Visiting Scientist, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS)

Main: Senior Research Scientist, Quantum Machine Learning and Algorithms, Quantinuum K.K.

Enrico Rinaldi

Ph.D

Research Fields
Particle and Nuclear Physics, Machine Learning
Term and History
2019/10/01 - 2019/10/23 Research Part-time Worker Ⅰ
2019/11/16 - 2021/02/28 Visiting Scientist (Main: AI Researcher/Engineer, Arithmer Inc.)
2021/05/01 - 2022/10/31 Visiting Scientist (Main: Research Fellow, Physics Department, University of Michigan, USA)
2023/02/16 - Visiting Scientist (Main: Senior Research Scientist, Quantum Machine Learning and Algorithms, Quantinuum K.K.)
Related Websites
Enrico Rinaldi's personal website

Self-introduction

I am Enrico Rinaldi, a part-time researcher in iTHEMS, who was previously a SPDR fellow in RIKEN BNL and Quantum Hadron Physics Laboratory.
My expertise is Monte Carlo numerical simulations of quantum field theories, also known as Lattice Field Theory simulations, which use massively parallel supercomputers (CPU and GPU-based) around the world to solve the complex equations hiding the mysteries of particle physics.
My research is focused on understanding high energy strongly-coupled gauged theories, in particular in the context of extensions of the Standard Model (SM) of particle physics, like Dark Matter physics or theories of Composite Higgs. New discoveries are hinging on the theoreticians’ ability to make predictions that can be tested by experimentalists, and that is precisely the my goal.
I am also engaged in projects related to low-energy nuclear physics, for example calculating nucleon-nucleon interactions or nuclear form-factors directly from the theory of Quantum Chromo-Dynamics (QCD). Moreover, I have been working on matrix models to study the gauge/gravity duality conjecture with the aim of understanding the possible intriguing relation between gauge theories and quantum gravity.
I am currently researching new Machine Learning (ML) approaches to physics, mainly based on the promising rise of generative models. The aim is to improve our ability to get access to multi-dimensional parametric distributions describing physical systems with specific models.

Related Events

Using a trapped ion quantum computer for hamiltonian simulations

February 28 (Wed) at 10:30 - 12:00, 2024 Seminar Quantum Computation SG Seminar

Simulation-based inference for multi-type cortical circuits

November 29 (Mon) at 13:30 - 15:00, 2021 Seminar Information Theory SG Seminar

iTHEMS-phys Intro Meeting on June 1, 2021

June 1 (Tue) at 13:00 - 15:00, 2021 Seminar iTHEMS Theoretical Physics Seminar

Composite Dark matter and gravitational waves

October 20 (Tue) at 10:00 - 11:00, 2020 Seminar DMWG Seminar

A new lamppost in dark matter searches: Composite Dark Matter

October 1 (Tue) at 10:00 - 18:00, 2019 Seminar DMWG Seminar

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