In the quest to optimize industrial processes, computational fluid dynamics (CFD) simulations have long been a cornerstone, enabling engineers to model and predict fluid behavior with remarkable accuracy. However, these simulations often demand extensive computational resources, particularly when dealing with extreme turbulent regimes. Enter quantum computing, a promising frontier that could revolutionize the way we approach CFD. A recent study published in the IEEE Transactions on Quantum Engineering, titled “Toward Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations,” explores a hybrid quantum-classical approach that could significantly enhance the efficiency of these simulations.
The research, led by Francesco Turro of Quantum Computing Solutions at Leonardo S.p.A. in Genova, Italy, focuses on the lattice Boltzmann method (LBM), a popular technique for simulating fluid dynamics. The challenge lies in the nonlinear nature of the LBM equations, which are notoriously difficult to solve efficiently. Turro and his team propose a novel solution: linearizing these equations using a Carleman expansion and solving them with the quantum Harrow–Hassidim–Lloyd algorithm (HHL).
“This approach allows us to encode large-scale simulations with a limited number of qubits,” explains Turro. “By leveraging the unique properties of quantum computing, we can achieve a level of efficiency that was previously unattainable with classical digital methods.”
The study evaluates the method on three benchmark cases featuring different boundary conditions—periodic, bounce-back, and moving wall—using state-vector emulation on high-performance computing resources. The results are promising, with median error fidelities on the order of \(10^{-3}\) and success probabilities sufficient for practical quantum state sampling.
One of the most intriguing findings is that the spectral properties of small lattice systems closely approximate those of larger ones. This suggests a potential workaround for one of HHL’s major bottlenecks: eigenvalue pre-evaluation. “This could significantly reduce the computational overhead and make the method more practical for real-world applications,” Turro notes.
The implications for the energy sector are substantial. CFD simulations are crucial for optimizing everything from wind turbine designs to oil and gas extraction processes. By making these simulations more efficient, quantum computing could accelerate innovation and reduce costs across the industry.
As the field of quantum computing continues to evolve, research like Turro’s offers a glimpse into a future where quantum and classical methods converge to solve some of the most complex challenges in industrial CFD. The study, published in the IEEE Transactions on Quantum Engineering (translated to English as “IEEE Transactions on Quantum Engineering”), represents a significant step forward in this exciting journey.
While the path to practical application is still being paved, the potential is undeniable. As Turro puts it, “We are on the cusp of a new era in computational fluid dynamics, one where quantum computing plays a central role.” The energy sector, and indeed many other industries, stand to benefit greatly from these advancements, heralding a new age of efficiency and innovation.