The Quantum Circuit team is seeking a Mid-Sr Quantum Developer to take on key tasks in Pasqal’s software stack, contributing to the logical computing architecture and implement key subroutines, interfacing internally and with external customers to investigate future quantum applications, and how to run them in digital hardware tailored to neutral atom architectures, in turn informing crucial developments of the latter.
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, independent 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 and adopt the latest developments in quantum error correction and circuit compilation, focusing on the interplay of encoding and compiling strategies with the other layers in the stack (hardware native operations at the bottom, applicative strategies at the top) and deploy them in quantum primitives relevant to the algorithms as above
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.
You should satisfy these fundamental requirements:
Good familiarity with 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 (even better know-how proven by publication or code repository records) about one or more among:
Knowledge in the following topics is also appreciated:
Quantum machine learning topics, particularly at the interface of scientific modelling and machine learning (physics-informed frameworks, neural operators, ...)
What we offer
Contract type: Permanent contract based in Europe
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 emergence of mid-senior quantum development roles signifies a critical transition in the quantum value chain from fundamental physics research to scalable software engineering. As the industry moves toward the early fault-tolerant era, this function serves as a structural bridge between abstract algorithm design and the constraints of specific hardware modalities, such as neutral atom architectures. By optimizing the logical computing architecture and refining circuit compilation strategies, these experts directly influence the deterministic progression of technology readiness levels. The necessity for this role is driven by the increasing complexity of hybrid classical-quantum workflows and the systemic requirement for reproducibility in quantum machine learning. Consequently, this tier of expertise is a primary determinant of an organization’s ability to translate theoretical advantage into industrial-grade subroutines.
The quantum computing ecosystem is currently navigating a pivotal maturation phase characterized by the diversification of hardware modalities and the rising importance of specialized software stacks. While initial sector growth was driven by experimental hardware breakthroughs, the current bottleneck has shifted toward the "application enablement" layer. This layer requires a sophisticated synthesis of quantum information theory and robust software architecture to manage the high overhead of error correction and circuit optimization. Macro-level analysis suggests that the transition from Noisy Intermediate-Scale Quantum (NISQ) devices to early fault-tolerant systems depends heavily on the development of hardware-native compilers and logical qubit mapping.
A significant constraint within this environment is the widening gap between academic algorithm research and industrial software engineering standards. The sector is increasingly prioritizing "quantum-classical orchestration," where quantum processors function as specialized accelerators within a larger High-Performance Computing (HPC) framework. This shift necessitates a workforce capable of navigating multi-disciplinary dependencies, ranging from low-level pulse control to high-level algorithmic abstraction. Furthermore, as organizations seek to demonstrate practical quantum advantage in fields like material science and optimization, the ability to benchmark quantum subroutines against classical baselines has become a strategic imperative.
Public and private investment cycles are also influencing the demand for this role type. National quantum strategies often emphasize the creation of sovereign technology stacks, leading to a fragmented vendor landscape. In this context, mid-senior developers play a vital role in ensuring interoperability and reducing the risks of vendor lock-in. By contributing to open-source libraries and establishing best practices for circuit compilation, these professionals facilitate the broader adoption of quantum technologies across diverse industrial sectors, ensuring that hardware advancements are matched by usable, high-fidelity software tools.
The capability architecture for this role type centers on the vertical integration of the quantum software stack, specifically focusing on the interface between algorithmic primitives and physical hardware execution. Structural enablement is achieved through mastery of circuit compilation and quantum error correction (QEC) protocols, which are essential for managing the noise profiles of contemporary processors. These technical domains are critical for improving the stability and fidelity of logical operations, directly impacting the throughput of complex workflows such as Quantum Machine Learning (QML). Proficiency in hardware-aware programming environments ensures that logical subroutines are optimally mapped to the underlying topology, whether based on superconducting loops or neutral atom arrays.
Beyond specific algorithmic implementation, the role facilitates the coupling of deep-tech research with large-scale software engineering principles. This includes the development of automated testing frameworks for quantum code and the integration of hybrid classical-quantum data pipelines. These interface points are vital for ensuring the reproducibility of results and the scalability of applications as hardware moves toward larger qubit counts. By establishing robust architectural blueprints, this function reduces the friction associated with deploying experimental protocols into production-ready environments, thereby accelerating the commercialization of quantum-enhanced solutions.
Accelerates the translation of theoretical quantum algorithms into hardware-efficient logical subroutines
Mitigates systemic risks by establishing rigorous benchmarking protocols for early fault-tolerant applications
Facilitates the integration of quantum processing units into existing high-performance computing infrastructures
Reduces iteration friction between hardware engineering teams and algorithm research departments
Strengthens the reliability of quantum machine learning models through physics-informed architectural design
Optimizes the lifecycle of quantum circuits via advanced error correction and compilation strategies
Supports the scaling of quantum adoption by identifying high-impact industrial use cases for neutral atom systems
Harmonizes abstract scientific breakthroughs with the practical requirements of enterprise-grade software stacks
Improves the precision of resource estimation for large-scale quantum workflows and error-corrected protocols
Protects intellectual property through the development of novel subroutines and logical computing architectures
Shortens the timeline for achieving practical quantum advantage in complex scientific modelling
Enables the strategic orchestration of hybrid classical-quantum tasks within global research networks
Industry Tags: Quantum Software Engineering, Neutral Atom Architectures, Circuit Compilation, Quantum Error Correction, Hybrid Computing, Quantum Machine Learning, Algorithmic Research, Software Architecture
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