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Salary: CAD 150,000 - 180,000 / annual
Xanadu is building quantum computers designed to solve real-world problems. This role joins a technical team of scientists focused on unlocking the practical impact of fault-tolerant quantum computers, with a specific focus on financial applications.
You will design and develop quantum algorithms for financial problems including derivative pricing, risk modeling, and portfolio optimization. Your work spans three core areas: (1) quantum algorithms for partial differential equations (PDEs) underlying financial derivatives and risk calculations, including linear-systems solvers and addressing input/output bottlenecks; (2) quantum machine learning and statistical estimation methods such as amplitude estimation, Monte Carlo approaches, and quantum regression and generative modeling; and (3) identifying high-value financial applications and establishing credible quantum advantage against classical baselines like Monte Carlo, finite-difference solvers, and deep learning.
Key responsibilities include developing core quantum subroutines (block encodings, Quantum Singular Value Transformations, data loading, Hamiltonian simulation), dramatically improving algorithm efficiency by reducing qubit counts and gate complexity, performing rigorous resource estimation, collaborating directly with financial-industry partners to translate their problems into quantum algorithmic questions, working with quantum error correction experts to compile algorithms to fault-tolerant architectures, and contributing to PennyLane software tools. You will author scientific papers, generate intellectual property and patent filings, and present research at premier conferences.
This is an algorithms-first role requiring deep expertise in quantum algorithms as the core requirement, with specialization in PDEs, machine learning, and statistical estimation. Familiarity with quantitative finance allows you to target algorithms at problems that matter. You will work directly alongside software, hardware, and business development teams.
REQUIREMENTS:
Basic qualifications:
- PhD in Physics, Computer Science, Mathematics, Engineering, Finance, or 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 quantum algorithm concepts: Hamiltonian simulation, Trotter product formulas, quantum signal processing, linear combination of unitaries, amplitude amplification, quantum phase estimation, quantum read-only memories
- Demonstrated specialization in quantum algorithms for differential equations, linear systems, and quantum machine learning
- Working knowledge of numerical PDE methods, stochastic processes, Monte Carlo estimation, statistical learning, and classical baseline methods
- Ability to reason quantitatively about algorithm cost (asymptotic scaling and leading constants, data-loading costs)
- Capacity to thrive in interdisciplinary teams and drive independent research
- Excellent communication skills for technical and non-technical audiences
- Strong self-management for balancing priorities and deadlines
Preferred qualifications:
- Previous experience applying quantum algorithms to finance or quantitative finance background
- Experience performing constant-factor resource estimation for fault-tolerant quantum algorithms
- Familiarity with derivative pricing, risk modeling, portfolio optimization, and production financial system constraints
- Practical experience with classical machine learning at scale (PyTorch, JAX) or high-performance numerical PDE solvers
- Experience collaborating with industry or government partners on application benchmarking
- Strong software development and scientific programming in Python with open-source contributions
- Understanding of quantum error correction and fault-tolerant architectures
- Knowledge of quantum compilation techniques
