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 capable of solving problems beyond the reach of classical computation.

Getting there will require several generations of intermediate architectures. Choosing the right ones means understanding what can be built, what these systems can compute and how efficiently they can do so.

About this position:

You will join Quandela’s Quantum Information team within Architecture, working on the evaluation and design of our near- and mid-term quantum architectures.

Your role will sit between quantum theory, computational capability and real hardware constraints. You will develop methods to evaluate architecture concepts, understand their performance and limitations, and help guide future architecture choices.

Depending on your background, you may draw on complexity theory, quantum learning theory, quantum-system characterisation, benchmarking or related areas. You will work closely with our Device Physics, FTQC and Q.Algorithms Team.

Requirements

What you'll do:

As a Quantum Information Scientist working on Architecture & Performance, you will:

Evaluate and compare the capabilities and limitations of near- and mid-term quantum architectures.
Draw on methods from complexity theory, benchmarking, characterisation and related fields to assess different architecture designs.
Incorporate realistic noise, native operations and physical constraints into your analyses.
Investigate when and how different architectures could provide meaningful computational capabilities or advantages.
Develop scientific models and numerical tools to explore architecture-performance trade-offs.
Study how architecture choices interact with algorithms, resource requirements and QEC strategies.
Progressively propose and lead new research projects around architecture design and performance.

You will work across our Architecture team and Algorithms and Applications team, connecting questions from the physical layer to computational capability and potential applications.
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How you'll grow:

During your first 3–6 months
Become familiar with Quandela’s technologies, architecture roadmap and ongoing research while contributing to existing evaluation and performance studies.

Within 1 year
Take increasing ownership of architecture studies, compare different concepts and bring your own scientific perspective to the team.

Longer term
Lead exploratory research projects, propose new architecture directions and contribute to shaping Quandela’s future architecture roadmap.

What we're looking for:
We are looking for a scientist with a strong theoretical background in quantum computing, scientific autonomy and an interest in connecting different layers of the quantum-computing stack.

Must-have

PhD in Quantum Information, Quantum Computing, Physics, Mathematics, Computer Science or a closely related field.
Strong expertise in one or more relevant areas, such as quantum complexity theory, quantum learning theory, quantum-system characterisation, performance analysis or quantum algorithms.
Experience connecting theoretical or computational questions with realistic implementation, hardware or experimental constraints.
Ability to assess the capabilities and limitations of quantum systems or architectures.
Experience with scientific modelling, numerical simulation and Python-based scientific computing.
Scientific autonomy and ability to structure open-ended research problems.
Collaborative and personable, with the ability to communicate ideas clearly to people from other fields of expertise.
Professional English.

Important, but you can grow here

Quantum Error Correction and early fault-tolerant architectures.
Hardware-aware algorithm or architecture design.
Quantum benchmarking and performance metrics.
Noise modelling.
Resource and computational-cost analysis.
Collaboration with experimental or hardware-oriented quantum-computing teams.

Bonus

Quantum compilation or native-gate-aware circuit design.
Photonic quantum computing.
Resource estimation.
Experience comparing quantum approaches against strong classical baselines.
Previous ownership of collaborative research projects.

You do not need to cover every topic above. We welcome both candidates with postdoctoral or equivalent industrial R&D experience and strong recent PhDs demonstrating scientific autonomy.

Benefits

Join a growing quantum-computing company and contribute directly to the research behind future generations of quantum architectures.

Explore open scientific questions grounded in real hardware constraints.
Work across Quantum Information, Device Physics, FTQC and Algorithms.
Take ownership of broad and exploratory research projects.
Contribute to the scientific directions of a growing team.

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
Interview with Quantum Information hiring manager, 30-45 min
Presentation of previous research work and discussion with members of the theory team
Reference checks
Technical discussion based on a scientific paper or research topic
Offer

For the technical exercise, you may be asked to explore scientific material outside your direct area of expertise and discuss how you would connect it to a broader architecture problem.