The Quantum Circuit team is seeking a Mid-Sr Quantum Developer to take on key tasks in Pasqal’s algorithm and software stack, interfacing internally and with external customers to investigate future applications in the Quantum Machine Learning realm, and how to run them in digital hardware.
The Quantum Circuit team is a cutting edge, R&D team focused on matching emerging digital HW capabilities from our devices to quantum protocols targeting the solution to some of the most complex problems in industry today. We seek an enthusiastic and knowledgeable colleague to expand our team, initially onboarding and contributing to existing projects, with the perspective of becoming a main point of contact in new ones.
Your key responsibilities will be as follows:
- Analyse the latest developments in machine learning, and deploy them in quantum protocols targeting problems in applied mathematics
- Work with our customers to ensure that our findings target relevant industry problems, and deliver sound perspectives of quantum advantage
- Collaborate with quantum algorithm developers to identify and develop promising algorithms to run in the early fault-tolerant regime, with a special emphasis on quantum machine learning
- Prepare publications and contribute to our intellectual property portfolio.
- Presenting results and innovations in technical discussions internally and in related conferences and outreach events
We look for candidates with a PhD in a technical field (primarily Computer Science, Physics or Mathematics), who have been involved in relevant research projects in their academic career (or via industry internships), are good team players with a proactive, autonomous attitude.
Ideally, you should satisfy these fundamental requirements:
- Familiarity with at least the basics of quantum algorithms & quantum information, and a related programming language (e.g. qadence, qiskit, pennylane, cirq)
- Strong code development skills, preferentially in Python (though equivalent languages will be considered)
- Experience working in multi-disciplinary teams
- Fluency in English
Strong preference will be given to candidates with familiarity (or proven know-how) about one or more among:
- The interface of scientific modelling and machine learning (physics-informed frameworks, neural operators, ...)
- With machine learning packages (e.g. torch, tensorflow, ...)
- Quantum machine learning topics
- Fault-tolerant protocols and resource estimation in this context
- Quantum error-correction protocols
What we offer
- Contract type: Permanent contract based in South Korea
- A dynamic and close-knit international team
- A key role in a fast-growing start-up
- Time allocated for training and attending conferences and meetups
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The structural transition of the quantum ecosystem from theoretical research to industrial utility necessitates a specialized tier of expertise focused on the convergence of quantum circuit design and machine learning. As the sector matures toward the early fault-tolerant regime, the role of a Mid-Senior Quantum Developer serves as a critical conduit for translating complex mathematical subroutines into hardware-executable protocols. This role type addresses the high-dimensional bottleneck in the application software layer, where algorithmic efficiency directly dictates the feasibility of near-term quantum advantage. By synchronizing emerging hardware capabilities with enterprise-relevant use cases, this function ensures the continuity of the technology roadmap during the transition from noisy intermediate-scale devices to scalable architectures. Market signals indicate that the ability to architect these hybrid workflows is becoming a primary determinant for organizations seeking to secure intellectual property in the emerging quantum economy.
The global quantum computing industry is currently navigating a period of strategic reorientation, shifting from broad exploratory research toward the development of high-fidelity software toolchains capable of handling complex computational kernels. Within this environment, the intersection of quantum information science and classical machine learning represents a high-growth domain, particularly for addressing non-linear challenges in applied mathematics and materials science. However, a persistent structural gap remains between abstract algorithm formulation and the practical constraints of specific hardware modalities, such as neutral-atom or superconducting architectures. Addressing this mismatch requires a specialized workforce that can navigate the nuances of resource estimation and error-mitigation while maintaining interoperability across fragmented software stacks.
Macro-level analysis of the quantum workforce reveals that while fundamental research remains robust, there is a systemic shortage of talent capable of managing the stakeholder landscape at the interface of deep-tech R&D and industrial application. Organizations are increasingly adopting a "full-stack" developmental philosophy, necessitating roles that can bridge the divide between low-level gate operations and high-level abstract modelling. This trend is accelerated by the rise of national quantum strategies which prioritize the establishment of sovereign intellectual property and the acceleration of Technology Readiness Levels (TRL) for mission-critical software subroutines.
Furthermore, the integration of quantum machine learning into existing high-performance computing (HPC) environments has become a strategic priority for diverse sectors, including finance and scientific modelling. This evolution favors the development of hybrid quantum-classical neural networks and physics-informed frameworks that can leverage the unique properties of quantum hardware to enhance classical benchmarks. As standardization efforts for quantum intermediate representations continue to evolve, the industry's focus is pivoting toward establishing verifiable performance metrics and robust benchmarking protocols that ensure long-term stability and reduce the risks of technology lock-in.
The capability architecture for this role type centers on the integration of advanced quantum information theory with large-scale software engineering principles and machine learning frameworks. At the foundational layer, mastery of circuit-level optimization and resource estimation is essential for ensuring computational reproducibility on current hardware trajectories. This technical proficiency is coupled with a deep understanding of hybrid workflows, where quantum subtasks are embedded within classical training loops to address specific dimensional bottlenecks. These capabilities are critical for the structural throughput of quantum research, as they directly influence the stability and accuracy of high-fidelity models. Beyond purely technical execution, the role facilitates a high-level coupling between algorithmic breakthroughs and industrial-grade software deployment, ensuring that theoretical gains are translated into tangible value for the broader ecosystem.
Accelerates the deterministic progression of technology readiness levels for enterprise-grade quantum machine learning applications
Mitigates systemic risks associated with hardware-specific algorithmic limitations through robust resource estimation and optimization
Facilitates the transition from isolated theoretical proofs to standardized, executable quantum subroutines for industrial use cases
Reduces iteration friction in the development of hybrid quantum-classical workflows through advanced software engineering integration
Strengthens the long-term intellectual property positioning of the sector by securing novel algorithmic formulations and patents
Harmonizes abstract mathematical research with the practical requirements of complex, scalable software architectures
Optimizes the lifecycle of quantum-classical systems through the development of interoperable middleware and toolchains
Supports the scaling of quantum adoption by identifying high-impact pathways for quantum advantage in applied mathematics
Shortens the time-to-market for quantum-ready products by ensuring algorithmic alignment with evolving hardware roadmaps
Improves the reliability of multi-stakeholder research initiatives through the application of architectural best practices and standardized coding
Protects capital-intensive investments in deep-tech by providing expert technical validation of emerging quantum-classical interfaces
Enables the strategic orchestration of development efforts across global networks of internal researchers and external industrial partners
Industry Tags: Quantum Machine Learning, Algorithmic Research, Software Engineering, Hybrid Computing, Quantum Circuit Design, Fault-Tolerant Quantum Computing, Resource Estimation, Applied Mathematics, Deep Tech R&D
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