rangle $. 此外, 一些HHL的改进算法可以有效利用辅助量子比特测量结果为$ | {0} \rangle $时量子寄存器中的信息 (Babukhin 2023, Tsemo et al. 2024), 从而避免该预选择过程

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图 6 从复函数场$ \psi $的零等值面中提取涡丝. 半透明红色曲面为等值面$ \text{Re}(\psi)=0 $, 半透明蓝色曲面为等值面$ \text{Im}(\psi)=0 $, 黄色曲线为$ \text{Re}(\psi)=0 $和$ \text{Im}(\psi)=0 $的交线

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图 7 “薛定谔化”变换或酉算符线性组合方法的示意图. 对一般线性偏微分方程, 直接对空间方向编码通常无法使状态向量的二范数对应系统的守恒量, 即状态向量不能保持归一化, 对应于系统演化中产生能量集中或耗散现象, 如左图所示对应于非酉的演化算符. 通过引入辅助变量(如右图中的转角), 左图中随时间耗散的量转化为右图中随时间不变的半径, 从而低维空间中的非酉算符映射为高维空间中的酉算符

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图 8 从简单到复杂流动的流体动力学方程的量子表示层级示意图 (Meng & Yang 2024b)

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图 9 基于量子线性求解器的量子−经典混合算法的量子计算真机实验结果. (a) 使用2和3个量子比特分别模拟一维对流−扩散问题, 并与经典计算结果进行对比 (Bharadwaj & Sreenivasan 2024a); (b) 使用 2 ~ 4 个量子比特模拟不可压缩 Poiseuille 流 (Chen et al. 2024).

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图 10 构建二维旋涡自旋向上波函数分量的10量子比特线路(Meng et al. 2024), 包括任意单量子门U和双量子门CZ. 从基态$ | {0} \rangle^{\otimes 10} $开始, 通过23层同步的单(双)量子门操作, 可以制备目标量子态. 每层操控时间为30(40)纳秒, 总运行时间为810纳秒, 远低于量子比特的寿命. 线路完成后, 需测量所有量子比特以重构流体的密度场和动量场

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图 11 二维可压缩渐扩势流的初始态下Pauli字符串的期望值(Meng et al. 2024). 按绝对值降序排列, 绘制了精确解(绿色)、CPFlow模拟(橘色)和实验(蓝色)得出的前20个Pauli字符串的期望值. 实验数据的误差棒代表10个标准差, 横轴下的每个点阵序列代表一个Pauli字符串, 每个量子比特根据对应的Pauli算符进行着色

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图 12 在超导量子计算机上基于哈密顿模拟的实验结果 (Meng et al. 2024). 其中(a)可压缩渐扩势流, (b)薛定谔涡流. 图中从上至下分别为t = 0和$ \pi/2 $两个时刻的测量结果, 包括密度场(蓝)、动量场(绿)和涡量场(紫), 其中动量场中绘制了若干流线

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