About Xanadu

Xanadu’s mission is to build quantum computers that are useful and available to people everywhere. At Xanadu, we are learners, innovators, researchers, collaborators and problem solvers. We are creating something that has never been built before. What we are doing is extremely hard, the classic moon shot. Few people in their life will be able to be a part of something like this, where if we are successful, the technologies we develop will solve some of the world’s most challenging problems, and literally change the world. And that is something to be excited about

Your role and responsibilities

As a quantum scientist, you will join a technical team of scientists dedicated to unlocking the real-world impact of fault-tolerant quantum computers. Your focus will be financial applications: quantum algorithms for partial differential equations (PDEs), quantum machine learning, and statistical estimation, and the question of where they can beat the classical methods banks and asset managers use today. This is an algorithms-first role. Deep expertise in quantum algorithms is the core requirement, with specialization in partial differential equations, machine learning, and statistical estimation; familiarity with quantitative finance is what lets you aim those algorithms at problems that matter. This research is conducted directly alongside industry partners, requiring close collaboration with software, hardware, and business development teams across the company. In more detail, you will focus deeply on two or more of the following areas:

- Design quantum algorithms for the PDEs underlying derivative pricing and risk, including linear-systems and differential-equation solvers and the input/output bottlenecks that govern their usefulness.




- Develop quantum machine learning and statistical estimation methods — amplitude estimation, Monte Carlo, and quantum approaches to regression, generative modelling, and time-series problems.
- Identify high-value financial applications — pricing, risk and VaR, portfolio construction, calibration, fraud and anomaly detection — and establish credible advantage against solid classical baselines such as Monte Carlo, finite-difference and finite-element solvers, and modern deep learning.
- Develop the core subroutines that quantum algorithms depend on: block encodings, Quantum Singular Value Transformations, data loading, and Hamiltonian simulation.
- Radically improve algorithm efficiency by reducing qubit counts, non-Clifford gate complexity, and overall resource requirements.
- Demonstrate feasibility and scalability through rigorous, constant-factor resource estimation.
- Collaborate directly with financial-industry partners to translate their problems into well-posed quantum algorithmic questions and deliver honest assessments of what is achievable.
- Work alongside compilation and quantum error correction (QEC) experts to compile these algorithms to fault-tolerant architectures.
- Collaborate with PennyLane software developers to create state-of-the-art tools for quantum algorithms research.

Regardless of your specific focus areas,



you will consistently:

- Author high-quality scientific papers, technical reports, and comprehensive documentation.
- Generate valuable intellectual property and actively support the filing of related patents.
- Present your research findings to strategic partners and at premier conferences and events.

Basic qualifications and experience

- PhD in Physics, Computer Science, Mathematics, Engineering, Finance, or a related quantitative discipline — or equivalent practical experience.
- Strong track record of research accomplishments in quantum computing, evidenced by publications, patents, or technical work.
- Deep knowledge of fundamental concepts in quantum algorithms, such as Hamiltonian simulation, Trotter product formulas, quantum signal processing, linear combination of unitaries, amplitude amplification, quantum phase estimation, and quantum read-only memories.
- Demonstrated specialization in quantum algorithms for differential equations and linear systems, and quantum machine learning.
- Working knowledge of numerical PDE methods, stochastic processes, Monte Carlo estimation, statistical learning, and of the classical methods that serve as baselines.
- Ability to reason quantitatively about the cost of an algorithm — not only asymptotic scaling, but where the leading constants and data-loading costs actually come from.
- Capacity to thrive in an interdisciplinary team environment and drive independent research when required.
- Excellent communication skills, with a proven ability to convey complex technical ideas to specialist and non-specialist audiences.
- Strong self-management skills to balance individual proje

📌 Quantum Scientist — Finance (Toronto)
🏢 Xanadu
📍 Toronto