Workshop on Tensor Networks and (Quantum) Machine Learning for High-Energy Physics

Contribution List

12. Welcome and introduction

Michelangelo Mangano (CERN)

04/11/2024, 10:00

Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics

2. Quantum Machine Learning Integration in the High Energy Physics Pipeline

Dr Michele Grossi (CERN)

04/11/2024, 10:05

Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics

The integration of Quantum Machine Learning (QML) into the High Energy Physics (HEP) pipeline represents a transformative approach to addressing computational challenges in the analysis of vast and complex datasets. This talk will explore the synergy between quantum computing and machine learning, demonstrating how QML algorithms can handle HEP data sets and where QML has been applied to HEP...

1. TNS and the simulation of Lattice Gauge Theories

Mari Carmen Bañuls

04/11/2024, 11:05

Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics

Tensor networks have demonstrated their suitability to describe equilibrium states of LGT in small spatial dimensions, where it has been possible to realize continuum limit extrapolations with record precision. And they are also a most adequate tool to design and benchmark quantum simulation protocols.
Real time evolution poses a more challenging scenario, which escapes the reach of the most...

3. Discussion: Utilizing QML Algorithms within Tensor Networks

04/11/2024, 12:05

Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics

4. Boosting applications in nuclear physics via recent advances in tensor network state methods

Ors Legeza

04/11/2024, 15:00

Tensor Networks and Neural Networks

Recently we have proposed combination of the valence-space in-medium similarity renormalization group (VS-IMSRG) with the density matrix renormalization group (DMRG) offering a scalable and flexible many-body approach for strongly correlated open-shell nuclei. Combined with an analysis of quantum information measures, this further establishes the VS-DMRG as a valuable method for ab initio...

5. Quantum Machine Learning: A Computer Science and Engineering Perspective

Elias Fernandez-Combarro

04/11/2024, 16:30

Tensor Networks and Neural Networks

Quantum Machine Learning is a promising new field that joins quantum computing and machine learning to obtain computational advantages in learning from data. As such, there are plenty of opportunities but also challenges in the short and medium term. In this talk, we will address some of them from the point of view of computer science and engineering, with examples and lessons learned from...

6. Discussion: Accelerating Calculations with Specialized Hardware (TPUs, GPUs) for QML and TN

04/11/2024, 17:30

Tensor Networks and Neural Networks

7. Tensor network algorithms for HEP quantum simulation

Simone Montangero (Padova University)

05/11/2024, 09:00

Prospects on Tensor Networks and Machine Learning

We review some recent results on the development of efficient tree tensor network algorithms and their applications to quantum simulation benchmarking and theoretical interpretation. In particular, we present results on lattice gauge theories 2+1 and 3+1 dimensions at finite density, and out-of-equilibrium in 1+1 dimensions. Moreover, we present a roadmap for future tensor networks simulations...

10. Optimising Tree Tensor Networks for classification on hardware accelerators

Alberto Coppi

05/11/2024, 09:45

Prospects on Tensor Networks and Machine Learning

This talk presents the development of a framework for TTN based binary classification. The primary objective of this study is to train and evaluate the performance of TTN classifiers and optimise the code for their efficient deployment on computing accelerators, such as General Purpose Graphics Processing Units (GPGPU) or Field-Programmable Gate-Arrays (FPGA). To evaluate the effectiveness of...

11. Tree Tensor Network implementation on FPGA

Lorenzo Borella (Universita e INFN, Padova (IT))

05/11/2024, 10:05

Prospects on Tensor Networks and Machine Learning

Starting from the statements of the previous talk, we present the implementation on FPGA of Tree Tensor Networks as binary classifiers, highlighting the possibility of their deployment in high-frequency real-time environments, such as the online trigger systems of HEP experiments. The linear algebra operations needed by TTNs make them easily deployable on FPGAs, which are extremely suitable...

8. Provable exponential quantum advantages in learning from physics data

Vedran Dunjko

05/11/2024, 11:00

Prospects on Tensor Networks and Machine Learning

One of the key challenges of the quantum machine learning field is identifying learning problems where quantum learning algorithms can achieve a provable exponential advantage over classical learning algorithms. Previous examples of provable advantages are all arguably contrived, and all rely on cryptographic methods to make learning hard for a classical learner. Further, they are not aligned...

9. Discussion: Using Tensor Networks to Enhance QML Algorithms

05/11/2024, 12:00

Prospects on Tensor Networks and Machine Learning