Quantum Innovation Placement in Radio Astronomy
In collaboration with Google and the University of Manchester, DiRAC is pleased to invite applications for a 6-month Innovation Placement focussing on a Quantum Machine Learning pulsar classification model in the field of Radio Astronomy.
DiRAC Innovation Placements are a great opportunity for doctoral students and early career researchers to work on a 6-month industry-academic project that delivers research impact and develops key skills. Offering the potential to gain industrial experience by working collaboratively with businesses and fostering fundamental research competencies through academic mentoring, these competitive, fully funded internships address current challenges with cutting edge solutions. The projects are designed to tackle particular research challenges 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. The placement will be remote.
Google is advancing state of the art quantum computing and developing tools for researchers to operate beyond classical capabilities. This project will utilise Google’s Qsim Quantum Simulator – a full wave function simulator written in C++ which uses gate fusion, AVS/FMA vectorised instructions and multi-threading using OpenMP to achieve simulations of quantum circuits. Depending on progress, the possibility exists for the project to ultimately transition to Google’s quantum processor hardware.
The project’s academic mentor is Professor Anna Scaife from the University of Manchester’s Astronomy and Astrophysics Theory Group. Professor Scaife is head of the Jodrell Bank Centre for Astrophysics Interferometry Centre of Excellence and a Fellow of the Alan Turing Institute, where her research focusses on AI discovery in data intensive astrophysics.
The project will work with the Quantum Machine Learning (QML) pulsar classification model from Kordzanganeh, Utting & Scaife (NeurIPS 2021) and will be divided into two phases:
Phase 1: The initial learning phase will be devoted to understanding the basics of Quantum Computing and the Google quantum simulator training materials. The successful candidate will also gain a familiarisation of the details of Kordzanganeh, Utting & Scaife (NeurIPS 2021) and compare and contextualise that model with respect to the Google materials. During this period the student will create an implementation of the QML pulsar model for deployment on the Google quantum simulator.
Phase 2: Using the Google quantum simulator, the student will evaluate the performance of the QML pulsar model. This will include benchmarking a selection of different quantum encodings and encoding depths, evaluating the performance impact of the entangling layers in the multi-qubit model, determining how the algorithm will scale for different volumes of train/test data, and undertaking a trade-off analysis exploring the computational cost of using different numbers of qubits vs. different levels of parallelisation as a function of machine learning model performance. Depending on progress in this phase of the project there is the possibility of using Google’s quantum hardware to compare the performance of real hardware to the quantum simulator.
This project is suitable for students who have experience in Python programming. Experience with deep-learning is desirable but not required as the principles of QML will be covered in the first 3 months of the internship. No previous experience of quantum computing is required. Code for the QML pulsar classification model is available in Python (using the pennylane QML library).
The successful candidate will work with Professor Anna Scaife as a research point of contact, with support from collaborators in the University of Manchester Physics Theory Group. Access to Google materials and systems will be facilitated by Google High Performance Computing consultants and Google.edu, a service which assists students and researchers to use Google technology.
This placement is now closed to applications.