Innovation Placement in Quantum Field Theory

INNOVATION PLACEMENT IN QUANTUM FIELD THEORY

In collaboration with ATOS and the University of Edinburgh, DiRAC is pleased to invite applications for a 6-month Innovation Placement looking at the application of Quantum Computing to Quantum Field Theory.

Applications are now closed

DiRAC Innovation Placements are a great opportunity for students and early career researchers to work with deep technology companies on 6-month industry-academia projects, using cutting-edge approaches to learn new skills.  This project is designed to address a particular research challenge and provide useful insight to both the academic University partner and the industrial partner. 

You must get your supervisor’s or PI’s permission before applying for this placement. Participation in the placement scheme is allowed under UKRI’s rules, but only with your supervisor/PI’s consent. We will do our best to be flexible; part time working can be arranged as long as the placement does not exceed 9 months. 

Background: 

Quantum Field Theory (QFT) is the current framework for describing relativistic quantum processes. The Standard Model of particle physics is formulated as a QFT and provides a unified description of the interactions between the elementary constituents of matter, which has been successfully tested to unprecedented levels of precision. The rich phenomenology of QFTs is difficult to explore beyond the regime where the coupling is weak and perturbative methods can be used. Jordan et al (https://arxiv.org/abs/1112.4833) have developed a quantum algorithm to compute relativistic scattering amplitudes, which has become one of the foundational results for applications of quantum computing to QFT.  

The Atos Quantum Learning Machine (Atos QLM) is a complete environment designed for quantum software developers.  It is dedicated to the development of quantum software, training and experimentation and embeds a programming platform and a high-performance quantum simulator.  The QLM allows researchers to develop and experiment with quantum software and emulates execution as a genuine quantum computer would. myQLM is a bespoke python package designed to democratize quantum computing by allowing researchers and developers to create and simulate quantum circuits on their laptops. It is fully compatible with the Atos QLM and the successful applicant will launch their myQLM program on Atos QLM to benefit from larger simulation capabilities and advanced features like quantum circuit optimizers and noisy simulators. For more information on the Atos QLM and myQLM see: https://atos.net/en/solutions/quantum-learning-machine.   

Project Description:  

The aim of the project is to study the algorithm suggested by Jordan et al and write a first implementation for the QLM using myQLM. The project is divided into 2 phases, each lasting approximately 3 months.  

Phase 1:  The initial learning phase is devoted to gaining a familiarisation of the details of Jordan’s algorithm and, using training materials, provided by Atos, an understanding of the basics of Quantum Computing and myQLM. Deliverables from this initial phase will be training materials including an introduction to ‘Quantum Computing for particle physics’ and a summary of Jordan’s theory.  

Phase 2: The second phase of the project will focus on the implementation of the algorithm for the simplest case of a scalar field theory. The deliverable for the second phase will be the QLM implementation and a first benchmark of the code.  

Prerequisite: 

The prerequisite for this project is a good knowledge of QFT and quantum mechanics. Experience of Python Programming is also desirable.

Placement:  

The successful candidate will work with Professor Luigi Del Debbio from the University of Edinburgh School of Physics & Astronomy’s High Energy Physics Group. In phase 1 there will also be the opportunity to work with Atos training staff.  It is expected that the placement will be fully remote.

Application Process:  
  • The Closing date for applications is 12pm Monday 21st March 2022 
  • Ideally, the candidate would start on or before 31st March 2022, but later start dates can be accommodated if necessary.  
  • Speak to your current supervisor/line manager and get their views BEFORE applying. 
  • Contact  Clare Jenner, DiRAC Deputy Director & Project Scientist, c.jenner@ucl.ac.uk, if you need further information. 
  • Complete the application form here. 
  • Send your completed application form and CV to Clare Jenner c.jenner@ucl.ac.uk
  • Selection process: shortlisted candidates will be invited to attend a zoom interview with representatives from both DiRAC and the placement hosts.