About PASQAL
PASQAL designs, develops and markets new quantum computers (QPU for Quantum Processor Unit) based on laser-cooled neutral atom technology. QPUs are machines featuring high-precision optical and laser technologies.
Joining PASQAL is an opportunity to contribute to the rapid development of a Deep-Tech startup at the forefront of the second quantum revolution.
You'll be involved in one of the biggest challenges ever, working with multidisciplinary teams and sharing our passion for shaping the technological landscape of the 21st century.
Job Description
As a Quantum algorithm developper, you will work among top scientists and developers in the quantum industry and tackle materials with digital quantum computers. You will also work with our full-stack quantum team in France, coordinating projects to run on the reality of quantum hardware.
Your missions and responsibilities will include:
- Use and develop state-of-the-art digital quantum algorithms to explore quantum materials that can be realized on PASQAL’s quantum processing unit (QPU).
- Deploy and execute large-scale classical numerical simulations on our cluster.
- Propose implementation and detection schemes tailored for neutral atom quantum processors.
- Simulate and study the inherent noise present in the device and suggest mitigation schemes.
- Work closely together with PASQAL’s Hardware teams to implement realistic proposals.
- Collaborate with PASQAL’s Software teams to develop and consolidate re-usable building blocks and protocols in topics such as error mitigation or tomography
- Project topics include primarily algorithm R&D in the domains of simulating quantum materials with a digital quantum processing unit.
- Keep a regular technological watch.
- Support an inventive activity in the scientific and technical fields related to the Company's research, products, technologies and markets; fill patents.
About you
Hard skills:
- PhD in quantum physics, quantum computing, condensed matter physics, computational physics, computer engineering
- 1+ years’ experience as a postdoc or equivalent position in the industry
- Excellent academic track record
- Demonstrable experience with Many-body physics/ digital quantum computing
- Proficient in or more programming languages including Python, Julia, Fortran, and C, with a strong aptitude for utilizing version control tools such as git, gitlab, and others.
Soft skills:
- Experience in project management
- Team spirit, ambitious, organized, proactive, sense of urgency… a true pioneer!
- Experience speaking in front of technical and non-technical audiences
- English: a good professional capability is essential (written and oral).
Bonus points for:
- Experience in working with experimental groups
- Experience in cold atom quantum computers
- Experience with large-scale numerical simulations
- Supervision of master students
We offer
Office in Massy
Hybride remote policy
Permanent contract
Impactful role in a fast pace company
Free time to train yourself and go to conferences and meetups
Hiring process
Recruiter screen 30’
Hiring manager screen 45’ to 60’
Technical interview 60’
Onsite visit of the office and meeting with the future team
Offer!
PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, because inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, race, ethnic origin, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of specialized algorithm roles focusing on quantum materials simulations represents a critical evolution in the deep-tech ecosystem, transitioning from abstract computing verification to physical application enablement. As the hardware landscape matures across neutral-atom and related modalities, the primary bottleneck for commercial value creation has shifted to domain-specific algorithm design. This role type serves as a primary translation layer, converting complex multi-body quantum states into programmable digital circuits capable of resolving material science bottlenecks. Market signals from consortia such as the QED-C highlight that optimizing these hardware-software co-dependencies is essential to mitigate the integration complexity inherent in Noisy Intermediate-Scale Quantum devices. By industrializing scientific models for hardware architectures, this function directly accelerates the commercialization timeline for advanced material discoveries and enterprise application scaling.
The quantum simulation landscape is currently transitioning from proof-of-concept laboratory benchmarks to structured integration within broader high-performance computing environments. Within this evolving framework, algorithm development for materials science sits at the critical intersection of fundamental physics and enterprise application deployment. While hardware scaling remains a parallel constraint, the intermediate viability of the industry rests heavily on algorithmic optimization, error mitigation, and structural reproducibility across fragmented hardware backends.
Ecosystem analyses indicate that the primary structural challenge in this category is the persistent deficit of professionals who possess simultaneous mastery over condensed matter theory and standard software engineering practices. This workforce scarcity creates a significant hurdle for the translation of academic algorithms into robust, production-ready libraries. Consequently, the commercial sector depends on roles that bridge the gap between abstract algorithmic logic and hardware-level noise characteristics, ensuring that simulation protocols remain reliable under real-world operational constraints.
Furthermore, public-private funding patterns and national technology initiatives are increasingly prioritizing research that shows a clear pathway toward physical utility, such as energy storage optimization or advanced semiconductor modeling. As software stack maturity varies between architectures, establishing hardware-agnostic benchmarking and re-usable protocol building blocks is paramount. This foundational coordination stabilizes the deep-tech value chain, ensuring long-term interoperability as companies scale toward early fault-tolerant operations.
The capability architecture for this role type centers on the integration of quantum information protocols with advanced numerical workflows. Mastery of many-body physics, digital gate-level compilations, and quantum noise mitigation layers is critical for maximizing the utility of existing quantum processors. These capabilities operate as the primary mechanism for reducing circuit depth and managing error propagation, directly affecting the operational throughput of deep-tech organizations.
Furthermore, this function requires a strong interface with classical computing infrastructures to support large-scale validation, benchmarking, and hybrid workflow orchestration. Developing standardized application layers and reusable algorithm components ensures that complex physics simulations can remain stable across shifting hardware modalities. This cross-functional alignment between physical architecture capabilities and application code is what ultimately enables predictable software delivery in a rapidly changing technical landscape. - Accelerates the transition of quantum material simulations from abstract academic theory into scalable industrial frameworks
- Mitigates hardware execution risks through the development of specialized digital algorithms optimized for noise-heavy environments
- Facilitates the integration of native quantum computational kernels with existing classical high-performance computing infrastructure
- Minimizes processing bottlenecks by deploying advanced error mitigation protocols at the hardware-software interface layer
- Enhances data validation reliability through the execution of large-scale classical numerical simulation benchmarks
- Optimizes code portability by constructing reusable software building blocks for diverse digital quantum architectures
- Shortens commercial product iteration cycles by aligning algorithm designs directly with physical hardware constraints
- Supports enterprise research stability by transforming complex physical constraints into definitive intellectual property and patents
- Promotes technical interoperability by contributing to standard benchmarking protocols across the quantum software stack
- Reduces development friction through structured collaboration between pure research teams and hardware systems engineers
- Secures competitive marketplace positioning by identifying high-value application domains within material science sectors
- Strengthens the broader deep-tech workforce pipeline by establishing repeatable paradigms for academic-to-industrial technology translationIndustry Tags: Quantum Materials Simulation, Digital Quantum Algorithms, Neutral Atom Architectures, Condensed Matter Physics, Error Mitigation, High-Performance Computing Integration, Software Tooling Maturity, Many-Body Physics, Deep Tech Value Chain
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