Quantum Technology Initiative Journal Club

Thursday Jun 27, 2024, 4:00 PM → 5:00 PM Europe/Zurich

513/R-070 - Openlab Space (CERN)

Alice Barthe (Leiden University (NL)), Michele Grossi (CERN), Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))

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

4:00 PM → 5:00 PM CERN QTI Journal CLUB: TITLE

Convener: Dr Michele Grossi (CERN)

4:00 PM Ema Puljak

TITLE: Dequantizing quantum machine learning models using tensor networks

Link to the paper: https://arxiv.org/abs/2307.06937

Abstract:
Ascertaining whether a classical model can efficiently replace a given quantum model -- dequantization -- is crucial in assessing the true potential of quantum algorithms. In this work, we introduced the dequantizability of the function class of variational quantum-machine-learning~(VQML) models by employing the tensor network formalism, effectively identifying every VQML model as a subclass of matrix product state (MPS) model characterized by constrained coefficient MPS and tensor product-based feature maps. From this formalism, we identify the conditions for which a VQML model's function class is dequantizable or not. Furthermore, we introduce an efficient quantum kernel-induced classical kernel which is as expressive as given any quantum kernel, hinting at a possible way to dequantize quantum kernel methods. This presents a thorough analysis of VQML models and demonstrates the versatility of our tensor-network formalism to properly distinguish VQML models according to their genuine quantum characteristics, thereby unifying classical and quantum machine-learning models within a single framework.

Speaker: Ms Ema Puljak (Universitat Autonoma de Barcelona (ES))

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