Geometric encoding of turbulence for end-to-end quantum simulation
Zhaoyuan Meng, Xiao-Ming Zhang, Xiao Yuan, Yue Yang

TL;DR
This paper introduces 'turbuloscope', a quantum encoding method that efficiently generates turbulent flow fields by leveraging multiscale structures, enabling high-Reynolds-number simulations with minimal qubits and exponential speedup.
Contribution
The authors develop a physics-informed, geometric quantum encoding technique that overcomes data loading bottlenecks, scales logarithmically with Reynolds number, and reproduces realistic turbulence features.
Findings
Generated turbulent fields at Re=35,000 using only 30 qubits.
Reproduced Kolmogorov's 5/3 energy spectrum and vortex structures.
Achieved exponential speedup over classical methods.
Abstract
Multiscale organization is a hallmark of fluid turbulence in aerospace, energy, and transport systems. While quantum computing promises exponential speedups for solving the evolution equations governing flow fields, this potential is fundamentally hindered by the quantum state preparation bottleneck, the prohibitive cost of loading classical complex data into quantum states. Here, we overcome this barrier by introducing a physics-informed, three-stage geometric encoding method "turbuloscope", which efficiently generates turbulent fields relevant to high-Reynolds-number engineering flows. Rather than brute-force data loading, our approach acts as a kaleidoscope, leveraging the multiscale structures of turbulence. We capture scale-invariant self-similarity via a hyperplane approximation in high-dimensional feature space, and utilize the Hopf fibration to map quantum observables directly…
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