Quantum Technology Initiative Journal Club

Thursday 25 Sept 2025, 09:30 → 10:30 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

09:30 → 10:30 CERN QTI Journal CLUB

Convener: Dr Michele Grossi (CERN)

09:30 Lirandë Pira (Centre for Quantum Technologies, National University of Singapore)

TITLE: QKAN: Quantum Kolmogorov-Arnold Networks

Link: https://arxiv.org/abs/2410.04435

Abstract: The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architecture, called Kolmogorov-Arnold Networks (KAN), has emerged, inspired by the compositional structure of the Kolmogorov-Arnold representation theorem. In this work, we design a quantum version of KAN called QKAN. Our QKAN exploits powerful quantum linear algebra tools, including quantum singular value transformation, to apply parameterized activation functions on the edges of the network. QKAN is based on block-encodings, making it inherently suitable for direct quantum input. Furthermore, we analyze its asymptotic complexity, building recursively from a single layer to an end-to-end neural architecture. The gate complexity of QKAN scales linearly with the cost of constructing block-encodings for input and weights, suggesting broad applicability in tasks with high-dimensional input. QKAN serves as a trainable quantum machine learning model by combining parameterized quantum circuits with established quantum subroutines. Lastly, we propose a multivariate state preparation strategy based on the construction of the QKAN architecture.

Speaker: Prof. Lirandë Pira (Centre for Quantum Technologies, National University of Singapore)