Quantum Information Scientist - Tensor Network Simulations

About Quandela: 

Quandela is a global leader in quantum computing, designing, building, and delivering cutting-edge quantum solutions for research and industry. Its offerings include the most energy-efficient quantum computers for data centers, full-stack quantum computing solutions accessible via the cloud, and algorithm access services for academic and industrial customers.
Following a pragmatic, step-by-step roadmap, Quandela has been deploying industrial-grade systems since 2023 while developing future generations of fault-tolerant quantum computers capable of scaling through the integration of thousands of photonic components. Quandela is committed to making quantum computing accessible to all in order to address the most complex industrial and societal challenges.
Learn more at: Quandela | Leading Photonic Quantum Computing Solutions

Our ambition is to build large-scale fault-tolerant quantum computers. Within Architecture, we develop methods to model and simulate our systems, understand their performance and guide future architecture choices.

About this position: 

You will work across our Device Physics and Quantum Information teams, bringing practical expertise in Tensor Network methods to develop a new simulation capability at Quandela.

You will apply and adapt approaches such as MPS, PEPS and related methods to quantum systems, circuits and future architectures.

The scientific questions will be defined together with the teams. Your role will be to determine how to address them: identify relevant literature, select numerical methods, develop simulations and assess their limitations.
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What you'll do:

As a Quantum Information Scientist working on Tensor Network Simulations, you will: 

• Apply Tensor Network methods to the classical simulation of quantum systems, circuits and architectures. 
• Investigate scientific questions, review relevant approaches and select suitable numerical methodologies. 
• Develop research-grade simulations adapted to Quandela’s hardware and future SPOQC architectures. 
• Study entanglement growth, simulation of complexity and the propagation of physical noise. 
• Explore different hardware configurations and architecture choices through numerical modelling. 
• Simulate quantum algorithms and contribute to comparisons with relevant classical approaches. 
• Develop reusable scientific code and document methods so they can support research across different teams. 
• Contribute to scientific discussions, publications and future research directions around classical simulation.

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How you'll grow 

During your first 3 months: become familiar with Quandela’s architectures and research questions, investigate relevant methods and propose a first simulation approach.

Within 6–12 months: develop and validate your methods on concrete problems, build reusable tools and take increasing ownership of projects across Device Physics and Quantum Information. 

Longer term: become a key contributor to classical simulation at Quandela and extend your work toward future architectures, compilation or algorithm-level studies.

What we're looking for: 

We are looking for a researcher with practical Tensor Network experience and the autonomy to develop numerical approaches for open research problems.

Relevant backgrounds include computational or quantum many-body physics, quantum information, mathematics and computer science. Previous experience in photonic quantum computing is not required.

Must-have: 

• PhD in Physics, Mathematics, Computer Science, Quantum Information or a closely related field, or equivalent research experience. 
• Practical experience with Tensor Network methods, such as MPS, PEPS or closely related approaches. 
• Experience developing or adapting numerical simulations for quantum systems, circuits or algorithms. 
• Ability to move from an open scientific question to literature review, methodology selection, implementation and validation. 
• Scientific programming skills in Python, Julia and/or C++. 
• Ability to assess the limits of a numerical method, including trade-offs between accuracy, computational cost and scalability. 
• Scientific autonomy and ability to collaborate across different research disciplines. 
• Professional English. 

Important, but you can grow here:

• Classical simulation of quantum circuits or quantum algorithms. 
• Quantum noise modelling. 
• Efficient classical algorithms and benchmarking against classical baselines. 
• Quantum compilation and native-gate-aware simulation.
• Development of reusable scientific software and collaborative code practices. 
• Experience working with hardware-specific constraints. 

Bonus: 

• Experience with tools such as ITensor, TeNPy or Quimb. 
• DMRG, TEBD or other Tensor Network techniques beyond MPS / PEPS. 
• Quantum-inspired or dequantised algorithms. 
• Photonic or spin-photonic quantum computing. 
• Experience applying Tensor Networks to architecture evaluation or quantum-advantage studies. 

You do not need experience across every application above. Practical Tensor Network expertise, strong numerical reasoning and the ability to develop a method when the path is not predefined are what matter most.

Postdoctoral or equivalent research experience is particularly relevant, while strong recent PhDs with substantial hands-on Tensor Network experience are also encouraged to apply.

Join a research environment where advanced numerical methods can directly influence how future quantum architectures are evaluated and developed. 

• Work across Device Physics, Quantum Information, FTQC and potentially Quantum Algorithms. 
• Apply Tensor Network methods to concrete hardware and architecture questions. 
• Take scientific ownership of open research problems and progressively build a reusable simulation capability. 
• Contribute to publications, conferences and broader research directions as the scope develops. 

Plus: 

• Competitive compensation aligned with your experience and level of responsibility. 
• Company savings plan. 
• 100% health coverage through Alan. 
• Transport reimbursement or sustainable mobility bonus. 
• Swile meal vouchers. 
• Access to Gymlib. 

Process: 

• Talent Acquisition interview, 30–45 min 
• Scientific interview with members of the relevant research teams 
• Discussion of your previous research and Tensor Network experience 
• Technical scientific discussion 
• References check
• Final discussion with the team 
• Offer 

The scientific discussions are intended to understand how you approach an open research problem, identify relevant methods, assess their limitations and turn them into a practical numerical approach. 

Beyond that 
If you enjoy turning open scientific questions into numerical methods, this role offers the opportunity to develop Tensor Network simulation capabilities at Quandela and apply them directly to future quantum systems.