Quantum Technology Initiative Journal Club

Thursday 2 Oct 2025, 16:00 → 17:00 Europe/Zurich

513/R-070 - Openlab Space (CERN)

Michele Grossi (CERN)

Weekly Journal Club meetings organised in the framework of the CERN Quantum Technology Initiative (QTI) to present and discuss scientific papers in the field of quantum science and technology. The goal is to help researchers keep track of current findings and walk away with ideas for their own research. Some previous knowledge of quantum physics would be helpful, but is not required to follow the talks.

To propose a paper for discussion, contact: michele.grossi@cern.ch

16:00 → 17:00 CERN QTI Journal CLUB

Convener: Dr Michele Grossi (CERN)

16:00 Paolo Marcandelli (Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy)

TITLE: Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning

Link: https://arxiv.org/abs/2507.08746

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.

Speaker: Paolo Marcandelli (Politecnico Milan)