Quantum AI Report

The convergence of Quantum with AI

CERN

CERN is a European particle physics laboratory that runs a Quantum Technology Initiative, applying quantum computing, sensing, and networking to challenges in high-energy physics such as event reconstruction, simulation, and detector optimization.

AI-written profile · not yet reviewed · 18 August 2026

Headquarters
Meyrin, Switzerland
Founded
1954
Status
Private
quantum sensingalgorithms softwarequantum networking

Coverage

arXiv quant-ph

A Coherent Memory Register for Sequential Quantum Generative Modeling, with Application to Calorimeter Showers

A new arXiv preprint introduces a coherent memory register designed for sequential quantum generative modeling and applies it to simulating particle showers in calorimeters. The work targets the high computational cost of calorimeter simulation in high-energy physics by proposing quantum circuits as fast generative surrogates.

OutlookPlausible

If the coherent memory register can be implemented with current two-qubit gate fidelities, researchers could demonstrate small-scale sequential generative models on existing superconducting or trapped-ion processors within 1-2 years.

algorithms softwareCERNIBMQuantinuum
arXiv quant-ph

Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware

A preprint on arXiv reports a study applying quantum graph neural networks to jet classification, motivated by jet measurements at the Large Hadron Collider and the future Electron-Ion Collider. The authors explore quantum machine learning methods for jet tagging and present an implementation intended to run on quantum hardware.

OutlookPlausible

This preprint could become a reference benchmark for quantum GNN jet tagging on small datasets, with follow-up papers testing variations in encoding and circuit depth across cloud-accessible quantum processors.

algorithms softwaresuperconductingtrapped ionBrookhaven National LaboratoryCERNIBMIonQ
arXiv quant-ph

An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment

A quantum machine learning model, specifically an IQP Born Machine, was developed to generate calorimeter images for high-energy physics. The model was deployed on a 64-qubit system using compiled IQP circuits for efficient execution. The work was published as a preprint on arXiv.

OutlookPlausible

The compiled-IQP deployment method could be extended to larger qubit counts and more complex imaging tasks, offering a viable quantum alternative for fast event simulation in particle physics.

algorithms softwareCERNGoogleIBM