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International Journal of Thermofluids

Unlocking quantum fluids by the transformative power of artificial intelligence

Marwan Al‐Raeei

Published Aug 18, 2026• 2 citations
Publisher

Abstract

Quantum fluids, such as superfluid helium and Bose-Einstein condensates (BECs), exhibit a range of complex quantum phenomena that challenge traditional modeling techniques. These systems involve highly nonlinear interactions especially for thermal changes, many-body correlations, and quantum entanglement, making their accurate simulation and analysis a formidable task. Over the past decade, artificial intelligence (AI), particularly machine learning (ML), has emerged as a transformative tool in addressing these challenges, offering new avenues for simulation, data analysis, and experimental optimization. In this article, we focus on the multifaceted role of AI in quantum fluid research. We explore how AI enhances simulation accuracy and efficiency through models like neural networks approximating wave functions and physics-informed neural networks (PINNs) that embed fundamental quantum laws into predictive frameworks. AI techniques such as reinforcement learning (RL) facilitate dynamic control in experiments, optimizing parameters like magnetic fields to stabilize quantum states. Data analysis methods including deep learning for pattern recognition, unsupervised clustering, and dimensionality reduction enable researchers to interpret large, multidimensional datasets, revealing hidden phase transitions, vortex structures, and turbulence phenomena. Furthermore, neural operators and foundation models offer rapid, scalable predictions of quantum dynamics and phase behaviors, accelerating discovery and understanding. Our paper highlights the advantages of AI over traditional methods, including reduced computational costs, improved scalability, and enhanced interpretability. Despite current limitations, ongoing developments point toward increasingly sophisticated AI-driven approaches that will deepen insights into quantum hydrodynamics, superfluid thermodynamics, and quantum turbulence. The merging of AI into quantum fluid research promises to unlock new levels of control and understanding, ultimately advancing quantum technology applications such as quantum computing, sensing, and cryogenics. As AI algorithms and experimental techniques continue to evolve, their synergy will play a critical role in shaping the future of quantum fluid physics.

DOI
10.1016/j.ijft.2026.101689

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