China

particle track reconstruction

quantum neural networks

quantum computing

physics

embedding

The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.


Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. The broad themes covered by Quantum 2020 included:

Quantum communication;
Quantum computing and quantum simulation;
Quantum sensing, metrology and imaging;
Quantum information theory, software and algorithms;
Quantum foundations;
Quantum technology in industry.
The four-day event featured talks from world-renowned scientists, interactive poster sessions and two exciting panel discussions on the themes of Quantum Technology in Industry and Quantum Technology Programmes Around the World.


You will need to [register](https://www.iopconferences.org/iop/frontend/reg/tRegisterEmailNew.csp?pageID=978403&eventID=1552&tempPersonID=536224) to obtain a ticket. . If you have already registered you should have been sent an email from Underline (to the email address that you registered with) with the subject line ‘Welcome to Quantum 2020’. This email includes your ticket for the event. 

If you have a ticket and are experiencing problems in accessing the event, please contact us. 

Quantum 2020

This meeting is a great opportunity to explore sessions and workshops designed with your professional development in mind. Present your research and exchange ideas on the latest in teaching and pedagogy. Participate in a wide range of events, activities, plenaries, and so much more, connecting you with peers and colleagues who share your commitment to excellence in physics education.

AAPT WM22

The High Luminosity Large Hadron Collider (HL-LHC) is expected to reach a significant increase in complexity and scale of data. Hence, the task of reconstructing the trajectories is getting more and more complex due to the increasing number of simultaneous collisions, occupancy and scalability. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of including quantum computing concepts within graph neural networks that are able to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of quantum circuits within the task of embedding using hybrid-classical neural network architectures and show preliminary results.

Exploring hybrid quantum-classical neural networks for particle tracking

technical paper

Single-Path Two-photon Interferences Between Spatial Structures

Quantum Generative Adversarial Networks in Continuous Variable Architecture to Simulate High Energy Physics Detectors

Bipartite energy-time uncertainty relation  and  quantum error correction for metrology with noise

Different descriptions of open quantum system dynamics - master equations

Multidimensional spectroscopy with squeezed light

Covariant Quantum Error Correcting Codes via Reference Frames

Quantum magnetism driven by vacuum fields

Using a commercial scanning single spin microscope to diagnose magnetic materials for next generation magnetic memories

Relativistic independence and the immense explanatory power of quantum uncertainty

Randomized benchmarking protocols in the all-electrical quantum dot qubit with 1/f noise

Premium content

Exploring hybrid quantum-classical neural networks for particle tracking

Downloads

Next from Quantum 2020

Single-Path Two-photon Interferences Between Spatial Structures

Similar lectures

Embedding Emotional Intelligence into Physics Education