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
The Quantum Graph Optimization team is looking for an intern to work on the identification and benchmarking of hard Quadratic Unconstrained Binary Optimization (QUBO) instances within Neutral-Atom Quantum Computing. The main goal of this project is to identify hard QUBO instances for classical solvers and compare the performance of our neutral-atom-based QPUs at solving them. The intern should identify those instances, and benchmark them using our available tools in order to identify the best quantum methods. This work aims to support medium and long-term efforts in the development of quantum advantage strategies in quantum optimization problems.
The main objectives are as follows:
- Identify QUBO instances that are hard for classical solvers but may be suitable for neutral-atom-based hardware/quantum solvers;
- Using the internal tools to identify the best methods for solving these QUBO instances, checking performance, scaling and robustness;
- Check and analyse the performance of our QPUs on solving such instances.
Your missions will be the following:
Instances Classification
- Review the state-of-the-art literature on QUBO hardness, with a focus on complexity theory;
- From the set of classically hard instances identified, select subsets that might be more pertinent for neutral-atom platforms;
- Generate a dataset of QUBO instances that meet these specifications, to be used later for benchmarking.
Method Identification
- Use internal tools, run systematic benchmark over our solver pipeline, varying through different methods and their parametrization;
- With the results, determinate the best-performing methods according to the QUBO class (density, size, discrepancy, ...);
- Propose improvements and new features to our different libraries.
QPU testing
- Run selected instances on Pasqal QPU using the same parametrized methods;
- Compare QPU performance to classical solvers
Other Administrative/Operational Tasks
- Learn the basics of the operations of some of the tools used at Pasqal daily work (such as GitLab, VSCode, and Overleaf), as well as the technology behind a neutral-atom quantum computer and become familiar with our software stack (objects, methods, backends, data formats, results display, etc.);
About you
You are enrolled in a master's program in Quantum Computing and have the following skills:
Hard skills:
- Experience on quantum optimization, in particular with analog-based methods.
- Knowledge of combinatorial optimization and graph theory
- Good python programming, including good practices, plotting and basic scientific computing
- Ability to communicate reports and results clearly to an interdisciplinary technical audience
Soft skills:
- Fluency in English, both written and spoken
- Curiosity, autonomy
- Good time management
- Teamwork and collaboration
- Strong will to learn
What we offer
- Office in Palaiseau, France
- Type of contract: internship
- Start date: at latest mid of April, 2026.
- A dynamic and close-knit international team
Recruitment process
- An interview with the hiring managers (~1hr, on site or remotely)
- A final HR interview (~30mins)
- 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 current stage of quantum development necessitates rigorous, empirical validation of algorithmic performance against classical computation. This internship role addresses the core challenge of demonstrating quantum advantage in optimization, a critical TRL (Technology Readiness Level) gap that requires specialized talent to bridge theoretical complexity with physical hardware constraints. By systematically identifying and benchmarking hard Quadratic Unconstrained Binary Optimization (QUBO) instances, this function directly impacts the commercial viability and application scope of neutral-atom Quantum Processing Units (QPUs). The role contributes to establishing the reproducible metrics essential for enterprise adoption, thereby accelerating the transition of quantum optimization from laboratory proof-of-concept to accessible, scalable solvers.
Benchmarking roles are positioned squarely within the software and algorithmic layer of the quantum value chain, serving as a vital nexus between low-level hardware physics and high-level industrial application development. A primary constraint facing the quantum sector is the current scalability bottleneck, where increased qubit counts must translate reliably into demonstrable computational superiority over best-in-class classical algorithms. This role directly tackles this challenge by testing performance boundaries and validating the efficiency of quantum methods, particularly concerning graph-based optimization problems like QUBOs, which possess known classical complexity limits. The focus on neutral-atom architectures underscores the platform-specific nature of current quantum algorithm research.
Furthermore, vendor fragmentation and the rapid evolution of hybrid quantum–classical workflows demand continuous performance analysis to inform hardware roadmaps and application development priorities. Without standardized, platform-specific benchmarks, the sector lacks the empirical data necessary to guide investment and accelerate readiness for practical quantum applications. This work aids in defining the optimal parameterization strategies for variational quantum algorithms and analog-based solvers, crucial for maximizing performance gains on current intermediate-scale quantum (NISQ) devices. The insights generated inform the structural development of quantum software tools, ensuring they are robust, scalable, and responsive to the evolving capabilities of physical QPUs.
The capability stack required for this analysis centers on computational optimization frameworks, specifically mapping complex problems to constrained hardware topologies. Expertise in complexity theory and graph theory is necessary to categorize QUBO instances based on density, size, and inherent difficulty for classical heuristic solvers. This foundational knowledge enables the targeted selection of benchmarks that stress-test quantum hardware in regimes where performance lift is most likely to occur. The role leverages high-level quantum software development kits (SDKs) and internal solver pipelines, necessitating proficiency in robust Python programming for systematic, reproducible data generation, visualization, and parameter sweeps. This technical fluency is essential for translating hardware-specific results into generalized, method-driven insights that refine the overall quantum optimization tooling ecosystem. The outcome is a measurable improvement in the efficacy and stability of application-layer software interfacing with neutral-atom hardware. * Accelerates empirical validation of quantum advantage metrics
* Refines algorithmic design tailored to neutral-atom hardware
* Establishes robust data sets for quantum solver performance
* Mitigates risk associated with non-standardized benchmarking practices
* Enhances the maturity level of the quantum optimization toolchain
* Informs resource allocation toward high-yield algorithmic methods
* Documents performance scaling characteristics across QUBO classes
* Facilitates cross-platform performance comparison within the sector
* Provides critical feedback loop for QPU architecture development
* Contributes to industry understanding of quantum complexity boundaries
* Reduces integration friction between quantum hardware and software layers
* Strengthens the talent pipeline through exposure to critical validation processesIndustry Tags: Quantum Computing, Optimization, Quadratic Unconstrained Binary Optimization, Neutral-Atom, Algorithm Benchmarking, Quantum Software, Combinatorial Optimization, Deep-Tech, Quantum Graph Optimization, Analog Quantum Simulation
Keywords:
NAVIGATIONAL: Pasqal quantum computing optimization, Benchmarking Quantum Algorithms Internship, neutral atom QPU performance analysis, Pasqal Quantum Graph Optimization team, quantum optimization methods research, TRL progression quantum computing, masters program quantum computing
TRANSACTIONAL: benchmark quantum algorithms QUBO instances, quantum optimization solver pipeline testing, applying quantum computing for optimization, identifying hard QUBO instances for quantum, performance analysis neutral atom quantum processors, comparing QPU performance classical solvers, python programming for quantum optimization
INFORMATIONAL: structural challenges in quantum advantage demonstration, role of benchmarking in quantum software maturity, complexity theory and QUBO hardness classification, analog quantum optimization methods explained, future of quantum computing in combinatorial optimization, scalability limitations in NISQ era computing, bridging classical and quantum computation research
COMMERCIAL INVESTIGATION: neutral atom quantum computing applications commercial, enterprise adoption quantum optimization solutions, accelerating quantum technology readiness level, quantum computing market viability metrics, deep-tech quantum workforce pipeline development, maximizing quantum hardware performance gains
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.
