Computational Metastasis Lab
Dr. Nima Maftoon
The Computational Metastasis Lab is looking for graduate students with strong background in microfabrication, image processing or CFD, computational solid mechanics and fluid-structure interaction. Following experience and knowledge are highly favourable: lattice-Boltzmann method, finite-element method, 3-D model reconstruction using serial images, image processing algorithms, Fortran, C and Python programming.
Canadian and permanent resident students and students with funding are strongly encouraged to apply.
If interested, please send me an email at firstname.lastname@example.org, summarizing your education, background and interests along with your CV and transcripts.
Mitacs - Accelerate Internship Opportunity
ProteinQure, a Toronto-based Fields-CQAM incubated startup, is seeking a candidate with a PhD in quantum information or an appropriate field (theoretical physics, computer science, statistics or mathematics) for a 4 - 6 month internship with an immediate start date.
ProteinQure is a software platform for computational peptide drug discovery, that combines quantum computing, molecular simulations and machine learning to enable structural-based drug design. They are one of the top graduates of the Quantum Machine Learning stream at the Creative Destruction Lab incubator (University of Toronto) and have partnerships with several quantum computing hardware providers (Rigetti, IBM, Xanadu, D-Wave and Fujitsu).
One of ProteinQure's major goals is to use the results obtained from noisy quantum computations to improve and speed up molecular dynamics simulations on classical hardware. For example, one of our quantum algorithms models protein folding as an optimization problem on a discrete lattice. In our most recent work, we adapted the Quantum Approximate Optimization Algorithm (QAOA) to solve the protein folding problem on universal gate based quantum computers, including IBM and Rigetti quantum computers.
The QAOA algorithm has become very popular among researchers due to its applicability and usefulness in the near-term. In the next 3-5 years, researchers expect noisy intermediate scale quantum computers without error correction rather than fault-tolerant devices. The QAOA belongs to the class of variational quantum algorithms which have shown to be robust against certain types of errors. It is expected that quantum advantage (often called quantum supremacy) will be demonstrated with such a variational algorithm in the near future. However, a known problem with this algorithm is choosing “good” initial parameters becomes more difficult as the quantum computers scale. Current implementations often start with random parameter initialization.
The main research question to be explored in this internship is how to best choose the initial parameterization for QAOA in order to avoid barren plateaus when it is being applied to combinatorial optimization problems. With the specific example of protein folding providing a real world case study. This would address and ideally resolve the concerns outlined in the aforementioned paper. Any progress in this area will have big impact on the scalability of variational approaches to near-term quantum computing in general.
Determine a suitable method for choosing initial parametrization of QAOA based on the energy landscape of the chosen problem
Benchmark different approaches
Test strategy on the problem of protein folding
Consider how to translate information from energy landscapes to choice of initial parameters
Create algorithm (or strategy) for choosing initial parametrization of a given problem
Consider specific examples in protein folding
Use quantum computers and quantum computer simulators to benchmark the approach vs. other strategies (such as random initialization). QPU access will be provided by ProteinQure
Expertise and Required Skills:
Familiarity with quantum computing algorithms
For more information please see the Mitacs website. Interested students will need to get approval from their supervisor and send their CV along with a link to their supervisor’s university webpage prior to application. Apply here.