Application of the quantum Fourier transform in a harmonic balance solver for Burgers’ equation

This paper explores the potential of quantum computing for computational fluid dynamics (CFD) applications, with a focus on turbomachinery CFD. A harmonic balance solver is developed for Burgers’ equation, in which discrete Fourier transforms are approximated using a hybrid quantum-classical algorithm based on the quantum Fourier transform (QFT). Three novel algorithms are presented to estimate Fourier coefficients, providing complete knowledge of their amplitudes and phases, which is not possible with the standard QFT. Their behaviour is studied theoretically and numerically, under noiseless and noisy conditions. The algorithms, whose performance is limited by the extensive sampling required to achieve a small error on the Fourier coefficients, tolerate low levels of depolarising noise. The behaviour of the hybrid solver is investigated for a baseline case with a Reynolds number of 1000, 7 harmonics and 100 grid cells, using the best performing algorithm in a noiseless setting with up to 10⁸ samples per QFT. Residuals decrease until errors introduced by statistical uncertainty dominate the total error on the solution. Nevertheless, the hybrid solutions match the classical one closely, with RMS residuals as low as 10^–4. The impact of several solver parameters on convergence and solution quality is also assessed, including the effect of noise. Although the latter rapidly degrades the solution, the solver achieves satisfactory approximations to the classical solution with 0.001% of depolarising noise. Without seeking to demonstrate a quantum advantage, this work offers valuable insights into the opportunities and challenges of quantum computing, helping readers understand how to design, implement and study quantum algorithms, as well as evaluate their impact in CFD applications.