PhD scholarship in Quantum Machine Learning for Healthcare

This multi-disciplinary PhD thesis will be focused on the design, development and application of novel methods for data analysis based on quantum computing, in what is known as quantum machine learning (QML) (1). Quantum computing is known to outperform classical computers in tasks such as unstructured search (Grover’s algorithm) and factorization (Shor’s algorithm), fundamental to applications such as cryptography. QML explores the potential benefits of quantum representations and mechanisms such as superposition and entanglement for massively parallel processing. Current research is limited by the state of quantum technology, which is in its infancy but developing at an impressive pace.
This PhD candidate will carry out both methodological and applied research towards exploring the potential of QML for healthcare. Related work in our group has focused on quantum computing embeddings for medical imaging data and quantum machine learning algorithms for classification of lung cancer(2). Current on-going research also includes preliminary work on quantum generative models, such as quantum GANs(3), quantum VAEs(4) and quantum circuit Born machines(5). We will explore the use of these techniques for health data, as well as devising novel approaches based on quantum adaptations of algorithms such as optimal transport normalizing flows, which are being developed by our group to analyze medical imaging data(6).
In a related parallel line of work, we will also explore quantum-inspired algorithms, such as those based on tensor networks(7), for which promising preliminary results have been reported for big data analysis tasks. The applicability of these techniques for multimodal clinical data exploration will be studied.
We will make extensive use of open access software libraries (e.g. Qiskit, Pennylane, Cirq), as well as open quantum simulators and hardware systems, such as those provided by IBM and Xanadu. Furthermore, we will work with existing collaborators in this line of research, such as the Barcelona Supercomputing Center.

The PhD supervisor will be Prof. Miguel A. González Ballester, ICREA Professor at UPF.

This position includes a teaching commitment load of 45 hours per academic year.

Requirements:

1. Candidates are required to have a Bachelor's degree and an MSc in a relevant field, such as e.g. computer science, engineering disciplines, physics or mathematics. MSc students currently enrolled in programmes that are expected to finish by the date of project start can also apply.
2. Although not a strict requirement, previous experience in quantum computing, and in particular quantum machine learning, as well as proven experience in the use of related libraries such as Qiskit and Pennylane, will be highly valued.
3. Complementarily, experience in biomedical data science and/or computer vision applied to medical data will also be an important asset.
4. Good programming skills, particularly in Python, and experience in the use of deep learning / data science libraries is also of high relevance.
5. Excellent level of English language is required.
6. Admission in the PhD program of the Department of Information and Communication Technologies at UPF is a prerequisite to enjoy the contract.

Starting date (planned): Oct/Nov 2023

Application deadline: 30 June 2023

Gross yearly salary: 20.200€ (1st and 2nd year), 21.043€ (3rd year), 24.204€ (4th year).

For application or further information, contact Prof. Miguel A. González Ballester, BCN Medtech – Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
Please send your CV and motivation letter, along with any additional relevant material, to the email address: ma.gonzalez@upf.edu

This position is co-funded by the PhD fellowship program of the Department of Information and Communication Technologies at Universitat Pompeu Fabra (DTIC-UPF), and the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond. Its benefits and conditions are available at: https://www.upf.edu/web/etic/predoctoral-research-contracts.
More information about the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond: https://www.upf.edu/web/mdm-dtic .

___________________________

(1) Schuld M., Petruccione F. (2021). Machine learning with quantum computers. Springer.

(2) Riobó L. (2022). Quantum machine learning for medical applications. MSc thesis, UPF.

(3) Assouel A. et al. (2022). A quantum generative adversarial network for distributions. Quantum Machine Intelligence, vol. 4(28):1-19.

(4) Kohshaman A. et al. (2018). Quantum variational autoencoder. Quantum Science and Technology, vol. 4.

(5) Gili K. et al. (2022). Do quantum circuit Born machines generalize? arXiv:2207.13645.

(6) Masias M. et al. (2022). Predicting structural brain trajectories with discrete optimal transport normalizing flows. Medical Imaging Meets Neurips (Med-Neurips), New Orleans, USA, pp. 99.1-4.
(7) Stoudenmire E. et al. (2016). Supervised learning with quantum-inspired tensor networks. arXiv:1605.05775.