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arXiv:2506.04023 · Nature Communications

Simulating fluid vortex interactions on a superconducting quantum processor

Ziteng Wang, Jiarun Zhong, Ke Wang, Zitian Zhu, Zehang Bao, Chenjia Zhu, Wenwen Zhao, Yaomin Zhao, Yue Yang, Chao Song, Shiying Xiong

Published Feb 10, 2026• 7 citations
arXivHTMLPDF

Abstract

Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier–Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97% for single-qubit gates and 99.76% for two-qubit gates, we successfully reproduce natural vortex interactions. Overall, we establish a framework that reformulates vortex dynamics into a normalized wavefunction representation compatible with quantum system unitary evolution, combined with the designed spatiotemporal encoding scheme, providing a concrete pathway toward leveraging quantum resources in fluid systems. Despite recent progress, simulations of complex fluid dynamics phenomena on quantum computers remain challenging. Here, the authors present a quantum friendly reformulation of vortex dynamics with a spatiotemporal encoding scheme and apply it to simulations on a superconducting quantum processor.

DOI
10.1038/s41467-026-69168-8

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Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier–Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97% for single-qubit gates and 99.76% for two-qubit gates, we successfully reproduce natural vortex interactions. Overall, we establish a framework that reformulates vortex dynamics into a normalized wavefunction representation compatible with quantum system unitary evolution, combined with the designed spatiotemporal encoding scheme, providing a concrete pathway toward leveraging quantum resources in fluid systems. Despite recent progress, simulations of complex fluid dynamics phenomena on quantum computers remain challenging. Here, the authors present a quantum friendly reformulation of vortex dynamics with a spatiotemporal encoding scheme and apply it to simulations on a superconducting quantum processor.
Recent advancements of quantum technologies have triggered tremendous interest in exploring practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a potential direction. Here, we report an experiment on the digital simulation of unsteady flows with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. Note that the former case is an inviscid potential flow, and the latter one is an artificial vortical flow with an external body force. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications. Fluid dynamics simulation, a complex challenge in classical physics, is relevant for real-world applications and highlights the potential of quantum computing. The authors report an experiment for the digital simulation of unsteady flows on a superconducting quantum processor, and show that the results effectively capture the evolution of flow fields.
Simulating fluid dynamics on a quantum computer is intrinsically difficult due to the nonlinear and non-Hamiltonian nature of the Navier-Stokes equation (NSE). We propose a framework for quantum computing of fluid dynamics based on the hydrodynamic Schr\"odinger equation (HSE), which can be promising in simulating three-dimensional turbulent flows in various engineering applications. The HSE is derived by generalizing the Madelung transform to compressible or incompressible flows with finite vorticity and dissipation. Since the HSE is expressed as a unitary operator on a two-component wave function, it is more suitable than the NSE for quantum computing. The flow governed by the HSE can resemble a turbulent flow consisting of tangled vortex tubes with the five-thirds scaling of energy spectrum. We develop a prediction-correction quantum algorithm to solve the HSE. This algorithm is implemented for simple flows on the quantum simulator Qiskit with partial exponential speedup.
From fluid flow and transport to collective dynamics, numerical simulation of nonlinear partial differential equations underpins modern scientific computing. Extending this capability to quantum computers remains a longstanding challenge because nonlinear and non-Hermitian evolution is fundamentally incompatible with conventional Hamiltonian-based quantum simulation. Here we experimentally realize the time evolution of nonlinear fluid dynamics on a quantum processor using a hybrid variational framework for the viscous and inviscid Burgers equations. Our approach directly encodes the nonlinear dynamics into a variational optimization procedure, avoiding the enlarged linear embeddings and truncation overhead associated with Carleman linearization-based quantum algorithms. We further demonstrate convection-dominated dynamics corresponding to Reynolds numbers of order 10210^2102. We encode the governing evolution into parametrized quantum circuits and iteratively reconstruct the time-dependent field through quantum-classical optimization. By introducing a zero-noise extrapolation method without additional circuit-folding overhead, we accurately execute deep error-circuits with entangling-gate counts beyond those typical of Hadamard test circuits. We accurately reconstruct the time evolution across multiple timesteps despite hardware noise and finite device coherence. Our results constitute, to our knowledge, the first experimental realization of nonlinear time propagation on a quantum processor, extending quantum simulation beyond predominantly linear settings and establishing a route toward quantum computation for nonlinear continuum dynamics.
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Quantum computing holds transformative potential for simulating nonlinear physical systems, such as fluid turbulence. However, mapping nonlinear dynamics to the linear operations required by quantum hardware remains a fundamental challenge. Here, we bridge this gap by introducing a novel node-level ensemble description of a lattice gas, which enables the simulation of nonlinear fluid dynamics on quantum computers. This approach combines the advantages of the lattice Boltzmann method with low-dimensional representation (computational cost) and lattice gas cellular automata with linear collision treatment (quantum compatibility). Building on this framework, we propose a quantum lattice Boltzmann method that relies on linear operations with medium dimensionality, offering the potential for quantum speedup. We validated the algorithm through simulations of vortex-pair merging and decaying turbulence on up to 16.8 million computational grid points. The results demonstrate remarkable agreement with direct numerical simulation, effectively capturing the essential nonlinear mechanisms of fluid dynamics. This work potentially advances the development of quantum algorithms for other nonlinear problems across various transport phenomena in engineering.