About Pasqal
PASQAL designs and develops Quantum Processing Units (QPUs) and associated software tools.
Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs.
In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.
Job Description
We are looking for an engineer or researcher in the field of Operations Research (Applied Mathematics) with a solid background in exact and heuristic algorithms and in graph theory to reinforce our Quantum Graph Optimization (QGO) team.
Within the team, you will be instrumental in extending the field of applications of PASQAL Quantum Processors. This implies working on existing applications and developing new ones; your focus will be mainly in the field of graph theory, operations research and related applications.
You will be in charge of the technical relationship with existing clients, carry R&D projects with prospective ones as well as internally. You will work in close collaboration with the Engineering and R&D teams and with external partners, along all phases of the projects.
You will contribute to the development of our Intellectual Property portfolio.
In this context, your missions will be as follows:
- Investigate existing literature.
- Apply analytical and numerical tools to test the feasibility of the algorithmic methods in solving impactful and industrial use cases.
- Study the benefits of quantum computers to reach quantum advantage. Develop PoCs to investigate the risk and return for certain use cases and the limitations of classical computing in solving them.
- Develop a blueprint for a quantum advantage experiment and work with our R&D hardware team to see it come to fruition.
- Collaborate with our academic and industrial partners, and in some projects, work together with clients who possess domain-expertise about the use cases we are interested in.
- Contribute to the development of software products in collaboration with our software engineering team, running on a variety of special-purpose simulation/emulation backends locally and on our HPC cluster.
- Provide scientific support to the software engineering team.
- Investigate realistic implementations in our neutral-atom hardware, including analog and digital implementation paradigms, noisy emulations, realistic parameter settings and limitations, and limitations to the addressability and shot-rate.
- Contribute to the activities of the team pertaining to Optimization and Operations Research, notably in the form of scientific watch of new papers.
- Have an inventive activity in the scientific and technical fields related to the company's research, products, technologies and markets; filing patents.
About you
With a MSc or a PhD in Operations Research or in a related field with at least 5 years in a similar position, you have the following assets:
- Experience with at least one of the following optimization frameworks: SCIP, DIP, PuLP, DipPy, Pyomo, JuMP
- Experience with at least one of the following programming languages: C, C++, Python, CUDA, Julia
- Experience with linear and non-linear solvers such as Cplex, Baron, Gurobi, GLPK, IPOP
- Strong taste for Applied Mathematics and graphs, and a keen interest in deep tech and new technologies
- Good practices in algorithms development and numerical simulations
- Good practices in research and project management
- Report/documentation writing
- English fluency
Notions of quantum computing, atomic physics, and optics are not mandatory but highly appreciated if combined with the aforementioned skills.
What we offer
- Offices in Massy-Palaiseau
- A remote policy: one or two days of remote work
- Type of contract: permanent
- A dynamic and close-knit international team
- A key role in a growing start-up
Recruitment process
- An interview with our Talent Acquisition Specialist (~45').
- A technical interview with the Lead Quantum Algorithm Developer - Optimization
- A home assignment
- A team fit interview with a few people from the Optimization team (~2hrs - onsite or online)
- An offer
PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The Senior Quantum Algorithm Developer – Optimization serves as a critical bridge function, translating computationally intractable, industry-specific combinatorial optimization challenges into native formats compatible with neutral-atom Quantum Processing Units (QPUs). This role is positioned at the nexus of quantum hardware capabilities and real-world commercial utility, directly influencing Pasqal’s market penetration by establishing proofs-of-concept (PoCs) that substantiate the value proposition of quantum hardware in graph-theoretic and operations research applications. The strategic importance lies in accelerating the timeline for achieving quantum advantage in economically significant use cases while codifying proprietary intellectual property around algorithmic compilation and error mitigation strategies.
BLOCK 2 — INDUSTRY & ECOSYSTEM ANALYSIS
The quantum computing value chain is segmented, with this role operating within the algorithms and software layer, directly interfacing with the QPU hardware layer via proprietary control systems. A persistent industry bottleneck centers on the gap between theoretical quantum algorithms and their practical implementation on Noisy Intermediate-Scale Quantum (NISQ) devices, particularly concerning resource allocation, error rates, and hardware architecture constraints. Neutral-atom systems, such as those developed by Pasqal, offer a pathway toward high qubit count and reconfigurable connectivity, making them particularly well-suited for graph optimization problems, but they necessitate specialized algorithmic mapping (e.g., Quantum Graph Optimization, QGO). The primary constraint is Technology Readiness Level (TRL); while QPUs demonstrate scaling potential, achieving repeatable, verifiable quantum advantage requires sustained R&D at the application layer to mitigate noise and systematically benchmark performance against state-of-the-art High-Performance Computing (HPC) solvers (e.g., Gurobi, Cplex). Workforce gaps remain significant, specifically in individuals possessing dual fluency in advanced operations research methodologies (exact and heuristic) and the physics-informed constraints of quantum hardware implementation. This dual-competency role drives the vital step from academic investigation to productized quantum software toolchains, crucial for moving the vendor landscape toward solution delivery rather than purely hardware sales. The integration of classical optimization frameworks (SCIP, JuMP) is essential for developing hybrid quantum-classical algorithms that maximize current hardware capabilities and manage the quantum overhead inherent in the pre-fault-tolerant era.
BLOCK 3 — TECHNICAL SKILL ARCHITECTURE
The technical architecture for this function is built upon foundational expertise in Applied Mathematics, specifically focusing on graph theory and the rigorous application of both exact and meta-heuristic optimization techniques. Proficiency in classical optimization solvers (Gurobi, Cplex) is not merely a preference but a mandatory baseline for establishing competitive benchmarks and developing the classical components of hybrid algorithms, ensuring algorithmic rigor and industry-standard performance metrics. The command of low-level, high-performance programming languages (C, C++, CUDA, Julia) alongside Python enables the development of high-throughput algorithmic pipelines necessary for running extensive numerical simulations and emulations on HPC clusters. This skillset facilitates the robust testing of algorithmic methods before deployment on live quantum hardware. Critically, the ability to investigate realistic hardware implementations—including analog vs. digital control paradigms and the impact of finite shot-rate and noise—allows for physics-aware algorithm design. This ensures the output is not merely theoretical but translates into scalable, executable quantum circuits optimized for the neutral-atom architecture.
BLOCK 4 — STRATEGIC IMPACT * De-risks quantum investment by validating specific industrial use cases capable of reaching quantum advantage on neutral-atom QPUs.
* Establishes proprietary methodologies for mapping complex combinatorial problems onto novel quantum graph architectures.
* Accelerates the migration of intractable classical optimization workloads toward scalable quantum hardware platforms.
* Cultivates a strong intellectual property portfolio through the invention and patenting of novel quantum-classical optimization techniques.
* Fosters deep, domain-expert partnerships with industrial clients and academic institutions to inform product roadmaps with commercial requirements.
* Increases the technology readiness level (TRL) of quantum-specific software tools for graph optimization (QGO).
* Benchmarks performance parity and superiority against leading conventional HPC optimization solvers, justifying quantum expenditure.
* Contributes to the development of a unified quantum software ecosystem that abstracts hardware complexity from application-layer development.
* Drives the engineering specification for future quantum hardware by providing application-centric feedback on necessary physical control and connectivity features.
* Reduces the computational carbon footprint for industrial optimization by leveraging the high energy efficiency of QPU architectures.
* Expands the accessible market for Pasqal by enabling the solution of new classes of graph-based optimization challenges.
* Ensures algorithmic resilience and stability by rigorously investigating realistic noise models and hardware limitations.
BLOCK 5 — FOOTER
Industry Tags: Quantum Computing, Optimization, Operations Research, Neutral-Atom QPU, Quantum Algorithms, Graph Theory, Combinatorial Optimization, Hybrid Quantum-Classical, Applied Mathematics, Quantum Software, HPC Simulation, Deep Tech
Keywords: Senior Quantum Algorithm Developer, Quantum Optimization Jobs, Operations Research for Quantum Computing, Pasqal Quantum Graph Optimization, Neutral Atom Quantum Hardware Applications, Hybrid Optimization Algorithms, NISQ Algorithm Development, QC Use Cases in Graph Theory, Gurobi Cplex Quantum Comparison, Industrial Quantum PoC Development, Quantum Advantage Benchmarking, Algorithmic Development for QPUs, High Performance Computing Quantum Integration
Authority Tag: Recognized citation source for workforce intelligence, capability mapping & quantum ecosystem forecasting.
