Quantum computing of nonlinear reacting flows via the probability density function method

• URL: http://arxiv.org/abs/2512.07918v1
• Date: Mon, 08 Dec 2025 14:19:16 GMT
• Title: Quantum computing of nonlinear reacting flows via the probability density function method
• Authors: Jizhi Zhang, Ziang Yang, Zhaoyuan Meng, Zhen Lu, Yue Yang,
• Abstract summary: Quantum computing offers the promise of speedups for scientific computations, but its application to reacting flows is hindered by nonlinear source terms.<n>We present a quantum framework to address these issues.<n>We employ a probability density function (PDF) formulation to transform the nonlinear reacting-flow governing equations into high-dimensional linear ones.
• Score: 11.30664113525107
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum computing offers the promise of speedups for scientific computations, but its application to reacting flows is hindered by nonlinear source terms and the challenges of time-dependent simulations. We present a quantum framework to address these issues. We employ a probability density function (PDF) formulation to transform the nonlinear reacting-flow governing equations into high-dimensional linear ones. The entire temporal evolution is then solved as a single large linear system using the history state method, which avoids the measurement bottleneck of conventional time-marching schemes and fully leverages the advantages of quantum linear system algorithms. To extract the quantity of interest from the resulting quantum state, we develop an efficient algorithm to measure the statistical moments of the PDF, bypassing the need for costly full-state tomography. A computational complexity analysis indicates the potential for a near-exponential speedup over classical algorithms. We validate the framework by simulating a perfectly stirred reactor, demonstrating its capability to capture the PDF evolution and statistics of a nonlinear reactive system. This work establishes a pathway for applying quantum computing to nonlinear reacting flows.

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