Developing and implementing quantum computing solutions for financial problems -ON-434Project type: Research
Desired discipline(s): Engineering - computer / electrical, Engineering
Company: Multiverse Computing
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Toronto, ON, Canada; Canada; Canada
No. of positions: 1
Desired education level: CollegeUndergraduate/BachelorMaster'sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: No
About the company:
Multiverse is a well funded deep-tech Canadian company. We are one of the few world companies working with Quantum Computing.
We provide hyper-efficient software for companies from the financial industry wanting to gain an edge with quantum computing and artificial intelligence. Our main verticals are fraud detection, credit scoring assessment, and financial optimization.
Our team of experts is world-renowned for innovative approaches to intractable financial and macro-economics problems. We work with quantum hardware and quantum inspired methods to build machine learning solutions which exceed the predictive power of the current best solutions. We are applying to Mitacs to allow us to expand our R&D team in Canada to tackle problems of huge commercial impact and expand our expertise.
Describe the project.:
During the project, the researcher will be able to learn and develop abilities from a fast-paced startup environment. The student will participate in the creation and implementation of new Machine Learning models and benchmark our algorithms against the industry’s bleeding edge. The intern will contribute to designing and improving Multiverse’s codebase. The researcher will contribute to designing and improving Multiverse’s quantum-inspired algorithms. He/She will implement and integrate new quantum algorithms.
The quantum computing paradigm promises to disrupt the way quantitative problems are tackled across the entire industry spectrum. We plan to harness the power of quantum computing to address a variety of real-world problems, like hard-optimization tasks and large-scale machine learning. The intern will develop algorithms both from the gate-based and analog-based perspective. As for an example, we plan to develop new QAOA-based algorithms for optimization purposes and push the boundary of supervised machine learning through the design and implementation of quantum ensemble classifiers. We also plan to investigate the interplay between neural networks and quantum algorithms leveraging the concept of transfer learning.
- Candidate of Masters (or PhD) degree in Quantum Physics, Computer Science, Operations Research, Mathematics.
- Knowledge of quantum computing algorithms, particularly gate model and hybrid techniques.
- Advanced knowledge of Python and related scientific computing tools.
- Experience with quantum computing SDKs (QISkit, Pennylane, or others).
- Knowledge of machine learning and deep learning frameworks (Sklearn, Tensorflow, etc)
- Ideal: In the last year of studies.