Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning

• URL: http://arxiv.org/abs/2507.08746v1
• Date: Fri, 11 Jul 2025 16:56:37 GMT
• Title: Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
• Authors: Paolo Marcandelli, Yuanchun He, Stefano Mariani, Martina Siena, Stefano Markidis,
• Abstract summary: PHQFNO partitions the Quantum Fourier Neural Operator across classical and quantum resources.<n>We show that PHQFNO recovers classical FNO accuracy.<n>On incompressible Navier-Stokes, PHQFNO achieves higher accuracy than its classical counterparts.
• Score: 2.2174431958553824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the Partitioned Hybrid Quantum Fourier Neural Operator (PHQFNO), a generalization of the Quantum Fourier Neural Operator (QFNO) for scientific machine learning. PHQFNO partitions the Fourier operator computation across classical and quantum resources, enabling tunable quantum-classical hybridization and distributed execution across quantum and classical devices. The method extends QFNOs to higher dimensions and incorporates a message-passing framework to distribute data across different partitions. Input data are encoded into quantum states using unary encoding, and quantum circuit parameters are optimized using a variational scheme. We implement PHQFNO using PennyLane with PyTorch integration and evaluate it on Burgers' equation, incompressible and compressible Navier-Stokes equations. We show that PHQFNO recovers classical FNO accuracy. On incompressible Navier-Stokes, PHQFNO achieves higher accuracy than its classical counterparts. Finally, we perform a sensitivity analysis under input noise, confirming improved stability of PHQFNO over classical baselines.

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