Quantum-inspired fluid simulation of two-dimensional turbulence with GPU acceleration
Leonhard Hölscher, Pooja Rao, Lukas Müller, Johannes Klepsch, André Luckow, Tobias Stollenwerk, Frank K. Wilhelm
Abstract
Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a spectrum of fluid phenomena, from the aerodynamics of vehicles to weather patterns. Within this quantum-inspired paradigm, velocity is encoded as matrix product states (MPS), effectively harnessing the analogy between interscale correlations of fluid dynamics and entanglement in quantum many-body physics. This particular tensor structure is also called quantics tensor train (QTT). By utilizing NVIDIA's cuQuantum library to perform parallel tensor computations on GPUs, our adaptation speeds up simulations by up to 12.1 times. This allows us to study the algorithm in terms of its applicability, scalability, and performance. By simulating two qualitatively different but commonly encountered 2D flow problems at high Reynolds numbers up to <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mn>1</a:mn> <a:mo>×</a:mo> <a:msup> <a:mn>10</a:mn> <a:mn>7</a:mn> </a:msup> </a:mrow> </a:math> using a fourth-order time stepping scheme, we find that the algorithm has a potential advantage over direct numerical simulations in the turbulent regime as the requirements for grid resolution increase drastically. In addition, we derive the scaling <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:mrow> <b:mi>χ</b:mi> <b:mo>=</b:mo> <b:mi mathvariant="script">O</b:mi> <b:mrow> <b:mo>(</b:mo> </b:mrow> <b:mtext>poly</b:mtext> <b:mo>(</b:mo> <b:mn>1</b:mn> <b:mo>/</b:mo> <b:mi>ε</b:mi> <b:mo>)</b:mo> <b:mrow> <b:mo>)</b:mo> </b:mrow> </b:mrow> </b:math> for the maximum bond dimension <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"> <d:mi>χ</d:mi> </d:math> of MPS representing turbulent flow fields, with an error <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:mi>ε</e:mi> </e:math> , based on the spectral distribution of turbulent kinetic energy. Our findings motivate further exploration of related quantum algorithms and other tensor network methods.